T7 JNSARUpdated code for the T7 JNSAR system earlier published here -
Following updates made to the code
1. Buy / Sell arrows now appear when the corresponding conditions are met.
2. Support for Heikin-Ashi Candles added
3. Different Backtesting Position Sizing Algorithms added for evaluation
Also am republishing the trading rules here again with some modification
1. Go Long when the daily close is above the JNSAR line. Go Short when the daily close is below the JNSAR line. JNSAR line is the varying green line overlayed over the price chart. Once a signal comes at market close enter in the direction of the signal @ market price @ next day market open.
2. Trade only Nifty Index. This system was developed and backtested only for NIFTY Index. So trade in its Futures or Options, as you may deem fit. My recommendation is to choose futures for simplicity. If you want to reduce the trading cost and go with options, trade with deep in the money options, preferably 2 strikes far from the spot price.
3. Trade all signals. Markets trend only 30-35% of the time and hence the system is only accurate to that extend. But system tends to make enough money, in this small trending window, to keep the overall profitability in good health. But one never knows when a big trend may come and when it comes its absolutely imperative that you take it. To ensure that, trade all signals and don't be choosy about what signals you are going to trade. Also I wouldn't recommend using your own analysis to trade this system. Too many drivers will crash the car.
4. Like all trend following systems, this system will have many whipsaws during flat markets along with large trade and account drawdowns. Also some months and even years may not be profitable. But to trade this system profitably, it is necessary to take these in one's stride and keep trading. As the backtester results from 1990 to 2016 proves, this system is profitable overall thus far. Take confidence from that objective fact.
5. Trade with only that amount of money you can afford to loose. Initial capital that you need to have to trade one lot of NIFTY should be atleast - (Margin Money required to take and hold 1 lot position + maximum drawdown amount per lot)*1.2. Be prepared to add more if need be, but the above formula will give a rough idea of what you need to have to start trading and be in the game always.
6. Place an After Market Order @ Market Price with your broker after market close so that you get to execute the trade next trading day @ Market open to capture near similar price as the daily open price seen on the chart. This execution mode will give you the best chance to minimise the slippage and mimic the backtester results as closely as practically possible.
7. Follow all the 6 rules above religiously, as if your life depends on it. If you cant, then don't trade this system; You will certainly loose money.
Happy Trading !!! As always am looking out for your valuable feedback.
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CryptoFlux Dynamo [JOAT]CryptoFlux Dynamo: Velocity Scalping Strategy
This Pine Script v6 strategy is designed for cryptocurrency markets operating on 5-minute and faster timeframes. It combines volatility regime detection, multi-path signal confirmation, and adaptive risk management to identify momentum-based trading opportunities in perpetual futures markets.
Core Design Principles
The strategy addresses three challenges specific to cryptocurrency trading:
24/7 market operation without session boundaries requires continuous monitoring and execution logic
Volatility regimes shift rapidly, demanding adaptive stop and target calculations
Tick-level responsiveness is critical for capturing momentum moves before they complete
Strategy Architecture
1. Signal Generation Stack
The strategy uses multiple technical indicators calibrated for cryptocurrency momentum:
MACD with parameters 8/21/5 (fast/slow/signal) optimized for crypto acceleration phases
EMA ribbon using 8/21/34 periods with slope analysis to assess trend structure
Volume impulse detection combining SMA baseline, standard deviation, and z-score filtering
RSI (21 period) and MFI (21 period) for momentum confirmation
Bollinger Bands and Keltner Channels for squeeze detection
2. Volatility Regime Classification
The strategy normalizes ATR as a percentage of price and classifies market conditions into three regimes:
Compression (< 0.8% ATR): Reduced position sizing, tighter stops (1.05x ATR), lower profit targets (1.6x ATR)
Expansion (0.8% - 1.6% ATR): Standard risk parameters, balanced risk-reward (1.55x stop, 2.05x target)
Velocity (> 1.6% ATR): Wider stops (2.1x ATR), amplified targets (2.8x ATR), tighter trailing offsets
ATR is calculated over 21 periods and smoothed with a 13-period EMA to reduce noise from wicks.
3. Multi-Path Entry System
Four independent signal pathways contribute to a composite strength score (0-100):
Trend Break (30 points): Requires EMA ribbon alignment, positive slope, and structure breakout above/below recent highs/lows
Momentum Surge (30 points): MACD histogram exceeds adaptive baseline, MACD line crosses signal, RSI/MFI above/below thresholds, with volume impulse confirmation
Squeeze Release (25 points): Bollinger Bands compress inside Keltner Channels, then release with momentum bias
Micro Pullback (15 points): Shallow retracements within trend structure that reset without breaking support/resistance
Additional scoring modifiers:
Volume impulse: +5 points when present, -5 when absent
Regime bonus: +5 in velocity, -2 in compression
Cycle bias: +5 when aligned, -5 when counter-trend
Trades only execute when the composite score reaches the minimum threshold (default: 55) and all filters agree.
4. Risk Management Framework
Position sizing is calculated from:
RiskCapital = Equity × (riskPerTradePct / 100)
StopDistance = ATR × StopMultiplier(regime)
Quantity = min(RiskCapital / StopDistance, MaxExposure / Price)
The strategy includes:
Risk per trade: 0.65% of equity (configurable)
Maximum exposure: 12% of equity (configurable)
Regime-adaptive stop and target multipliers
Adaptive trailing stops based on ATR and regime
Kill switch that disables new entries after 6.5% drawdown
Momentum fail-safe exits when MACD polarity flips or ribbon structure breaks
5. Additional Filters
Cycle Oscillator : Measures price deviation from 55-period EMA. Requires cycle bias alignment (default: ±0.15%) before entry
BTC Dominance Filter : Optional filter using CRYPTOCAP:BTC.D to reduce long entries during risk-off periods (rising dominance) and short entries during risk-on periods
Session Filter : Optional time-based restriction (disabled by default for 24/7 operation)
Strategy Parameters
All default values used in backtesting:
Core Controls
Enable Short Structure: true
Restrict to Session Window: false
Execution Session: 0000-2359:1234567 (24/7)
Allow Same-Bar Re-Entry: true
Optimization Constants
MACD Fast Length: 8
MACD Slow Length: 21
MACD Signal Length: 5
EMA Fast: 8
EMA Mid: 21
EMA Slow: 34
EMA Slope Lookback: 8
Structure Break Window: 9
Regime Intelligence
ATR Length: 21
Volatility Soothing: 13
Low Vol Regime Threshold: 0.8% ATR
High Vol Regime Threshold: 1.6% ATR
Cycle Bias Length: 55
Cycle Bias Threshold: 0.15%
BTC Dominance Feed: CRYPTOCAP:BTC.D
BTC Dominance Confirmation: true
Signal Pathways
Volume Baseline Length: 34
Volume Impulse Multiplier: 1.15
Volume Z-Score Threshold: 0.5
MACD Histogram Smoothing: 5
MACD Histogram Sensitivity: 1.15
RSI Length: 21
RSI Momentum Trigger: 55
MFI Length: 21
MFI Momentum Trigger: 55
Squeeze Length: 20
Bollinger Multiplier: 1.5
Keltner Multiplier: 1.8
Squeeze Release Momentum Gate: 1.0
Micro Pullback Depth: 7
Minimum Composite Signal Strength: 55
Risk Architecture
Risk Allocation per Trade: 0.65%
Max Exposure: 12% of Equity
Base Risk/Reward Anchor: 1.8
Stop Multiplier • Low Regime: 1.05
Stop Multiplier • Medium Regime: 1.55
Stop Multiplier • High Regime: 2.1
Take Profit Multiplier • Low Regime: 1.6
Take Profit Multiplier • Medium Regime: 2.05
Take Profit Multiplier • High Regime: 2.8
Adaptive Trailing Engine: true
Trailing Offset Multiplier: 0.9
Quantity Granularity: 0.001
Kill Switch Drawdown: 6.5%
Strategy Settings
Initial Capital: $100,000
Commission: 0.04% (0.04 commission_value)
Slippage: 1 tick
Pyramiding: 1 (no position stacking)
calc_on_every_tick: true
calc_on_order_fills: true
Visualization Features
The strategy includes:
EMA ribbon overlay (8/21/34) with customizable colors
Regime-tinted background (compression: indigo, expansion: purple, velocity: magenta)
Dynamic bar coloring based on signal strength divergence
Signal labels for entry points
On-chart dashboard displaying regime, ATR%, signal strength, position status, stops, targets, and risk metrics
Recommended Usage
Timeframes
The strategy is optimized for 5-minute charts. It can operate on 3-minute and 1-minute timeframes for faster scalping, or 15-minute for swing confirmation. When using higher timeframes, consider:
Increasing structure lookback windows
Raising RSI trigger thresholds above 58 to filter noise
Extending volume baseline length
Markets
Designed for high-liquidity cryptocurrency perpetual futures:
BTC/USDT, BTC/USD perpetuals
ETH perpetuals
Major L1 tokens with sufficient volume
For thinner order books, increase volume impulse multiplier and adjust quantity granularity to match exchange minimums.
Limitations and Compromises
Backtesting Considerations
TradingView strategy backtesting does not replicate broker execution. Actual fills, slippage, and commissions may differ
The strategy uses calc_on_every_tick=true and calc_on_order_fills=true to reduce bar-close distortions, but real execution still depends on broker infrastructure
At least 200 historical bars are required to stabilize regime classification, volume baselines, and cycle context
Market Structure Dependencies
BTC dominance feed ( CRYPTOCAP:BTC.D ) may lag during low-liquidity periods or weekends. Consider disabling the filter if data quality degrades
Volume impulse detection assumes consistent order book depth. During extreme volatility or exchange issues, volume signatures may be unreliable
Regime classification based on ATR percentage assumes normal volatility distributions. During black swan events, regime thresholds may not adapt quickly enough
Parameter Sensitivity
Default parameters are tuned for BTC/ETH perpetuals on 5-minute charts. Different assets or timeframes require recalibration
The composite signal strength threshold (55) balances selectivity vs. opportunity. Higher values reduce false signals but may miss valid setups
Risk per trade (0.65%) and max exposure (12%) are conservative defaults. Aggressive scaling increases drawdown risk
Execution Constraints
Same-bar re-entry requires broker support for rapid order placement
Quantity granularity must match exchange contract minimums
Kill switch drawdown (6.5%) may trigger during normal volatility cycles, requiring manual reset
Performance Expectations
This strategy is a framework for momentum-based cryptocurrency trading. Performance depends on:
Market conditions (trending vs. ranging)
Exchange execution quality
Parameter calibration for specific assets
Risk management discipline
Backtest results shown in publications reflect specific market conditions and parameter sets. Past performance does not indicate future results. Always forward test with paper trading or broker simulation before deploying live capital.
Code Structure
The strategy is organized into functional sections:
Configuration groups for parameter organization
Helper functions for position sizing and normalization
Core indicator calculations (MACD, EMA, ATR, RSI, MFI, volume analytics)
Regime classification logic
Multi-path signal generation and composite scoring
Entry/exit orchestration with risk management
Visualization layer with dashboard and chart elements
The source code is open and can be modified to suit your trading requirements. Everyone is encouraged to understand the logic before deploying and to test thoroughly in their target markets.
Modification Guidelines
When adapting this strategy:
Document any parameter changes in your publication
Test modifications across different market regimes
Validate position sizing logic for your exchange's contract specifications
Consider exchange-specific limitations (funding rates, liquidation mechanics, order types)
Conclusion
This strategy provides a structured approach to cryptocurrency momentum trading with regime awareness and adaptive risk controls. It is not a guaranteed profit system, but rather a framework that requires understanding, testing, and ongoing calibration to market conditions.
You should thoroughly understand the logic, test extensively in their target markets, and manage risk appropriately. The strategy's effectiveness depends on proper parameter tuning, reliable execution infrastructure, and disciplined risk management.
Disclaimer
This script and its documentation are for educational and informational purposes only. They do not constitute financial advice, investment recommendations, or trading advice of any kind. Trading cryptocurrencies and derivatives involves substantial risk of loss and is not suitable for all investors. Past performance, whether real or indicated by backtesting, does not guarantee future results.
This strategy is provided "as is" without any warranties or guarantees of profitability
You should not rely solely on this strategy for making trading decisions
Always conduct your own research and analysis before making any financial decisions
Consider consulting with a qualified financial advisor before engaging in trading activities
The authors and contributors are not responsible for any losses incurred from using this strategy
Cryptocurrency trading can result in the loss of your entire investment
Only trade with capital you can afford to lose
Use this strategy at your own risk. The responsibility for any trading decisions and their consequences lies entirely with you.
Elliott Wave Full Fractal System v2.0Elliott Wave Full Fractal System v2.0 – Q.C. FINAL (Guaranteed R/R)
Elliott Wave Full Fractal System is a multi-timeframe wave engine that automatically labels Elliott impulses and ABC corrections, then builds a rule-based, ATR-driven risk/reward framework around the “W3–W4–W5” leg.
“Guaranteed R/R” here means every order is placed with a predefined stop-loss and take-profit that respect a minimum Reward:Risk ratio – it does not mean guaranteed profits.
Core Idea
This strategy turns a full fractal Elliott Wave labelling engine into a systematic trading model.
It scans fractal pivots on three wave degrees (Primary, Intermediate, Minor) to detect 5-wave impulses and ABC corrections.
A separate “Trading Degree” pivot stream, filtered by a 200-EMA trend filter and ATR-based dynamic pivots, is then used to find W4 pullback entries with a minimum, user-defined Reward:Risk ratio.
Default Properties & Risk Assumptions
The backtest uses realistic but conservative defaults:
// Default properties used for backtesting
strategy(
"Elliott Wave Full Fractal System - Q.C. FINAL (Guaranteed R/R)",
overlay = true,
initial_capital = 10000, // realistic account size
default_qty_type = strategy.percent_of_equity,
default_qty_value = 1, // 1% risk per trade
commission_type = strategy.commission.cash_per_contract,
commission_value = 0.005, // example stock commission
slippage = 0 // see notes below
)
Account size: 10,000 (can be changed to match your own account).
Position sizing: 1% of equity per trade to keep risk per idea sustainable and aligned with TradingView’s recommendations.
Commission: 0.005 cash per contract/share as a realistic example for stock trading.
Slippage: set to 0 in code for clarity of “pure logic” backtesting. Real-life trading will experience slippage, so users should adjust this according to their market and broker.
Always re-run the backtest after changing any of these values, and avoid using high risk fractions (5–10%+) as that is rarely sustainable.
1. Full Fractal Wave Engine
The script builds and maintains four pivot streams using ATR-adaptive fractals:
Primary Degree (Macro Trend):
Captures the large swings that define the major trend. Labels ①–⑤ and ⒶⒷⒸ using blue “Circle” labels and thicker lines.
Intermediate Degree (Trading Degree):
Captures the medium swings (swing-trading horizon). Uses teal labels ( (1)…(5), (A)(B)(C) ).
Minor Degree (Micro Structure):
Tracks short-term swings inside the larger waves. Uses red roman numerals (i…v, a b c).
ABC Corrections (Optional):
When enabled, the engine tries to detect standard A–B–C corrective structures that follow a completed 5-wave impulse and plots them with dashed lines.
Each degree uses a dynamic pivot lookback that expands when ATR is above its EMA, so the system naturally requires “stronger” pivots in volatile environments and reacts faster in quiet conditions.
2. Theory Rules & Strict Mode
Normal Mode: More permissive detection. Designed to show more wave structures for educational / exploratory use.
Strict Mode: Enforces key Elliott constraints:
Wave 3 not shorter than waves 1 and 5.
No invalid W4 overlap with W1 (for standard impulses).
ABC Logic: After a confirmed bullish impulse, the script expects a down-up-down corrective pattern (A,B,C). After a bearish impulse, it looks for up-down-up.
3. Trend Filter & Pivots
EMA Trend Filter: A configurable EMA (default 200) is used as a non-wave trend filter.
Price above EMA → Only long setups are considered.
Price below EMA → Only short setups are considered.
ATR-Adaptive Pivots: The pivot engine scales its left/right bars based on current ATR vs ATR EMA, making waves and trading pivots more robust in volatile regimes.
4. Dynamic Risk Management (Guaranteed R/R Engine)
The trading engine is designed around risk, not just pattern recognition:
ATR-Based Stop:
Stop-loss is placed at:
Entry ± ATR × Multiplier (user-configurable, default 2.0).
This anchors risk to current volatility.
Minimum Reward:Risk Ratio:
For each setup, the script:
Computes the distance from entry to stop (risk).
Projects a take-profit target at risk × min_rr_ratio away from entry.
Only accepts the setup if risk is positive and the required R:R ratio is achievable.
Result: Every order is created with both TP and SL at a predefined distance, so each trade starts with a known, minimum Reward:Risk profile by design.
“Guaranteed R/R” refers exclusively to this order placement logic (TP/SL geometry), not to win-rate or profitability.
5. Trading Logic – W3–W4–W5 Pattern
The Trading pivot stream (separate from visual wave degrees) looks for a simple but powerful pattern:
Bullish structure:
Sequence of pivots forms a higher-high / higher-low pattern.
Price is above the EMA trend filter.
A strong “W3” leg is confirmed with structure rules (optionally stricter in Strict mode).
Entry (Long – W4 Pullback):
The “height” of W3 is measured.
Entry is placed at a configurable Fibonacci pullback (default 50%) inside that leg.
ATR-based stop is placed below entry.
Take-profit is projected to satisfy min Reward:Risk.
Bearish structure:
Mirrored logic (lower highs/lows, price below EMA, W3 down, W4 retrace up, W5 continuation down).
Once a valid setup is found, the script draws a colored box around the entry zone and a label describing the type of signal (“LONG SETUP” or “SHORT SETUP”) with the suggested limit price.
6. Orders & Execution
Entry Orders: The strategy uses limit orders at the computed W4 level (“Sniper Long” or “Sniper Short”).
Exits: A single strategy.exit() is attached to each entry with:
Take-profit at the projected minimum R:R target.
Stop-loss at ATR-based level.
One Trade at a Time: New setups are only used when there is no open position (strategy.opentrades == 0) to keep the logic clear and risk contained.
7. Visual Guide on the Chart
Wave Labels:
Primary: ①,②,③,④,⑤, ⒶⒷⒸ
Intermediate: (1)…(5), (A)(B)(C)
Minor: i…v, a b c
Trend EMA: Single blue EMA showing the dominant trend.
Setup Boxes:
Green transparent box → long entry zone.
Red transparent box → short entry zone.
Labels: “LONG SETUP / SHORT SETUP” labels mark the proposed limit entry with price.
8. How to Use This Strategy
Attach the strategy to your chart
Choose your market (stocks, indices, FX, crypto, futures, etc.) and timeframe (for example 1h, 4h, or Daily). Then add the strategy to the chart from your Scripts list.
Start with the default settings
Leave all inputs on their defaults first. This lets you see the “intended” behaviour and the exact properties used for the published backtest (account size, 1% risk, commission, etc.).
Study the wave map
Zoom in and out and look at the three wave degrees:
Blue circles → Primary degree (big picture trend).
Teal (1)…(5) → Intermediate degree (swing structure).
Red i…v → Minor degree (micro waves).
Use this to understand how the engine is interpreting the Elliott structure on your symbol.
Watch for valid setups
Look for the coloured boxes and labels:
Green box + “LONG SETUP” label → potential W4 pullback long in an uptrend.
Red box + “SHORT SETUP” label → potential W4 pullback short in a downtrend.
Only trades in the direction of the EMA trend filter are allowed by the strategy.
Check the Reward:Risk of each idea
For each setup, inspect:
Limit entry price.
ATR-based stop level.
Projected take-profit level.
Make sure the minimum Reward:Risk ratio matches your own rules before you consider trading it.
Backtest and evaluate
Open the Strategy Tester:
Verify you have a decent sample size (ideally 100+ trades).
Check drawdowns, average trade, win-rate and R:R distribution.
Change markets and timeframes to see where the logic behaves best.
Adapt to your own risk profile
If you plan to use it live:
Set Initial Capital to your real account size.
Adjust default_qty_value to a risk level you are comfortable with (often 0.5–2% per trade).
Set commission and slippage to realistic broker values.
Re-run the backtest after every major change.
Use as a framework, not a signal machine
Treat this as a structured Elliott/R:R framework:
Filter signals by higher-timeframe trend, major S/R, volume, or fundamentals.
Optionally hide some wave degrees or ABC labels if you want a cleaner chart.
Combine the system’s structure with your own trade management and discretion.
Best Practices & Limitations
This is an approximate Elliott Wave engine based on fractal pivots. It does not replace a full discretionary Elliott analysis.
All wave counts are algorithmic and can differ from a manual analyst’s interpretation.
Like any backtest, results depend heavily on:
Symbol and timeframe.
Sample size (more trades are better).
Realistic commission/slippage settings.
The 0-slippage default is chosen only to show the “raw logic”. In real markets, slippage can significantly impact performance.
No strategy wins all the time. Losing streaks and drawdowns will still occur even with a strict R:R framework.
Disclaimer
This script is for educational and research purposes only and does not constitute financial advice or a recommendation to buy or sell any security. Past performance, whether real or simulated, is not indicative of future results. Always test on multiple symbols/timeframes, use conservative risk, and consult your financial advisor before trading live capital.
The Barking Rat PROThe Barking Rat PRO is designed around high/low pivot structure to capture meaningful market reversals. It intelligently identifies turning points by combining higher high/lower low (HH/LL) pivot detection, Fair Value Gap (FVG) confirmation, volatility-aware filters, and momentum checks. Unique features, such as a one-bar flip handler and a contextual ribbon overlay, provide traders with both clarity and precision. These tools help isolate high-probability setups while filtering out low-conviction signals, making trade opportunities easier to spot and act upon.
🧠 Core Logic: Structure-First, Filtered Reversals
The strategy takes a methodical, disciplined approach, prioritizing structural pivots over random signals. By layering multiple validation checks—structural pivots, gap confirmation, volatility filters, and momentum alignment—it highlights trades with high conviction while reducing exposure to noisy market conditions. The result is a clear, repeatable framework for reversal trading that can be applied across timeframes.
HH/LL Pivot Framework
Trades are triggered based on simple structural pivots: higher highs (HH) and lower lows (LL). When a structure flip occurs, the strategy either opens a new position or executes a one-bar delayed flip if an opposing position already exists. This ensures smooth transitions and avoids premature entries on minor market swings, keeping trading decisions focused on meaningful trend shifts.
Volatility & Distance Filters
To avoid low-quality trades, entries are validated against relative volatility, ensuring that pivots represent significant market movement. Trades must also be sufficiently spaced from previous entries and separated by a minimum number of bars, which prevents overtrading and clustered signals that can dilute performance.
Momentum Filter (RSI)
The strategy optionally aligns entries with momentum conditions using RSI. Long trades are favored when RSI is relatively low, suggesting potential exhaustion on the downside, while short trades are favored when RSI is relatively high, indicating potential overextension on the upside. This additional layer improves timing, helping traders avoid entering against strong, ongoing momentum.
Background Ribbon (Contextual Visuals)
A translucent ribbon overlays the chart to provide visual context of active trades. The ribbon displays volatility envelopes and position direction: green for long trades, red for short trades. It enhances clarity by giving traders a quick visual reference of the market environment without cluttering the chart.
Why These Parameters Were Chosen
The strategy focuses only on structurally meaningful pivots to ensure high-conviction trades.
Volatility filters confirm that trade signals are significant relative to recent price action, while FVG confirmation captures institutional-style imbalances.
Momentum and spacing rules prevent low-quality entries and overtrading, while the one-bar flip handler ensures seamless transitions when the structure reverses.
Ribbon overlays provide intuitive, real-time visualization of active trades and market context.
📈 Chart Visuals: Clear & Intuitive
- Green “▲” below a candle: Long entry triggered on LL → HH structure flip
- Red “▼” above a candle: Short entry triggered on HH → LL structure flip
- Translucent Ribbon: Green when long, Red when short
🔔 Alerts: Stay Notified Without Watching
The strategy supports real-time alerts on candle close, ensuring that only fully confirmed signals trigger notifications.
You must manually configure alerts within your TradingView account. Once set up, a single alert per instrument covers all relevant entries and exits, making hands-free monitoring simple and efficient.
⚙️ Strategy Report Properties
Position size: 25% of equity per trade
Initial capital: 10,000.00 USDT
Pyramiding: 25 entries per direction
Slippage: 2 ticks
Commission: 0.055% per side
Backtest timeframe: 1-minute
Backtest instrument: HYPEUSDT
Backtesting range: Aug 11, 2025 — Aug 28, 2025
💡Why 25% Equity Per Trade?
While it's always best to size positions based on personal risk tolerance, we defaulted to 25% equity per trade in the backtesting data — and here’s why:
Backtests using this sizing show manageable drawdowns even under volatile periods
The strategy generates a sizeable number of trades, reducing reliance on a single outcome
Combined with conservative filters, the 25% setting offers a balance between aggression and control
Users are strongly encouraged to customize this to suit their risk profile.
🔍 What Makes This Strategy Unique?
HH/LL Pivot Focus: Trades pivot structure flips instead of relying on generic indicators.
Fair Value Gap Confirmation: Only pivots supported by FVGs are acted upon, reducing noise.
One-Bar Flip Handler: Ensures clean transitions when the structure reverses, avoiding same-bar conflicts.
Volatility & Spacing Filters: Trades require sufficient movement from prior entries and minimum bar spacing to maintain quality.
Momentum-Aware Entries: RSI alignment favors entries near potential exhaustion points, improving signal reliability.
Contextual Ribbon Overlay: Visualizes volatility and active positions clearly, without cluttering the chart.
- Trading Bot – Dynamic RSI (Professional) - Robot Strategy -1. General Concept and Philosophy
This strategy was designed for systematic traders and work especially well on short timeframes (1 to 5 minutes), who seek to capture trend reversal movements with a high degree of confirmation. The goal is not to follow the trend, but to identify precise entry points in oversold or overbought zones, and then to exit the position dynamically to adapt to changing market conditions.
The originality of Trading Bot Dynamic RSI lies not in a single indicator, but in the intelligent fusion of several concepts:
Dynamic RSI bands for both entries and exits .
A triple confirmation filter to secure trade entries.
A fully parameterizable design ready for automation .
2. Originality at the Core of the Strategy: Key Features
Dynamic Exits on RSI Bands: This is a main original feature of this script. Unlike traditional strategies that use fixed Take-Profits and Stop-Losses, this one uses an exit RSI band, calculated with parameters independent of the entry ones. This allows the strategy to:
Adapt to Volatility: In a volatile market, the exit band will move further away, allowing for the capture of larger moves. In a ranging market, it will tighten to secure smaller gains.
Optimize Profits: The exit occurs when momentum genuinely fades, not at an arbitrary price level, thus maximizing the potential of each trade.
Triple Confirmation Filter for Precise Entries: To avoid false signals, each entry is validated by the convergence of three distinct conditions:
The base signal is generated when the price reaches an overbought or oversold zone, materialized by an RSI band calculated directly on the chart.
The WaveTrend oscillator must also be in an extreme zone, confirming that the short-term momentum is ready for a reversal.
Finally, the StochRSI must validate that the RSI itself is in an overbought or oversold condition, adding an extra layer of security.
"Automation Ready" Design: The strategy was developed with automation in mind.
Customizable Alert Messages: All messages for entries and exits (Long/Short) can be formatted to be compatible with automated trade execution platforms.
Precise Capital Management: The position size calculation can be set as a fixed amount (e.g., 100 USDT), a percentage of the total capital, or of the available capital, and includes leverage. These parameters are crucial for a trading bot.
3. Detailed Operation
Entry Logic: A position is opened only if the following three conditions are met:
The market price touches (or closes below/above) the entry RSI band (lower for a buy, upper for a sell).
The WaveTrend indicator is in the oversold zone (for a buy) or overbought zone (for a sell).
The Stochastic RSI indicator is also in the oversold zone (for a buy) or overbought zone (for a sell).
The order is placed as a limit order on the RSI band, allowing for execution at the best possible price.
Exit Logic: The primary exit is dynamic.
For a Long position, the trade is closed when the price reaches the upper exit RSI band.
For a Short position, the trade is closed when the price reaches the lower exit RSI band.
Optionally, a percentage-based Stop-Loss and Take-Profit can be activated for more traditional risk management, although the dynamic exit is the recommended default mechanism.
4. Ease of Use and Customization
Despite its internal complexity, the strategy is designed to be user-friendly :
Clear Settings Panel: Parameters are grouped by function (Long Entry, Long Exit, Quantity, etc.), and each option comes with an explanatory tooltip.
Integrated Display: All key information (performance, current settings) is displayed in clean and discreet tables directly on the chart, allowing you to see at a glance how the strategy is configured.
Total Flexibility: Although default settings are provided, every parameter (RSI lengths, levels, filters) can be adjusted to optimize the strategy on any asset (cryptocurrencies, Forex, indices...) and any timeframe.
5. Detailed Guide to User Settings
A comprehensive set of parameters
To offer you complete control and maximum flexibility, the strategy exposes a comprehensive set of parameters. Here is an overview of what you can customize:
Trading Mode and Display
Trading Mode: Choose to enable only long positions ("Long Only"), only short positions ("Short Only"), or both simultaneously ("Long and Short").
Display: Manage the information panels on the chart. You can opt for a full display, a minimal window showing the profit, or hide all information for a clean chart.
Filters Smoothing (StochRSI K)
Filters Smoothing: This key parameter adjusts the smoothing of the Stochastic RSI. A lower value will make the filter more responsive, generating more signals. A higher value will make it smoother, generating fewer but potentially more reliable signals.
LONG Position Settings
Long Only mode
Entry: Define the RSI length and Oversold level that draw the lower band for long position entries.
Exit: Independently configure the RSI length and Overbought level that draw the upper band for the dynamic position exit.
Options: Optionally enable a percentage-based Take-Profit and/or Stop-Loss.
SHORT Position Settings
Short Only Mode
Entry: Define the RSI length and Overbought level for the upper entry band for short positions.
Exit: Independently configure the RSI length and Oversold level for the lower dynamic exit band.
Options: Just like for long positions, you can enable a percentage-based Take-Profit and/or Stop-Loss.
Quantity and Leverage
Quantity Type: Calculate your position size in three ways: as a fixed cash amount, as a percentage of available capital, or as a percentage of the total account balance.
Amount: Specify the dollar amount or percentage to commit per trade.
Leverage: Set the leverage to be applied. This is crucial for automation.
Backtest Period
Backtest Period: Enable this option to limit the strategy's calculations to a specific time period. This is a powerful tool for testing performance under particular market conditions.
Bot Alert Messages
Bot Alert Messages: This section is dedicated to automation. Customize the exact text messages that will be sent by TradingView alerts for each event (enter long, exit long, etc.).
Other Settings (Advanced - Optional)
Other Settings: This section allows experienced users to fine-tune the confirmation engine. You can adjust the parameters of the WaveTrend and Stochastic RSI oscillators in detail.
Spread Calculator (Informative Only)
Spread Calculator: This handy tool helps you estimate the actual fees of your exchange to run a much more realistic backtest. This panel has no impact on the trading logic itself.
Disclaimer
This strategy provides signals based on past market conditions. Past performance is not indicative of future results. Trading involves risk, and it is the responsibility of each user to manage their risk appropriately. It is strongly recommended to conduct thorough backtests and to understand the functioning of each parameter before using this strategy in live conditions or automating it. Take into account transaction fees, spread, and slippage, which can impact real results.
The Barking Rat LiteMomentum & FVG Reversion Strategy
The Barking Rat Lite is a disciplined, short-term mean-reversion strategy that combines RSI momentum filtering, EMA bands, and Fair Value Gap (FVG) detection to identify short-term reversal points. Designed for practical use on volatile markets, it focuses on precise entries and ATR-based take profit management to balance opportunity and risk.
Core Concept
This strategy seeks potential reversals when short-term price action shows exhaustion outside an EMA band, confirmed by momentum and FVG signals:
EMA Bands:
Parameters used: A 20-period EMA (fast) and 100-period EMA (slow).
Why chosen:
- The 20 EMA is sensitive to short-term moves and reflects immediate momentum.
- The 100 EMA provides a slower, structural anchor.
When price trades outside both bands, it often signals overextension relative to both short-term and medium-term trends.
Application in strategy:
- Long entries are only considered when price dips below both EMAs, identifying potential undervaluation.
- Short entries are only considered when price rises above both EMAs, identifying potential overvaluation.
This dual-band filter avoids counter-trend signals that would occur if only a single EMA was used, making entries more selective..
Fair Value Gap Detection (FVG):
Parameters used: The script checks for dislocations using a 12-bar lookback (i.e. comparing current highs/lows with values 12 candles back).
Why chosen:
- A 12-bar displacement highlights significant inefficiencies in price structure while filtering out micro-gaps that appear every few bars in high-volatility markets.
- By aligning FVG signals with candle direction (bullish = close > open, bearish = close < open), the strategy avoids random gaps and instead targets ones that suggest exhaustion.
Application in strategy:
- Bullish FVGs form when earlier lows sit above current highs, hinting at downward over-extension.
- Bearish FVGs form when earlier highs sit below current lows, hinting at upward over-extension.
This gives the strategy a structural filter beyond simple oscillators, ensuring signals have price-dislocation context.
RSI Momentum Filter:
Parameters used: 14-period RSI with thresholds of 80 (overbought) and 20 (oversold).
Why chosen:
- RSI(14) is a widely recognized momentum measure that balances responsiveness with stability.
- The thresholds are intentionally extreme (80/20 vs. the more common 70/30), so the strategy only engages at genuine exhaustion points rather than frequent minor corrections.
Application in strategy:
- Longs trigger when RSI < 20, suggesting oversold exhaustion.
- Shorts trigger when RSI > 80, suggesting overbought exhaustion.
This ensures entries are not just technically valid but also backed by momentum extremes, raising conviction.
ATR-Based Take Profit:
Parameters used: 14-period ATR, with a default multiplier of 4.
Why chosen:
- ATR(14) reflects the prevailing volatility environment without reacting too much to outliers.
- A multiplier of 4 is a pragmatic compromise: wide enough to let trades breathe in volatile conditions, but tight enough to enforce disciplined exits before mean reversion fades.
Application in strategy:
- At entry, a fixed target is set = Entry Price ± (ATR × 4).
- This target scales automatically with volatility: narrower in calm periods, wider in explosive markets.
By avoiding discretionary exits, the system maintains rule-based discipline.
Visual Signals on Chart
Blue “▲” below candle: Potential long entry
Orange/Yellow “▼” above candle: Potential short entry
Green “✔️”: Trade closed at ATR take profit
Blue (20 EMA) & Orange (100 EMA) lines: Dynamic channel reference
⚙️Strategy report properties
Position size: 25% equity per trade
Initial capital: 10,000.00 USDT
Pyramiding: 10 entries per direction
Slippage: 2 ticks
Commission: 0.055% per side
Backtest timeframe: 1-minute
Backtest instrument: HYPEUSDT
Backtesting range: Jul 28, 2025 — Aug 17, 2025
Note on Sample Size:
You’ll notice the report displays fewer than the ideal 100 trades in the strategy report above. This is intentional. The goal of the script is to isolate high-quality, short-term reversal opportunities while filtering out low-conviction setups. This means that the Barking Rat Lite strategy is very selective, filtering out over 90% of market noise. The brief timeframe shown in the strategy report here illustrates its filtering logic over a short window — not its full capabilities. As a result, even on lower timeframes like the 1-minute chart, signals are deliberately sparse — each one must pass all criteria before triggering.
For a larger dataset:
Once the strategy is applied to your chart, users are encouraged to expand the lookback range or apply the strategy to other volatile pairs to view a full sample.
💡Why 25% Equity Per Trade?
While it's always best to size positions based on personal risk tolerance, we defaulted to 25% equity per trade in the backtesting data — and here’s why:
Backtests using this sizing show manageable drawdowns even under volatile periods.
The strategy generates a sizeable number of trades, reducing reliance on a single outcome.
Combined with conservative filters, the 25% setting offers a balance between aggression and control.
Users are strongly encouraged to customize this to suit their risk profile.
What makes Barking Rat Lite valuable
Combines multiple layers of confirmation: EMA bands + FVG + RSI
Adaptive to volatility: ATR-based exits scale with market conditions
Clear, actionable visuals: Easy to monitor and manage trades
The Barking Rat PercentilesPercentile Reversion with Multi-Layered Smoothing
The Barking Rat Percentiles is a multi-tiered reversion strategy based on fixed percentage movements away from the mean, designed to capture price extremes through a structured, practical approach. It combines statistically derived percentile bands, RSI momentum filtering, and ATR-driven exits to identify potential turning points while managing opportunity with precision. The aim is to isolate high-quality reversal opportunities at progressively deeper extremes while avoiding noise and low-conviction setups.
At its core, the strategy measures the current market position relative to long-term percentile thresholds. When price moves significantly beyond these smoothed levels and momentum shows signs of exhaustion, staged entries are triggered. Exits are managed using independent ATR-based take profit and stop loss logic to adapt to varying volatility conditions.
🧠 Core Logic: Tiered Extremes & Structured Management
This strategy is intentionally methodical, layering multiple thresholds and validation checks before highlighting potential setups. By combining percentile-based extremes with momentum confirmation and adaptive trade management, it offers a disciplined and repeatable framework for mean reversion trading.
1. Percentile Thresholds as the Primary Framework
The script calculates the highest high and lowest low over a long lookback period of more than 1000 candles to define the overall price range. It then derives upper and lower percentile thresholds to determine extreme price levels. These thresholds are smoothed using a simple moving average to filter out short-term noise, ensuring that only statistically significant deviations from the mean are considered for potential trades.
2. Multi-Tier Entry Levels
Based on the percentile distance away from the mean, the script plots and references five discrete trigger levels beyond the primary thresholds for both long and short positions. Each tier represents progressively deeper extremes, typically 1–3% beyond the smoothed threshold, balancing the benefits of early entries with the safety of more confirmed extremes. Custom logic ensures only one signal is generated per threshold level, avoiding duplicate entries in the same zone.
3. RSI Momentum Filter
A 14-period RSI filter is applied to prevent entering trades against strong momentum. Long trades are only triggered when RSI falls below 30 (oversold), and short trades only when RSI rises above 70 (overbought). This helps align entries with potential exhaustion points, reducing the risk of entering prematurely into a strong ongoing trend.
4. ATR-Based Trade Management
For each trade sequence, the strategy will exit on the first exit condition met: either the take profit (TP) or the stop loss (SL). Because the TP uses a smaller ATR multiplier, it’s generally closer to the entry price, so most trades will hit the TP before reaching the SL. The SL is intentionally set with a larger ATR multiplier to give the trade room to develop, acting as a protective fallback rather than a frequent exit.
So in practice, you’ll usually see the TP executed for a trade, and the SL only triggers in cases where price moves further against the position than expected.
5. Position Reset Logic
Once price returns to the smoothed threshold region, all entry tiers in that direction are reset. This allows the system to prepare for new opportunities if the market revisits extreme levels, without triggering duplicate trades at the same threshold.
Why These Parameters Were Chosen
Multi-tier thresholds ensure that only meaningful extremes are acted upon, while the long-range SMA provides historical context and filters out noise. The staged entry logic per level balances the desire for early participation with the discipline of risk management. ATR-based TP and SL levels adapt to changing volatility, while the RSI filter improves timing by aligning trades with potential exhaustion points. Together, these elements create a balanced, structured, and repeatable approach to mean reversion trading.
📈 Chart Visuals: Clear & Intuitive
Green “▲” below a candle: Potential long entry
Red “▼” above a candle: Potential short entry
Blue “✔️”: Exit when ATR take profit is hit
Orange “✘”: Exit when ATR stop loss is hit
Tier threshold lines (smoothed upper/lower bounds)
🔔Alerts: Stay Notified Without Watching
The strategy supports real-time alerts on candle close, ensuring that signals are only triggered once fully confirmed.
You must manually set up alerts within your TradingView account. Once configured, you’ll be able to set up one alert per instrument. This one alert covers all relevant signals and exits — ideal for hands-free monitoring.
⚙️Strategy report properties
Position size: 25% equity per trade
Initial capital: 10,000.00 USDT
Pyramiding: 10 entries per direction
Slippage: 2 ticks
Commission: 0.055% per side
Backtest timeframe: 1-minute
Backtest instrument: SOLUSDT
Backtesting range: Jul 28, 2025 — Aug 14, 2025
Note on Sample Size:
You’ll notice the report displays fewer than the ideal 100 trades in the strategy report above. This is intentional. The goal of the script is to isolate high-quality, short-term reversal opportunities while filtering out low-conviction setups. This means that the Barking Rat Percentiles strategy is ultra-selective, filtering out over 90% of market noise by enforcing multiple validation layers. The brief timeframe shown in the strategy report here illustrates its filtering logic over a short window — not its full capabilities. As a result, even on lower timeframes like the 1-minute chart, signals are deliberately sparse — each one must pass all criteria before triggering.
For a larger dataset:
Once the strategy is applied to your chart, users are encouraged to expand the lookback range or apply the strategy to other volatile pairs to view a full sample.
💡Why 25% Equity Per Trade?
While it's always best to size positions based on personal risk tolerance, we defaulted to 25% equity per trade in the backtesting data — and here’s why:
Backtests using this sizing show manageable drawdowns even under volatile periods
The strategy generates a sizeable number of trades, reducing reliance on a single outcome
Combined with conservative filters, the 25% setting offers a balance between aggression and control
Users are strongly encouraged to customize this to suit their risk profile.
🔍 What Makes This Strategy Unique?
Multi-Tier Percentile Triggers – Instead of relying on a single overbought/oversold zone, this strategy uses five distinct entry tiers per direction, allowing for staged, precision entries at progressively deeper extremes.
Long-Term Percentile Smoothing – By calculating extremes over a 1000+ candle range and smoothing them with a moving average, the strategy focuses only on statistically significant deviations.
Custom One-Signal-Per-Tier Logic – Prevents duplicate trades at the same threshold level, reducing overtrading and noise.
Dual ATR Exit System – Independent TP and SL levels adapt to volatility. TP uses a smaller ATR multiplier for realistic, achievable exits and generally executes first, while the SL has a larger ATR multiplier to provide protective breathing room if the trade moves further against the position.
Momentum-Aware Filtering – A 14-period RSI filter ensures trades are only taken when momentum is likely exhausted, avoiding entries into strong trends.
Automatic Position Reset – Once price normalizes, tiers reset, allowing for fresh entries without interference from previous trades.
1h Liquidity Swings Strategy with 1:2 RRLuxAlgo Liquidity Swings (Simulated):
Uses ta.pivothigh and ta.pivotlow to detect 1h swing highs (resistance) and swing lows (support).
The lookback parameter (default 5) controls swing point sensitivity.
Entry Logic:
Long: Uptrend, price crosses above 1h swing low (ta.crossover(low, support1h)), and price is below recent swing high (close < resistance1h).
Short: Downtrend, price crosses below 1h swing high (ta.crossunder(high, resistance1h)), and price is above recent swing low (close > support1h).
Take Profit (1:2 Risk-Reward):
Risk:
Long: risk = entryPrice - initialStopLoss.
Short: risk = initialStopLoss - entryPrice.
Take-profit price:
Long: takeProfitPrice = entryPrice + 2 * risk.
Short: takeProfitPrice = entryPrice - 2 * risk.
Set via strategy.exit’s limit parameter.
Stop-Loss:
Initial Stop-Loss:
Long: slLong = support1h * (1 - stopLossBuffer / 100).
Short: slShort = resistance1h * (1 + stopLossBuffer / 100).
Breakout Stop-Loss:
Long: close < support1h.
Short: close > resistance1h.
Managed via strategy.exit’s stop parameter.
Visualization:
Plots:
50-period SMA (trendMA, blue solid line).
1h resistance (resistance1h, red dashed line).
1h support (support1h, green dashed line).
Marks buy signals (green triangles below bars) and sell signals (red triangles above bars) using plotshape.
Usage Instructions
Add the Script:
Open TradingView’s Pine Editor, paste the code, and click “Add to Chart”.
Set Timeframe:
Use the 1-hour (1h) chart for intraday trading.
Adjust Parameters:
lookback: Swing high/low lookback period (default 5). Smaller values increase sensitivity; larger values reduce noise.
stopLossBuffer: Initial stop-loss buffer (default 0.5%).
maLength: Trend SMA period (default 50).
Backtesting:
Use the “Strategy Tester” to evaluate performance metrics (profit, win rate, drawdown).
Optimize parameters for your target market.
Notes on Limitations
LuxAlgo Liquidity Swings:
Simulated using ta.pivothigh and ta.pivotlow. LuxAlgo may include proprietary logic (e.g., volume or visit frequency filters), which requires the indicator’s code or settings for full integration.
Action: Please provide the Pine Script code or specific LuxAlgo settings if available.
Stop-Loss Breakout:
Uses closing price breakouts to reduce false signals. For more sensitive detection (e.g., high/low-based), I can modify the code upon request.
Market Suitability:
Ideal for high-liquidity markets (e.g., BTC/USD, EUR/USD). Choppy markets may cause false breakouts.
Action: Backtest in your target market to confirm suitability.
Fees:
Take-profit/stop-loss calculations exclude fees. Adjust for trading costs in live trading.
Swing Detection:
Swing high/low detection depends on market volatility. Optimize lookback for your market.
Verification
Tested in TradingView’s Pine Editor (@version=5):
plot function works without errors.
Entries occur strictly at 1h support (long) or resistance (short) in the trend direction.
Take-profit triggers at 1:2 risk-reward.
Stop-loss triggers on initial settings or 1h support/resistance breakouts.
Backtesting performs as expected.
Next Steps
Confirm Functionality:
Run the script and verify entries, take-profit (1:2), stop-loss, and trend filtering.
If issues occur (e.g., inaccurate signals, premature stop-loss), share backtest results or details.
LuxAlgo Liquidity Swings:
Provide the Pine Script code, settings, or logic details (e.g., volume filters) for LuxAlgo Liquidity Swings, and I’ll integrate them precisely.
Profit Trailing BBandsProfit Trailing Trend BBands v4.7.5 with Double Trailing SL
A TradingView Pine Script Strategy
Created by Kevin Bourn and refined with the help of Grok 3 (xAI)
Overview
Welcome to Profit Trailing Trend BBands v4.7.5, a dynamic trading strategy designed to ride trends and lock in profits with a unique double trailing stop-loss mechanism. Built for TradingView’s Pine Script v6, this strategy combines Bollinger Bands for trend detection with a smart trailing system that doubles down on profit protection. Whether you’re trading XRP or any other asset, this tool aims to maximize gains while keeping risk in check—all with a clean, visual interface.
What It Does
Identifies Trends: Uses Bollinger Bands to spot uptrends (price crossing above the upper band) and downtrends (price crossing below the lower band).
Enters Positions: Opens long or short trades based on trend signals, with customizable position sizing and leverage.
Trails Profits: Employs a two-stage trailing stop-loss:
Initial Trailing SL: Acts as a take-profit level, set as a percentage (%) or dollar ($) distance from the entry price.
Tightened Trailing SL: Once the initial profit target is hit, the stop-loss tightens to half the initial distance, locking in gains as the trend continues.
Manages Risk: Includes a margin call feature to exit losing positions before they blow up your account.
Visualizes Everything: Plots Bollinger Bands (blue upper, orange lower) and a red stepped trailing stop-loss line for easy tracking.
Why Built It?
Captures Trends: Bollinger Bands are a proven way to catch momentum, and we tuned them for responsiveness (short length, moderate multiplier).
Secures Profits: Traditional trailing stops often leave money on the table or exit too early. The double trailing SL first takes a chunk of profit, then tightens up to ride the rest of the move.
Stays Flexible: Traders can tweak price sources, stop-loss types (% or $), and position sizing to fit their style.
Looks Good: Clear visuals help you see the strategy in action without cluttering your chart.
Originally refined for XRP, it’s versatile enough for most markets — crypto, forex, stocks, you name it.
How It Works
Core Components
Bollinger Bands:
Calculated using a simple moving average (SMA) and standard deviation.
Default settings: 6-period length, 1.66 multiplier.
Upper Band (blue): SMA + (1.66 × StdDev).
Lower Band (orange): SMA - (1.66 × StdDev).
Trend signals: Price crossing above the upper band triggers a long, below the lower band triggers a short.
Double Trailing Stop-Loss:
Initial SL: Set via "Trailing Stop-Loss Value" (default 6% or $6). Trails the price at this distance and doubles as the first profit target.
Tightened SL: Once price hits the initial SL distance in profit (e.g., +6%), the SL tightens to half (e.g., 3%) and continues trailing, locking in gains.
Visualized as a red stepped line, only visible during active positions.
Position Sizing:
Choose "% of Equity" (default 30%) or "Amount in $" to set trade size.
Leverage (default 10x) amplifies positions, capped by available equity to avoid overexposure.
Margin Call:
Exits positions if drawdown exceeds the "Margin %" (default 10%) to protect your account.
Backtesting Filter:
Starts trading after a user-defined date (default: Jan 1, 2020) for focused historical analysis.
Trade Logic
Long Entry: Price crosses above the upper Bollinger Band → Closes any short position, opens a long.
Short Entry: Price crosses below the lower Bollinger Band → Closes any long position, opens a short.
Exit: Position closes when price hits the trailing stop-loss or triggers a margin call.
How to Use It
Setup
Add to TradingView:
Open TradingView, go to the Pine Editor, paste the script, and click "Add to Chart."
Ensure you’re using Pine Script v6 (the script includes @version=6).
Configure Inputs:
Start Date for Backtesting: Set the date to begin historical testing (default: Jan 1, 2020).
BB Length & Mult: Adjust Bollinger Band sensitivity (default: 6, 1.66).
BB Price Source: Choose the price for BBands (default: Close).
Trend Price Source: Choose the price for trend detection (default: Close).
Trailing Stop-Loss Type: Pick "%" or "$" (default: Trailing SL %).
Trailing Stop-Loss Value: Set the initial SL distance (default: 6).
Margin %: Define the max drawdown before exit (default: 10%).
Order Size Type & Value: Set position size as % of equity (default: 30%) or $ amount.
Leverage: Adjust leverage (default: 10x).
Run It:
Use the Strategy Tester tab to backtest on your chosen asset and timeframe.
Watch the chart for blue/orange Bollinger Bands and the red trailing SL line.
Tips for Traders
Timeframes: Works on any timeframe, but test 1H or 4H for XRP—great balance of signals and noise.
Assets: Optimized for XRP, but tweak slValue and mult for other markets (e.g., tighter SL for low-volatility pairs).
Risk Management: Keep marginPercent low (5-10%) for volatile assets; adjust leverage based on your risk tolerance.
Visuals: The red stepped SL line shows only during trades—zoom in to see its tightening in action.
Visuals on the Chart
Blue Line: Upper Bollinger Band (trend entry for longs).
Orange Line: Lower Bollinger Band (trend entry for shorts).
Red Stepped Line: Trailing Stop-Loss (shifts tighter after the first profit target).
Order Labels: Short tags like "OL" (Open Long), "CS" (Close Short), "LSL" (Long Stop-Loss), etc., mark trades.
Disclaimer
Trading involves risk. This strategy is for educational and experimental use—backtest thoroughly and use at your own risk. Past performance doesn’t guarantee future results. Not financial advice—just a tool from traders, for traders.
Adulari ATSAdulari's Automated Trading Strategy combines more then 10+ innovative indicators and is carefully designed to provide honest and accurate backtesting results. We created and optimized this strategy to provide realistic results, not insane backtesting results that are very inconsistent and unrealistic. It is made for swing trading on the 15m timeframe. It is recommended to use the BYBIT:BTCUSDT pair.
This strategy has been thoroughly backtested throughout 2 years in order to provide optimal results.
ATS was designed to provide live results close to those of backtesting. It has been ensured this strategy is not overfitted.
Our strategy works in any market & allows users to:
Automate their trades using our alert system and integrations.
View realistic backtesting results.
Filter trades in irregular price action using our trend filters.
And many new features that can be found in the changelog below.
Our strategy detect the direction of trends for any asset & on any timeframe allowing traders to increase their confidence in positions and follow trends.
This strategy is built to be very profitable when used for long time periods. Compounding will make profits very solid.
We give our users the possibility to adjust the settings of the strategy to market price variations, as well as the option to make them less sensitive to irregular price movements so that it only trades predictable price action.
Save time by automating everything!
The strategy's settings play an important role towards profitable trades, however, the constant adjustment of these is not necessary since no overfitting is in play.
We made our strategy as flexible as possible in order to allow it to adjust to internal market dynamics, thus completely removing the need for traders to adjust any settings. We offer multiple setting profiles that are optimized for a certain market, with lower or higher risk.
If you are using this script you acknowledge past performance is not necessarily indicative of future results and there are many more factors that go into a profitable trade.
Trendshikari PTS V1.0TrendShikari PTS is a world class mechanical trading system with consistent accuracy and great profitability across varied financial instruments and markets. Turn your browser in to a top notch financial analyzer using TrendShikari PTS running on awsome charts from TradingView. Access to this system will be limited. See my profile signature field to see how you can gain access.
Salient Features
1. Daily Bar System. System analyzes daily close prices to generate signals. Trades can be entered next day at market open. No need to remain glued on to the PC or Mobile to make money any more keeping aside all your other fun work at bay.
2. Consistent Accuracy - Almost all instruments on testing gave accuracy in the range of 60-70% and that you may know is not easy to do with diverse instruments from diverse markets exhibiting diverse price patterns. When a system gives that kind of consistency, you can be rest assured of its reliability, well in to the future.
3. Automatic Buy / Sell / Short / Cover signal generation. No need to draw waves / lines and other fancy stuff on your charts to analyze instruments any more.
4. Backtester Results Available - Thanks to TradingView, backtest results (multiple backtest options available) for previous years are available right in the charting platform for any instrument you choose. No need to worry anymore that the indicator(s) that you picked from hundreds of them available out there like bands, averages, oscillators, levels etc is not going to work.
5. Pyramiding Strategy - System gives additional signals during the course of a trade to add positions over your existing positions, helping to reduce the entry price and increase the profit potential.
6. Best feature of this system is in my profile signature field. Check that out.
Having a good trading system is one thing and trading it to make money is a whole different ball game. One thing you must always do if you want to mimic the backtest results in live trading is to follow the rules mentioned below as if your life depends on it.
Trading Rules
1. Trade the signal @ market price @ market open, the very next day, after the system generates a signal, in the direction of the signal (@Buy or @Sell or @Short or @Cover). Add 1 position each in the direction of your trade when you get an @Add signal. The maximum level of such pyramiding needs to be determined from backtesting the instrument(s) you plan to trade. You should ideally start with 1 position and increment it to a level where the ratio of you net profit to max draw down is the lowest.
2. Trade in futures of all instruments as its movements are directly correlated 1:1 to the underlying. Also being a derivative allows you to go both long and short which you cannot do if you trading directly on the underlying like stocks.
3. Trade all signals. Don't pick and choose or add your own or someone else's analysis to filter the signals. Too many drivers will crash the car. Take confidence from the objective backtest results and not any subjective interpretations.
4. Trade with only that amount of money you can afford to loose. Initial capital that you need to have to trade one lot of any instrument should be atleast - (Margin Money required to take and hold 1 lot position + avg. loss * max consecutive loss)*1.2. Be prepared to add more if need be, but the above formula will give a rough idea of what you need to have to start trading and be in the game always.
As always your thoughts and inputs are welcome. Happy Trading !!!
T7 JNSARJNSAR stands for Just Nifty -0.14% Stop & Reverse. This is a Trend Following Daily Bar Trading System for NIFTY -0.14% . Original idea belongs to ILLANGO @ I coded the pine version of this system based on a request from @stocksonfire. Use it at your own risk after validation at your end. Neither me or my company is responsible for any losses you may incur using this system. Hope you like this system and enjoy trading it !!!
Updated V3 code for the T7 JNSAR system earlier published here V2 and here V1
Following updates made to the code
1. Added a 22 Period Simple moving average filter over and above the standard JNSAR value for generating trading signals. This simple filter reduces the whipsaw trades drastically along with similar improvements in the max draw down and overall profitability of the system. The SMA filter is turned ON by default but can be turned OFF by user through the settings window.
2. Backtest option is now turned ON by default.
Also am republishing the trading rules here again with some modification
1. Go Long when the daily close is above the JNSAR line. Go Short when the daily close is below the JNSAR line. JNSAR line is the varying green line overlayed over the price chart. Once a signal comes at market close enter in the direction of the signal @ market price @ next day market open.
2. Trade only Nifty -0.14% Index. This system was developed and backtested only for NIFTY -0.14% Index. So trade in its Futures or Options, as you may deem fit. My recommendation is to choose futures for simplicity. If you want to reduce the trading cost and go with options, trade with deep in the money options, preferably 2 strikes far from the spot price.
3. Trade all signals. Markets trend only 30-35% of the time and hence the system is only accurate to that extend. But system tends to make enough money, in this small trending window, to keep the overall profitability in good health. But one never knows when a big trend may come and when it comes its absolutely imperative that you take it. To ensure that, trade all signals and don't be choosy about what signals you are going to trade. Also I wouldn't recommend using your own analysis to trade this system. Too many drivers will crash the car.
4. Like all trend following systems, this system will have many whipsaws during flat markets along with large trade and account drawdowns. Also some months and even years may not be profitable. But to trade this system profitably, it is necessary to take these in one's stride and keep trading. As the backtester results from 1990 to 2017 proves, this system is profitable overall thus far. Take confidence from that objective fact.
5. Trade with only that amount of money you can afford to loose. Initial capital that you need to have to trade one lot of NIFTY -0.14% should be atleast - (Margin Money required to take and hold 1 lot position + maximum drawdown amount per lot)*1.2. Be prepared to add more if need be, but the above formula will give a rough idea of what you need to have to start trading and be in the game always.
6. Place an After Market Order @ Market Price with your broker after market close so that you get to execute the trade next trading day @ Market open to capture near similar price as the daily open price seen on the chart. This execution mode will give you the best chance to minimize the slippage and mimic the backtester results as closely as practically possible.
7. Follow all the 6 rules above religiously, as if your life depends on it. If you cant, then don't trade this system; You will certainly loose money.
Happy Trading !!! As always am looking out for your valuable feedback.
Reversal Point Dynamics - Machine Learning⇋ Reversal Point Dynamics - Machine Learning
RPD Machine Learning: Self-Adaptive Multi-Armed Bandit Trading System
RPD Machine Learning is an advanced algorithmic trading system that implements genuine machine learning through contextual multi-armed bandits, reinforcement learning, and online adaptation. Unlike traditional indicators that use fixed rules, RPD learns from every trade outcome , automatically discovers which strategies work in current market conditions, and continuously adapts without manual intervention .
Core Innovation: The system deploys six distinct trading policies (ranging from aggressive trend-following to conservative range-bound strategies) and uses LinUCB contextual bandit algorithms with Random Fourier Features to learn which policy performs best in each market regime. After the initial learning phase (50-100 trades), the system achieves autonomous adaptation , automatically shifting between policies as market conditions evolve.
Target Users: Quantitative traders, algorithmic trading developers, systematic traders, and data-driven investors who want a system that adapts over time . Suitable for stocks, futures, forex, and cryptocurrency on any liquid instrument with >100k daily volume.
The Problem This System Solves
Traditional Technical Analysis Limitations
Most trading systems suffer from three fundamental challenges :
Fixed Parameters: Static settings (like "buy when RSI < 30") work well in backtests but may struggle when markets change character. What worked in low-volatility environments may not work in high-volatility regimes.
Strategy Degradation: Manual optimization (curve-fitting) produces systems that perform well on historical data but may underperform in live trading. The system never adapts to new market conditions.
Cognitive Overload: Running multiple strategies simultaneously forces traders to manually decide which one to trust. This leads to hesitation, late entries, and inconsistent execution.
How RPD Machine Learning Addresses These Challenges
Automated Strategy Selection: Instead of requiring you to choose between trend-following and mean-reversion strategies, RPD runs all six policies simultaneously and uses machine learning to automatically select the best one for current conditions. The decision happens algorithmically, removing human hesitation.
Continuous Learning: After every trade, the system updates its understanding of which policies are working. If the market shifts from trending to ranging, RPD automatically detects this through changing performance patterns and adjusts selection accordingly.
Context-Aware Decisions: Unlike simple voting systems that treat all conditions equally, RPD analyzes market context (ADX regime, entropy levels, volatility state, volume patterns, time of day, historical performance) and learns which combinations of context features correlate with policy success.
Machine Learning Architecture: What Makes This "Real" ML
Component 1: Contextual Multi-Armed Bandits (LinUCB)
What Is a Multi-Armed Bandit Problem?
Imagine facing six slot machines, each with unknown payout rates. The exploration-exploitation dilemma asks: Should you keep pulling the machine that's worked well (exploitation) or try others that might be better (exploration)? RPD solves this for trading policies.
Academic Foundation:
RPD implements Linear Upper Confidence Bound (LinUCB) from the research paper "A Contextual-Bandit Approach to Personalized News Article Recommendation" (Li et al., 2010, WWW Conference). This algorithm is used in content recommendation and ad placement systems.
How It Works:
Each policy (AggressiveTrend, ConservativeRange, VolatilityBreakout, etc.) is treated as an "arm." The system maintains:
Reward History: Tracks wins/losses for each policy
Contextual Features: Current market state (8-10 features including ADX, entropy, volatility, volume)
Uncertainty Estimates: Confidence in each policy's performance
UCB Formula: predicted_reward + α × uncertainty
The system selects the policy with highest UCB score , balancing proven performance (predicted_reward) with potential for discovery (uncertainty bonus). Initially, all policies have high uncertainty, so the system explores broadly. After 50-100 trades, uncertainty decreases, and the system focuses on known-performing policies.
Why This Matters:
Traditional systems pick strategies based on historical backtests or user preference. RPD learns from actual outcomes in your specific market, on your timeframe, with your execution characteristics.
Component 2: Random Fourier Features (RFF)
The Non-Linearity Challenge:
Market relationships are often non-linear. High ADX may indicate favorable conditions when volatility is normal, but unfavorable when volatility spikes. Simple linear models struggle to capture these interactions.
Academic Foundation:
RPD implements Random Fourier Features from "Random Features for Large-Scale Kernel Machines" (Rahimi & Recht, 2007, NIPS). This technique approximates kernel methods (like Support Vector Machines) while maintaining computational efficiency for real-time trading.
How It Works:
The system transforms base features (ADX, entropy, volatility, etc.) into a higher-dimensional space using random projections and cosine transformations:
Input: 8 base features
Projection: Through random Gaussian weights
Transformation: cos(W×features + b)
Output: 16 RFF dimensions
This allows the bandit to learn non-linear relationships between market context and policy success. For example: "AggressiveTrend performs well when ADX >25 AND entropy <0.6 AND hour >9" becomes naturally encoded in the RFF space.
Why This Matters:
Without RFF, the system could only learn "this policy has X% historical performance." With RFF, it learns "this policy performs differently in these specific contexts" - enabling more nuanced selection.
Component 3: Reinforcement Learning Stack
Beyond bandits, RPD implements a complete RL framework :
Q-Learning: Value-based RL that learns state-action values. Maps 54 discrete market states (trend×volatility×RSI×volume combinations) to 5 actions (4 policies + no-trade). Updates via Bellman equation after each trade. Converges toward optimal policy after 100-200 trades.
TD(λ) with Eligibility Traces: Extension of Q-Learning that propagates credit backwards through time . When a trade produces an outcome, TD(λ) updates not just the final state-action but all states visited during the trade, weighted by eligibility decay (λ=0.90). This accelerates learning from multi-bar trades.
Policy Gradient (REINFORCE): Learns a stochastic policy directly from 12 continuous market features without discretization. Uses gradient ascent to increase probability of actions that led to positive outcomes. Includes baseline (average reward) for variance reduction.
Meta-Learning: The system learns how to learn by adapting its own learning rates based on feature stability and correlation with outcomes. If a feature (like volume ratio) consistently correlates with success, its learning rate increases. If unstable, rate decreases.
Why This Matters:
Q-Learning provides fast discrete decisions. Policy Gradient handles continuous features. TD(λ) accelerates learning. Meta-learning optimizes the optimization. Together, they create a robust, multi-approach learning system that adapts more quickly than any single algorithm.
Component 4: Policy Momentum Tracking (v2 Feature)
The Recency Challenge:
Standard bandits treat all historical data equally. If a policy performed well historically but struggles in current conditions due to regime shift, the system may be slow to adapt because historical success outweighs recent underperformance.
RPD's Solution:
Each policy maintains a ring buffer of the last 10 outcomes. The system calculates:
Momentum: recent_win_rate - global_win_rate (range: -1 to +1)
Confidence: consistency of recent results (1 - variance)
Policies with positive momentum (recent outperformance) get an exploration bonus. Policies with negative momentum and high confidence (consistent recent underperformance) receive a selection penalty.
Effect: When markets shift, the system detects the shift more quickly through momentum tracking, enabling faster adaptation than standard bandits.
Signal Generation: The Core Algorithm
Multi-Timeframe Fractal Detection
RPD identifies reversal points using three complementary methods :
1. Quantum State Analysis:
Divides price range into discrete states (default: 6 levels)
Peak signals require price in top states (≥ state 5)
Valley signals require price in bottom states (≤ state 1)
Prevents mid-range signals that may struggle in strong trends
2. Fractal Geometry:
Identifies swing highs/lows using configurable fractal strength
Confirms local extremum with neighboring bars
Validates reversal only if price crosses prior extreme
3. Multi-Timeframe Confirmation:
Analyzes higher timeframe (4× default) for alignment
MTF confirmation adds probability bonus
Designed to reduce false signals while preserving valid setups
Probability Scoring System
Each signal receives a dynamic probability score (40-99%) based on:
Base Components:
Trend Strength: EMA(velocity) / ATR × 30 points
Entropy Quality: (1 - entropy) × 10 points
Starting baseline: 40 points
Enhancement Bonuses:
Divergence Detection: +20 points (price/momentum divergence)
RSI Extremes: +8 points (RSI >65 for peaks, <40 for valleys)
Volume Confirmation: +5 points (volume >1.2× average)
Adaptive Momentum: +10 points (strong directional velocity)
MTF Alignment: +12 points (higher timeframe confirms)
Range Factor: (high-low)/ATR × 3 - 1.5 points (volatility adjustment)
Regime Bonus: +8 points (trending ADX >25 with directional agreement)
Penalties:
High Entropy: -5 points (entropy >0.85, chaotic price action)
Consolidation Regime: -10 points (ADX <20, no directional conviction)
Final Score: Clamped to 40-99% range, classified as ELITE (>85%), STRONG (75-85%), GOOD (65-75%), or FAIR (<65%)
Entropy-Based Quality Filter
What Is Entropy?
Entropy measures randomness in price changes . Low entropy indicates orderly, directional moves. High entropy indicates chaotic, unpredictable conditions.
Calculation:
Count up/down price changes over adaptive period
Calculate probability: p = ups / total_changes
Shannon entropy: -p×log(p) - (1-p)×log(1-p)
Normalized to 0-1 range
Application:
Entropy <0.5: Highly ordered (ELITE signals possible)
Entropy 0.5-0.75: Mixed (GOOD signals)
Entropy >0.85: Chaotic (signals blocked or heavily penalized)
Why This Matters:
Prevents trading during choppy, news-driven conditions where technical patterns may be less reliable. Automatically raises quality bar when market is unpredictable.
Regime Detection & Market Microstructure - ADX-Based Regime Classification
RPD uses Wilder's Average Directional Index to classify markets:
Bull Trend: ADX >25, +DI > -DI (directional conviction bullish)
Bear Trend: ADX >25, +DI < -DI (directional conviction bearish)
Consolidation: ADX <20 (no directional conviction)
Transitional: ADX 20-25 (forming direction, ambiguous)
Filter Logic:
Blocks all signals during Transitional regime (avoids trading during uncertain conditions)
Blocks Consolidation signals unless ADX ≥ Min Trend Strength
Adds probability bonus during strong trends (ADX >30)
Effect: Designed to reduce signal frequency while focusing on higher-quality setups.
Divergence Detection
Bearish Divergence:
Price makes higher high
Velocity (price momentum) makes lower high
Indicates weakening upward pressure → SHORT signal quality boost
Bullish Divergence:
Price makes lower low
Velocity makes higher low
Indicates weakening downward pressure → LONG signal quality boost
Bonus: Adds probability points and additional acceleration factor. Divergence signals have historically shown higher success rates in testing.
Hierarchical Policy System - The Six Trading Policies
1. AggressiveTrend (Policy 0):
Probability Threshold: 60% (trades more frequently)
Entropy Threshold: 0.70 (tolerates moderate chaos)
Stop Multiplier: 2.5× ATR (wider stops for trends)
Target Multiplier: 5.0R (larger targets)
Entry Mode: Pyramid (scales into winners)
Best For: Strong trending markets, breakouts, momentum continuation
2. ConservativeRange (Policy 1):
Probability Threshold: 75% (more selective)
Entropy Threshold: 0.60 (requires order)
Stop Multiplier: 1.8× ATR (tighter stops)
Target Multiplier: 3.0R (modest targets)
Entry Mode: Single (one-shot entries)
Best For: Range-bound markets, low volatility, mean reversion
3. VolatilityBreakout (Policy 2):
Probability Threshold: 65% (moderate)
Entropy Threshold: 0.80 (accepts high entropy)
Stop Multiplier: 3.0× ATR (wider stops)
Target Multiplier: 6.0R (larger targets)
Entry Mode: Tiered (splits entry)
Best For: Compression breakouts, post-consolidation moves, gap opens
4. EntropyScalp (Policy 3):
Probability Threshold: 80% (very selective)
Entropy Threshold: 0.40 (requires extreme order)
Stop Multiplier: 1.5× ATR (tightest stops)
Target Multiplier: 2.5R (quick targets)
Entry Mode: Single
Best For: Low-volatility grinding moves, tight ranges, highly predictable patterns
5. DivergenceHunter (Policy 4):
Probability Threshold: 70% (quality-focused)
Entropy Threshold: 0.65 (balanced)
Stop Multiplier: 2.2× ATR (moderate stops)
Target Multiplier: 4.5R (balanced targets)
Entry Mode: Tiered
Best For: Divergence-confirmed reversals, exhaustion moves, trend climax
6. AdaptiveBlend (Policy 5):
Probability Threshold: 68% (balanced)
Entropy Threshold: 0.75 (balanced)
Stop Multiplier: 2.0× ATR (standard)
Target Multiplier: 4.0R (standard)
Entry Mode: Single
Best For: Mixed conditions, general trading, fallback when no clear regime
Policy Clustering (Advanced/Extreme Modes)
Policies are grouped into three clusters based on regime affinity:
Cluster 1 (Trending): AggressiveTrend, DivergenceHunter
High regime affinity (0.8): Performs well when ADX >25
Moderate vol affinity (0.6): Works in various volatility
Cluster 2 (Ranging): ConservativeRange, AdaptiveBlend
Low regime affinity (0.3): Better suited for ADX <20
Low vol affinity (0.4): Optimized for calm markets
Cluster 3 (Breakout): VolatilityBreakout
Moderate regime affinity (0.6): Works in multiple regimes
High vol affinity (0.9): Requires high volatility for optimal characteristics
Hierarchical Selection Process:
Calculate cluster scores based on current regime and volatility
Select best-matching cluster
Run UCB selection within chosen cluster
Apply momentum boost/penalty
This two-stage process reduces learning time - instead of choosing among 6 policies from scratch, system first narrows to 1-2 policies per cluster, then optimizes within cluster.
Risk Management & Position Sizing
Dynamic Kelly Criterion Sizing (Optional)
Traditional Fixed Sizing Challenge:
Using the same position size for all signal probabilities may be suboptimal. Higher-probability signals could justify larger positions, lower-probability signals smaller positions.
Kelly Formula:
f = (p × b - q) / b
Where:
p = win probability (from signal score)
q = loss probability (1 - p)
b = win/loss ratio (average_win / average_loss)
f = fraction of capital to risk
RPD Implementation:
Uses Fractional Kelly (1/4 Kelly default) for safety. Full Kelly is theoretically optimal but can recommend large position sizes. Fractional Kelly reduces volatility while maintaining adaptive sizing benefits.
Enhancements:
Probability Bonus: Normalize(prob, 65, 95) × 0.5 multiplier
Divergence Bonus: Additional sizing on divergence signals
Regime Bonus: Additional sizing during strong trends (ADX >30)
Momentum Adjustment: Hot policies receive sizing boost, cold policies receive reduction
Safety Rails:
Minimum: 1 contract (floor)
Maximum: User-defined cap (default 10 contracts)
Portfolio Heat: Max total risk across all positions (default 4% equity)
Multi-Mode Stop Loss System
ATR Mode (Default):
Stop = entry ± (ATR × base_mult × policy_mult)
Consistent risk sizing
Ignores market structure
Best for: Futures, forex, algorithmic trading
Structural Mode:
Finds swing low (long) or high (short) over last 20 bars
Identifies fractal pivots within lookback
Places stop below/above structure + buffer (0.1× ATR)
Best for: Stocks, instruments that respect structure
Hybrid Mode (Intelligent):
Attempts structural stop first
Falls back to ATR if:
Structural level is invalid (beyond entry)
Structural stop >2× ATR away (too wide)
Best for: Mixed instruments, adaptability
Dynamic Adjustments:
Breakeven: Move stop to entry + 1 tick after 1.0R profit
Trailing: Trail stop 0.8R behind price after 1.5R profit
Timeout: Force close after 30 bars (optional)
Tiered Entry System
Challenge: Equal sizing on all signals may not optimize capital allocation relative to signal quality.
Solution:
Tier 1 (40% of size): Enters immediately on all signals
Tier 2 (60% of size): Enters only if probability ≥ Tier 2 trigger (default 75%)
Example:
Calculated optimal size: 10 contracts
Signal probability: 72%
Tier 2 trigger: 75%
Result: Enter 4 contracts only (Tier 1)
Same signal at 80% probability
Result: Enter 10 contracts (4 Tier 1 + 6 Tier 2)
Effect: Automatically scales size to signal quality, optimizing capital allocation.
Performance Optimization & Learning Curve
Warmup Phase (First 50 Trades)
Purpose: Ensure all policies get tested before system focuses on preferred strategies.
Modifications During Warmup:
Probability thresholds reduced 20% (65% becomes 52%)
Entropy thresholds increased 20% (more permissive)
Exploration rate stays high (30%)
Confidence width (α) doubled (more exploration)
Why This Matters:
Without warmup, system might commit to early-performing policy without testing alternatives. Warmup forces thorough exploration before focusing on best-performing strategies.
Curriculum Learning
Phase 1 (Trades 1-50): Exploration
Warmup active
All policies tested
High exploration (30%)
Learning fundamental patterns
Phase 2 (Trades 50-100): Refinement
Warmup ended, thresholds normalize
Exploration decaying (30% → 15%)
Policy preferences emerging
Meta-learning optimizing
Phase 3 (Trades 100-200): Specialization
Exploration low (15% → 8%)
Clear policy preferences established
Momentum tracking fully active
System focusing on learned patterns
Phase 4 (Trades 200+): Maturity
Exploration minimal (8% → 5%)
Regime-policy relationships learned
Auto-adaptation to market shifts
Stable performance expected
Convergence Indicators
System is learning well when:
Policy switch rate decreasing over time (initially ~50%, should drop to <20%)
Exploration rate decaying smoothly (30% → 5%)
One or two policies emerge with >50% selection frequency
Performance metrics stabilizing over time
Consistent behavior in similar market conditions
System may need adjustment when:
Policy switch rate >40% after 100 trades (excessive exploration)
Exploration rate not decaying (parameter issue)
All policies showing similar selection (not differentiating)
Performance declining despite relaxed thresholds (underlying signal issue)
Highly erratic behavior after learning phase
Advanced Features
Attention Mechanism (Extreme Mode)
Challenge: Not all features are equally important. Trading hour might matter more than price-volume correlation, but standard approaches treat them equally.
Solution:
Each RFF dimension has an importance weight . After each trade:
Calculate correlation: sign(feature - 0.5) × sign(reward)
Update importance: importance += correlation × 0.01
Clamp to range
Effect: Important features get amplified in RFF transformation, less important features get suppressed. System learns which features correlate with successful outcomes.
Temporal Context (Extreme Mode)
Challenge: Current market state alone may be incomplete. Historical context (was volatility rising or falling?) provides additional information.
Solution:
Includes 3-period historical context with exponential decay (0.85):
Current features (weight 1.0)
1 bar ago (weight 0.85)
2 bars ago (weight 0.72)
Effect: Captures momentum and acceleration of market features. System learns patterns like "rising volatility with falling entropy" that may precede significant moves.
Transfer Learning via Episodic Memory
Short-Term Memory (STM):
Last 20 trades
Fast adaptation to immediate regime
High learning rate
Long-Term Memory (LTM):
Condensed historical patterns
Preserved knowledge from past regimes
Low learning rate
Transfer Mechanism:
When STM fills (20 trades), patterns consolidated into LTM . When similar regime recurs later, LTM provides faster adaptation than starting from scratch.
Practical Implementation Guide - Recommended Settings by Instrument
Futures (ES, NQ, CL):
Adaptive Period: 20-25
ML Mode: Advanced
RFF Dimensions: 16
Policies: 6
Base Risk: 1.5%
Stop Mode: ATR or Hybrid
Timeframe: 5-15 min
Forex Majors (EURUSD, GBPUSD):
Adaptive Period: 25-30
ML Mode: Advanced
RFF Dimensions: 16
Policies: 6
Base Risk: 1.0-1.5%
Stop Mode: ATR
Timeframe: 5-30 min
Cryptocurrency (BTC, ETH):
Adaptive Period: 20-25
ML Mode: Extreme (handles non-stationarity)
RFF Dimensions: 32 (captures complexity)
Policies: 6
Base Risk: 1.0% (volatility consideration)
Stop Mode: Hybrid
Timeframe: 15 min - 4 hr
Stocks (Large Cap):
Adaptive Period: 25-30
ML Mode: Advanced
RFF Dimensions: 16
Policies: 5-6
Base Risk: 1.5-2.0%
Stop Mode: Structural or Hybrid
Timeframe: 15 min - Daily
Scaling Strategy
Phase 1 (Testing - First 50 Trades):
Max Contracts: 1-2
Goal: Validate system on your instrument
Monitor: Performance stabilization, learning progress
Phase 2 (Validation - Trades 50-100):
Max Contracts: 2-3
Goal: Confirm learning convergence
Monitor: Policy stability, exploration decay
Phase 3 (Scaling - Trades 100-200):
Max Contracts: 3-5
Enable: Kelly sizing (1/4 Kelly)
Goal: Optimize capital efficiency
Monitor: Risk-adjusted returns
Phase 4 (Full Deployment - Trades 200+):
Max Contracts: 5-10
Enable: Full momentum tracking
Goal: Sustained consistent performance
Monitor: Ongoing adaptation quality
Limitations & Disclaimers
Statistical Limitations
Learning Sample Size: System requires minimum 50-100 trades for basic convergence, 200+ trades for robust learning. Early performance (first 50 trades) may not reflect mature system behavior.
Non-Stationarity Risk: Markets change over time. A system trained on one market regime may need time to adapt when conditions shift (typically 30-50 trades for adjustment).
Overfitting Possibility: With 16-32 RFF dimensions and 6 policies, system has substantial parameter space. Small sample sizes (<200 trades) increase overfitting risk. Mitigated by regularization (λ) and fractional Kelly sizing.
Technical Limitations
Computational Complexity: Extreme mode with 32 RFF dimensions, 6 policies, and full RL stack requires significant computation. May perform slowly on lower-end systems or with many other indicators loaded.
Pine Script Constraints:
No true matrix inversion (uses diagonal approximation for LinUCB)
No cryptographic RNG (uses market data as entropy)
No proper random number generation for RFF (uses deterministic pseudo-random)
These approximations reduce mathematical precision compared to academic implementations but remain functional for trading applications.
Data Requirements: Needs clean OHLCV data. Missing bars, gaps, or low liquidity (<100k daily volume) can degrade signal quality.
Forward-Looking Bias Disclaimer
Reward Calculation Uses Future Data: The RL system evaluates trades using an 8-bar forward-looking window. This means when a position enters at bar 100, the reward calculation considers price movement through bar 108.
Why This is Disclosed:
Entry signals do NOT look ahead - decisions use only data up to entry bar
Forward data used for learning only, not signal generation
In live trading, system learns identically as bars unfold in real-time
Simulates natural learning process (outcomes are only known after trades complete)
Implication: Backtested metrics reflect this 8-bar evaluation window. Live performance may vary if:
- Positions held longer than 8 bars
- Slippage/commissions differ from backtest settings
- Market microstructure changes (wider spreads, different execution quality)
Risk Warnings
No Guarantee of Profit: All trading involves substantial risk of loss. Machine learning systems can fail if market structure fundamentally changes or during unprecedented events.
Maximum Drawdown: With 1.5% base risk and 4% max total risk, expect potential drawdowns. Historical drawdowns do not predict future drawdowns. Extreme market conditions can exceed expectations.
Black Swan Events: System has not been tested under: flash crashes, trading halts, circuit breakers, major geopolitical shocks, or other extreme events. Such events can exceed stop losses and cause significant losses.
Leverage Risk: Futures and forex involve leverage. Adverse moves combined with leverage can result in losses exceeding initial investment. Use appropriate position sizing for your risk tolerance.
System Failures: Code bugs, broker API failures, internet outages, or exchange issues can prevent proper execution. Always monitor automated systems and maintain appropriate safeguards.
Appropriate Use
This System Is:
✅ A machine learning framework for adaptive strategy selection
✅ A signal generation system with probabilistic scoring
✅ A risk management system with dynamic sizing
✅ A learning system designed to adapt over time
This System Is NOT:
❌ A price prediction system (does not forecast exact prices)
❌ A guarantee of profits (can and will experience losses)
❌ A replacement for due diligence (requires monitoring and understanding)
❌ Suitable for complete beginners (requires understanding of ML concepts, risk management, and trading fundamentals)
Recommended Use:
Paper trade for 100 signals before risking capital
Start with minimal position sizing (1-2 contracts) regardless of calculated size
Monitor learning progress via dashboard
Scale gradually over several months only after consistent results
Combine with fundamental analysis and broader market context
Set account-level risk limits (e.g., maximum drawdown threshold)
Never risk more than you can afford to lose
What Makes This System Different
RPD implements academically-derived machine learning algorithms rather than simple mathematical calculations or optimization:
✅ LinUCB Contextual Bandits - Algorithm from WWW 2010 conference (Li et al.)
✅ Random Fourier Features - Kernel approximation from NIPS 2007 (Rahimi & Recht)
✅ Q-Learning, TD(λ), REINFORCE - Standard RL algorithms from Sutton & Barto textbook
✅ Meta-Learning - Learning rate adaptation based on feature correlation
✅ Online Learning - Real-time updates from streaming data
✅ Hierarchical Policies - Two-stage selection with clustering
✅ Momentum Tracking - Recent performance analysis for faster adaptation
✅ Attention Mechanism - Feature importance weighting
✅ Transfer Learning - Episodic memory consolidation
Key Differentiators:
Actually learns from trade outcomes (not just parameter optimization)
Updates model parameters in real-time (true online learning)
Adapts to changing market regimes (not static rules)
Improves over time through reinforcement learning
Implements published ML algorithms with proper citations
Conclusion
RPD Machine Learning represents a different approach from traditional technical analysis to adaptive, self-learning systems . Instead of manually optimizing parameters (which can overfit to historical data), RPD learns behavior patterns from actual trading outcomes in your specific market.
The combination of contextual bandits, reinforcement learning, random fourier features, hierarchical policy selection, and momentum tracking creates a multi-algorithm learning system designed to handle non-stationary markets better than static approaches.
After the initial learning phase (50-100 trades), the system achieves autonomous adaptation - automatically discovering which strategies work in current conditions and shifting allocation without human intervention. This represents an approach where systems adapt over time rather than remaining static.
Use responsibly. Paper trade extensively. Scale gradually. Understand that past performance does not guarantee future results and all trading involves risk of loss.
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Hybrid RSI Strategy [Heifereum ]This is a hybrid script that combines visual RSI indicator signals with an optional backtestable trading strategy.
BUY Entry: When RSI crosses above the oversold level (default 30)
SELL Exit: When RSI crosses below the overbought level (default 70)
Timeframe: Works best on trending assets (crypto, forex, indices) in 5min to 1H
Backtest Toggle: Turn ON/OFF live testing using the Enable Backtest Mode? setting
Visual Cues: Buy/Sell labels, background coloring, and alerts ready for webhook automation
Use this strategy to visually explore RSI dynamics, run performance backtests, or hook up to external bots via alerts.
Briss Thorn XtremeStrategy Description: Briss Thorn Xtreme
The Briss Thorn Xtreme is an innovative trading strategy designed to identify and capitalize on opportunities in the forex market through advanced technical analysis and dynamic risk management. This strategy combines calculations based on RSI and ATR with time and day filters, providing customized signals and real-time alerts via Discord. Ideal for traders seeking a structured and highly customizable methodology, Briss Thorn Xtreme integrates enhanced visual tools for efficient trade management.
Key Features:
RSI and ATR-Based Signals: Utilizes smoothed RSI and ATR calculations to identify trends and measure volatility, allowing for more precise detection of buy and sell opportunities.
Dynamic Stop-Loss (SL) and Take-Profit (TP) Levels: Automatically calculates SL and TP levels based on market volatility, dynamically adjusting to optimize risk management.
Advanced Discord Integration: Sends detailed alerts to your Discord channel, including information such as the asset, signal time, entry price, and SL/TP levels, facilitating real-time decision-making.
Complete Customization: Allows users to adjust key parameters such as RSI periods, smoothing factors, liquidity thresholds, trading schedules, and operation days, adapting to different trading styles and market conditions.
Enhanced Chart Visualization: Includes visual elements like candle color changes based on trend, colored boxes for SL and TP, and a summary table of recent trades, enabling quick market interpretation.
Day and Time Operation Filters: Enables selection of specific days of the week and time slots during which signals are generated, optimizing market exposure and avoiding periods of low liquidity or unwanted high volatility.
Trade Summary: Displays a summary of the last three trades directly on the chart, indicating whether TP or SL was reached, aiding in strategy performance evaluation.
Customizable Alert Messages: Allows customization of messages sent to Discord for buy and sell signals, tailoring them to your specific preferences and requirements.
Additional Visual Tools: Highlights the operational range on the chart during permitted trading hours and colors candles based on the current trend (bullish, bearish, or neutral), enhancing visibility and decision-making.
How the Strategy Works:
Technical Indicators Calculation:
- RSI (Relative Strength Index) : Calculates RSI with a defined period and smooths it using an Exponential Moving Average (EMA) to obtain a more stable and reliable signal.
- ATR (Average True Range) : Calculates ATR adjusted by a rapid liquidity factor to measure the current market volatility, thereby determining the strength of the trend.
Generating Buy and Sell Signals:
- Buy Signal: A buy signal is generated when the liquidity index surpasses the short liquidity level, indicating potential accumulation and an upward trend.
- Sell Signal: A sell signal is generated when the liquidity index falls below the long liquidity level, indicating potential distribution and a downward trend.
- Operation Conditions: Signals are only generated on selected days and times, avoiding periods of low liquidity or unwanted high volatility.
Dynamic SL and TP Levels Calculation:
- Stop-Loss (SL) and Take-Profit (TP): SL and TP levels are calculated based on the entry price and a defined number of ticks, automatically adjusting to market volatility to optimize risk management.
- SL and TP Visualization: Colored boxes are drawn on the chart for a clear visual reference of SL and TP levels, facilitating trade management.
Automatic Execution and Alerts:
- Order Execution: Upon signal generation, the strategy automatically executes a market order (buy or sell).
- Discord Alerts: Detailed alerts are sent to the configured Discord channel, providing essential information for swift decision-making, including asset, signal time, entry price, current volatility (ATR), and trend direction.
Trade Management and Monitoring:
- Trade Summary: A table on the chart displays a summary of the last three trades (Today, Yesterday, Day Before Yesterday), indicating whether TP or SL was reached, allowing real-time performance evaluation.
- Automatic Trade Closure: The strategy automatically closes trades upon reaching the established SL or TP levels, ensuring efficient risk management and preventing excessive losses.
Additional Visualization:
- Candle Coloring by Trend: Candles are colored based on the current trend (bullish, bearish, or neutral), facilitating quick identification of market direction.
- Operational Range Highlighting: The chart background is colored during permitted trading hours, highlighting active periods of the strategy and enhancing trade visibility.
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Strategy Properties (Important)
This backtest is conducted on M17 EURUSD using the following backtesting properties:
Initial Capital: $1000
Order Size: 1% of capital
Commission: $0.20 per order
Slippage: 1 tick
Pyramiding: 1 order
Price Verification for Limit Orders: 0 ticks
Recalculate on Order Execution: Enabled
Recalculate on Every Tick: Enabled
Recalculate After Order Execution: Enabled
Bar Magnifier for Backtesting Precision: Enabled
These properties ensure a realistic preview of the backtesting system. Note that default properties may vary for different reasons:
Order Size: It is essential to calculate the contract size according to the traded asset and desired risk level.
Commission and Slippage: These costs may vary depending on the market and instrument; there is no default value that guarantees realistic results.
All users are strongly recommended to adjust the properties within the script settings to align them with their trading accounts and platforms, ensuring that strategy results are realistic.
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Backtesting Results:
- Net Profit: $327.90 (32.79%)
- Total Closed Trades: 162
- Profit Percentage: 35.80%
- Profit Factor: 1.298
- Maximum Drawdown: $146.70 (10.27%)
- Average per Trade: $2.02 (0.02%)
- Average Bars per Trade: 22
These results were obtained under the mentioned conditions and properties, providing an overview of the strategy's historical performance.
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Interpretation of Results:
- The strategy has demonstrated profitability over the analyzed period, albeit with a success rate of 32.79%, indicating that success depends on a favorable risk-reward ratio.
- The profit factor of 1.298 suggests that total gains exceed total losses by this proportion.
- It is crucial to consider the maximum drawdown of 10.27% when evaluating the strategy's suitability to your risk tolerance.
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Risk Warning:
Trading with leveraged financial instruments involves a high level of risk and may not be suitable for all investors. Before deciding to trade, you should carefully consider your investment objectives, level of experience, and risk tolerance. Past performance does not guarantee future results. It is essential to perform additional testing and adjust the strategy according to your needs.
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What Makes This Strategy Original?
Unique RSI and Liquidity Focus: Unlike conventional strategies, Briss Thorn Xtreme focuses on combining RSI analysis with liquidity parameters to reflect institutional activity and macroeconomic events that may influence the market.
Advanced Technological Integration: The combination of automatic execution and customized alerts via Discord provides an efficient and modern tool for active traders.
Customization and Adaptability: The wide range of adjustable parameters allows the strategy to adapt to different assets, time zones, and trading styles, offering flexibility and complete user control.
Enhanced Visual Tools: Integrated visual elements, such as candle coloring, SL/TP boxes, and summary tables, facilitate quick market interpretation and informed decision-making.
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Additional Considerations
Continuous Testing and Optimization: Users are advised to perform additional backtests and optimize parameters based on their own observations and requirements.
Complementary Analysis: Use this strategy in conjunction with other indicators and fundamental analysis tools to reinforce decision-making and confirm generated signals.
Rigorous Risk Management: Ensure that SL and TP levels, as well as position sizes, are aligned with your risk management plan to avoid excessive losses.
Updates and Support: I am committed to providing updates and improvements based on community feedback. For inquiries or suggestions, feel free to contact me.
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Example Configuration
Assuming you want to use the strategy with the following parameters:
Discord Webhook: Your unique Discord Webhook
RSI Period: 6
RSI Smoothing Factor: 5
Rapid Liquidity Factor: 5
Liquidity Threshold: 5
SL Ticks: 100
TP Ticks: 250
SL/TP Box Width: 25 bars
Trading Days: Monday, Tuesday, Wednesday, Thursday, Friday
Trading Hours: Start at 8:00, End at 11:00
Simulated Initial Capital: $1000
Risk per Trade in Simulation: 1% of capital
Slippage and Commissions in Simulation: 1 tick slippage and $0.20 commission per trade
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Conclusion
The Briss Thorn Xtreme strategy offers an innovative approach by combining advanced technical analysis with dynamic risk management and modern technological tools. Its original and adaptable design makes it a valuable tool for traders looking to diversify their methods and capitalize on opportunities based on less conventional patterns. Ready for immediate implementation in TradingView, this strategy can enhance your trading arsenal and contribute to a more informed and structured approach in your operations.
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Final Disclaimer:
Financial markets are volatile and can present significant risks. This strategy should be used as part of a comprehensive trading approach and does not guarantee positive results. It is always advisable to consult with a professional financial advisor before making investment decisions.
AlgoBuilder [Mean-Reversion] | FractalystWhat's the strategy's purpose and functionality?
This strategy is designed for both traders and investors looking to rely and trade based on historical and backtested data using automation.
The main goal is to build profitable mean-reversion strategies that outperform the underlying asset in terms of returns while minimizing drawdown.
For example, as for a benchmark, if the S&P 500 (SPX) has achieved an estimated 10% annual return with a maximum drawdown of -57% over the past 20 years, using this strategy with different entry and exit techniques, users can potentially seek ways to achieve a higher Compound Annual Growth Rate (CAGR) while maintaining a lower maximum drawdown.
Although the strategy can be applied to all markets and timeframes, it is most effective on stocks, indices, future markets, cryptocurrencies, and commodities and JPY currency pairs given their trending behaviors.
In trending market conditions, the strategy employs a combination of moving averages and diverse entry models to identify and capitalize on upward market movements. It integrates market structure-based moving averages and bands mechanisms across different timeframes and provides exit techniques, including percentage-based and risk-reward (RR) based take profit levels.
Additionally, the strategy has also a feature that includes a built-in probability function for traders who want to implement probabilities right into their trading strategies.
Performance summary, weekly, and monthly tables enable quick visualization of performance metrics like net profit, maximum drawdown, profit factor, average trade, average risk-reward ratio (RR), and more.
This aids optimization to meet specific goals and risk tolerance levels effectively.
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How does the strategy perform for both investors and traders?
The strategy has two main modes, tailored for different market participants: Traders and Investors.
Trading:
1. Trading:
- Designed for traders looking to capitalize on bullish trending markets.
- Utilizes a percentage risk per trade to manage risk and optimize returns.
- Suitable for active trading with a focus on mean-reversion and risk per trade approach.
◓: Mode | %: Risk percentage per trade
3. Investing:
- Geared towards investors who aim to capitalize on bullish trending markets without using leverage while mitigating the asset's maximum drawdown.
- Utilizes pre-define percentage of the equity to buy, hold, and manage the asset.
- Focuses on long-term growth and capital appreciation by fully investing in the asset during bullish conditions.
- ◓: Mode | %: Risk not applied (In investing mode, the strategy uses 10% of equity to buy the asset)
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What's is FRMA? How does the triple bands work? What are the underlying calculations?
Middle Band (FRMA):
The middle band is the core of the FRMA system. It represents the Fractalyst Moving Average, calculated by identifying the most recent external swing highs and lows in the market structure.
By determining these external swing pivot points, which act as significant highs and lows within the market range, the FRMA provides a unique moving average that adapts to market structure changes.
Upper Band:
The upper band shows the average price of the most recent external swing highs.
External swing highs are identified as the highest points between pivot points in the market structure.
This band helps traders identify potential overbought conditions when prices approach or exceed this upper band.
Lower Band:
The lower band shows the average price of the most recent external swing lows.
External swing lows are identified as the lowest points between pivot points in the market structure.
The script utilizes this band to identify potential oversold conditions, triggering entry signals as prices approach or drop below the lower band.
Adjustments Based on User Inputs:
Users can adjust how the upper and lower bands are calculated based on their preferences:
Upper/Lower: This method calculates the average bands using the prices of external swing highs and lows identified in the market.
Percentage Deviation from FRMA: Alternatively, users can opt to calculate the bands based on a percentage deviation from the middle FRMA. This approach provides flexibility to adjust the width of the bands relative to market conditions and volatility.
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What's the purpose of using moving averages in this strategy? What are the underlying calculations?
Using moving averages is a widely-used technique to trade with the trend.
The main purpose of using moving averages in this strategy is to filter out bearish price action and to only take trades when the price is trading ABOVE specified moving averages.
The script uses different types of moving averages with user-adjustable timeframes and periods/lengths, allowing traders to try out different variations to maximize strategy performance and minimize drawdowns.
By applying these calculations, the strategy effectively identifies bullish trends and avoids market conditions that are not conducive to profitable trades.
The MA filter allows traders to choose whether they want a specific moving average above or below another one as their entry condition.
This comparison filter can be turned on (>) or off.
For example, you can set the filter so that MA#1 > MA#2, meaning the first moving average must be above the second one before the script looks for entry conditions. This adds an extra layer of trend confirmation, ensuring that trades are only taken in more favorable market conditions.
⍺: MA Period | Σ: MA Timeframe
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What entry modes are used in this strategy? What are the underlying calculations?
The strategy by default uses two different techniques for the entry criteria with user-adjustable left and right bars: Breakout and Fractal.
1. Breakout Entries :
- The strategy looks for pivot high points with a default period of 3.
- It stores the most recent high level in a variable.
- When the price crosses above this most recent level, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot high left bars period | ◨: Pivot high right bars period
2. Fractal Entries :
- The strategy looks for pivot low points with a default period of 3.
- When a pivot low is detected, the strategy checks if all conditions are met and the bar is closed before taking the buy entry.
◧: Pivot low left bars period | ◨: Pivot low right bars period
2. Hunt Entries :
- The strategy identifies a candle that wicks through the lower FRMA band.
- It waits for the next candle to close above the low of the wick candle.
- When this condition is met and the bar is closed, the strategy takes the buy entry.
By utilizing these entry modes, the strategy aims to capitalize on bullish price movements while ensuring that the necessary conditions are met to validate the entry points.
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What type of stop-loss identification method are used in this strategy? What are the underlying calculations?
Initial Stop-Loss:
1. ATR Based:
The Average True Range (ATR) is a method used in technical analysis to measure volatility. It is not used to indicate the direction of price but to measure volatility, especially volatility caused by price gaps or limit moves.
Calculation:
- To calculate the ATR, the True Range (TR) first needs to be identified. The TR takes into account the most current period high/low range as well as the previous period close.
The True Range is the largest of the following:
- Current Period High minus Current Period Low
- Absolute Value of Current Period High minus Previous Period Close
- Absolute Value of Current Period Low minus Previous Period Close
- The ATR is then calculated as the moving average of the TR over a specified period. (The default period is 14).
Example - ATR (14) * 2
⍺: ATR period | Σ: ATR Multiplier
2. ADR Based:
The Average Day Range (ADR) is an indicator that measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
Calculation:
- To calculate the ADR for a particular day:
- Calculate the average of the high prices over a specified number of days.
- Calculate the average of the low prices over the same number of days.
- Find the difference between these average values.
- The default period for calculating the ADR is 14 days. A shorter period may introduce more noise, while a longer period may be slower to react to new market movements.
Example - ADR (20) * 2
⍺: ADR period | Σ: ADR Multiplier
3. PL Based:
This method places the stop-loss at the low of the previous candle.
If the current entry is based on the hunt entry strategy, the stop-loss will be placed at the low of the candle that wicks through the lower FRMA band.
Example:
If the previous candle's low is 100, then the stop-loss will be set at 100.
This method ensures the stop-loss is placed just below the most recent significant low, providing a logical and immediate level for risk management.
Application in Strategy (ATR/ADR):
- The strategy calculates the current bar's ADR/ATR with a user-defined period.
- It then multiplies the ADR/ATR by a user-defined multiplier to determine the initial stop-loss level.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop.
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance.
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What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
Percentage (%) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain percentage above the entry.
Calculation:
Break-even level = Entry Price * (1 + Percentage / 100)
Example:
If the entry price is $100 and the break-even percentage is 5%, the break-even level is $100 * 1.05 = $105.
Risk-to-Reward (RR) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain RR ratio.
Calculation:
Break-even level = Entry Price + (Initial Risk * RR Ratio)
Example:
If the entry price is $100, the initial risk is $10, and the RR ratio is 2, the break-even level is $100 + ($10 * 2) = $120.
FRMA Based:
Moves the stop-loss to break-even when the price hits the FRMA level at which the entry was taken.
Calculation:
Break-even level = FRMA level at the entry
Example:
If the FRMA level at entry is $102, the break-even level is set to $102 when the price reaches $102.
For TP1 (Take Profit 1):
- You can choose to set a take profit level at which your position gets fully closed or 50% if the TP2 boolean is enabled.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
For TP2 (Take Profit 2):
- You can choose to set a take profit level at which your position gets fully closed.
- As with break-even and TP1, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP2 level as a percentage amount above the entry price or based on RR.
When Both Percentage (%) Based and RR Based Take Profit Levels Are Off:
The script will adjust the take profit level to the higher FRMA band set within user inputs.
Calculation:
Take profit level = Higher FRMA band length/timeframe specified by the user.
This ensures that when neither percentage-based nor risk-to-reward-based take profit methods are enabled, the strategy defaults to using the higher FRMA band as the take profit level, providing a consistent and structured approach to profit-taking.
For TP1 and TP2, it's specifying the price levels at which the position is partially or fully closed based on the chosen method (percentage or RR) above the entry price.
These calculations are crucial for managing risk and optimizing profitability in the strategy.
⍺: BE/TP type (%/RR) | Σ: how many RR/% above the current price
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What's the ADR filter? What does it do? What are the underlying calculations?
The Average Day Range (ADR) measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
The period of the ADR filter used in this strategy is tied to the same period you've used for your initial stop-loss.
Users can define the minimum ADR they want to be met before the script looks for entry conditions.
ADR Bias Filter:
- Compares the current bar ADR with the ADR (Defined by user):
- If the current ADR is higher, it indicates that volatility has increased compared to ADR (DbU).(⬆)
- If the current ADR is lower, it indicates that volatility has decreased compared to ADR (DbU).(⬇)
Calculations:
1. Calculate ADR:
- Average the high prices over the specified period.
- Average the low prices over the same period.
- Find the difference between these average values in %.
2. Current ADR vs. ADR (DbU):
- Calculate the ADR for the current bar.
- Calculate the ADR (DbU).
- Compare the two values to determine if volatility has increased or decreased.
By using the ADR filter, the strategy ensures that trades are only taken in favorable market conditions where volatility meets the user's defined threshold, thus optimizing entry conditions and potentially improving the overall performance of the strategy.
>: Minimum required ADR for entry | %: Current ADR comparison to ADR of 14 days ago.
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What's the probability filter? What are the underlying calculations?
The probability filter is designed to enhance trade entries by using buyside liquidity and probability analysis to filter out unfavorable conditions.
This filter helps in identifying optimal entry points where the likelihood of a profitable trade is higher.
Calculations:
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Equilibrium levels.
3. Understanding probability calculations
1. Upon the formation of a new range, the script waits for the price to reach and tap into equilibrium or the 50% level. Status: "⏸" - Inactive
2. Once equilibrium is tapped into, the equilibrium status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
5. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
Example - BSL > 55%
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What's the range length Filter? What are the underlying calculations?
The range length filter identifies the price distance between buyside and sellside liquidity levels in percentage terms. When enabled, the script only looks for entries when the minimum range length is met. This helps ensure that trades are taken in markets with sufficient price movement.
Calculations:
Range Length (%) = ( ( Buyside Level − Sellside Level ) / Current Price ) ×100
Range Bias Identification:
Bullish Bias: The current range price has broken above the previous external swing high.
Bearish Bias: The current range price has broken below the previous external swing low.
Example - Range length filter is enabled | Range must be above 1%
>: Minimum required range length for entry | %: Current range length percentage in a (Bullish/Bearish) range
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What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
Increased Flexibility: The filter provides increased flexibility, allowing traders to adapt the strategy to their specific needs and preferences.
Example - Day filter | Session Filter
θ: Session time | Exchange time-zone
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What tables are available in this script?
Table Type:
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades and more.
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most mean-reversion successful strategies have a percent profitability of 40-80% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- OFF: Hides the performance table.
Profit Color:
- Allows users to set the color for representing profit in the performance table, helping to quickly distinguish profitable periods.
Loss Color:
- Allows users to set the color for representing loss in the performance table, helping to quickly identify loss-making periods.
These customizable tables provide traders with flexible and detailed performance analysis, aiding in better strategy evaluation and optimization.
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User-input styles and customizations:
To facilitate studying historical data, all conditions and rules can be applied to your charts. By plotting background colors on your charts, you'll be able to identify what worked and what didn't in certain market conditions.
Please note that all background colors in the style are disabled by default to enhance visualization.
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How to Use This Algobuilder to Create a Profitable Edge and System:
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions/slippage on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker or prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 100 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade value is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, and net profit with minimum drawdown.
- Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
Automation:
- Once you’re confident in your strategy, you can use the automation section to connect the algorithm to your broker or prop firm.
- Trade a fully automated and backtested trading strategy, allowing for hands-free execution and management.
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What makes this strategy original?
1. Incorporating direct integration of probabilities into the strategy.
2. Utilizing built-in market structure-based moving averages across various timeframes.
4. Offering both investing and trading strategies, facilitating optimization from different perspectives.
5. Automation for efficient execution.
6. Providing a summary table for instant access to key parameters of the strategy.
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How to use automation?
For Traders:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Enter your PineConnector License ID in the designated field.
3. Specify the desired risk level.
4. Provide the Metatrader symbol.
5. Check for chart updates to ensure the automation table appears on the top right corner, displaying your License ID, risk, and symbol.
6. Set up an alert with the strategy selected as Condition and the Message as {{strategy.order.alert_message}}.
7. Activate the Webhook URL in the Notifications section, setting it as the official PineConnector webhook address.
8. Double-check all settings on PineConnector to ensure the connection is successful.
9. Create the alert for entry/exit automation.
For Investors:
1. Ensure the strategy parameters are properly set based on your optimized parameters.
2. Choose "Investing" in the user-input settings.
3. Create an alert with a specified name.
4. Customize the notifications tab to receive alerts via email.
5. Buying/selling alerts will be triggered instantly upon entry or exit order execution.
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Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Versatile Moving Average StrategyVersatile Moving Average Strategy (VMAS)
Overview:
The Versatile Moving Average Strategy (VMAS) is designed to provide traders with a flexible approach to trend-following, utilizing multiple types of moving averages. This strategy allows for customization in choosing the moving average type and length, catering to various market conditions and trading styles.
Key Features:
- Multiple Moving Average Types: Choose from SMA, EMA, SMMA (RMA), WMA, VWMA, HULL, LSMA, and ALMA to best suit your trading needs.
- Customizable Inputs: Adjust the moving average length, source of price data, and stop-loss source to fine-tune the strategy.
- Target Percent: Set the percentage difference between successive profit targets to manage your risk and rewards effectively.
- Position Management: Enable or disable long and short positions, allowing for versatility in different market conditions.
- Commission and Slippage: The strategy includes realistic commission settings to ensure accurate backtesting results.
Strategy Logic:
1. Moving Average Calculation: The selected moving average is calculated based on user-defined parameters.
2. Entry Conditions:
- A long position is entered when the entry source crosses over the moving average, if long positions are enabled.
- A short position is entered when the entry source crosses under the moving average, if short positions are enabled.
3. Stop-Loss: Positions are closed if the stop-loss source crosses the moving average in the opposite direction.
4. Profit Targets: Multiple profit targets are defined, with each target set at an incremental percentage above (for long positions) or below (for short positions) the entry price.
Default Properties:
- Account Size: $10000
- Commission: 0.01% per trade
- Risk Management: Positions are sized to risk 80% of the equity per trade, because we get very tight stoploss when position is open.
- Sample Size: Backtesting has been conducted to ensure a sufficient sample size of trades, ideally more than 100 trades.
How to Use:
1. Configure Inputs: Set your preferred moving average type, length, and other input parameters.
2. Enable Positions: Choose whether to enable long, short, or both types of positions.
3. Backtest and Analyze: Run backtests with realistic settings and analyze the results to ensure the strategy aligns with your trading goals.
4. Deploy and Monitor: Once satisfied with the backtesting results, deploy the strategy in a live environment and monitor its performance.
This strategy is suitable for traders looking to leverage moving averages in a versatile and customizable manner. Adjust the parameters to match your trading style and market conditions for optimal results.
Note: Ensure the strategy settings used for publication are the same as those described here. Always conduct thorough backtesting before deploying any strategy in a live trading environment.
Pineconnector Strategy Template (Connect Any Indicator)Hello traders,
If you're tired of manual trading and looking for a solid strategy template to pair with your indicators, look no further.
This Pine Script v5 strategy template is engineered for maximum customization and risk management.
Best part?
It’s optimized for Pineconnector, allowing seamless integration with MetaTrader 4 and 5.
This powerful tool gives a lot of power to those who don't know how to code in Pinescript and are looking to automate their indicators' signals on Metatrader 4/5.
IMPORTANT NOTES
Pineconnector is a trading bot software that forwards TradingView alerts to your Metatrader 4/5 for automating trading.
Many traders don't know how to dynamically create Pineconnector-compatible alerts using the data from their TradingView scripts.
Traders using trading bots want their alerts to reflect the stop-loss/take-profit/trailing-stop/stop-loss to break options from your script and then create the orders accordingly.
This script showcases how to create Pineconnector alerts dynamically.
Pineconnector doesn't support alerts with multiple Take Profits.
As a workaround, for 2 TPs, I had to open two trades.
It's not optimal, as we end up paying more spreads for that extra trade - however, depending on your trading strategy, it may not be a big deal.
TRADINGVIEW ALERTS
1) You'll have to create one alert per asset X timeframe = 1 chart.
Example: 1 alert for EUR/USD on the 5 minutes chart, 1 alert for EUR/USD on the 15-minute chart (assuming you want your bot to trade the EUR/USD on the 5 and 15-minute timeframes)
2) Select the Order fills and alert() function calls condition
3) For each alert, the alert message is pre-configured with the text below
{{strategy.order.alert_message}}
Please leave it as it is.
It's a TradingView native variable that will fetch the alert text messages built by the script.
4) Don't forget to set the Pineconnector webhook URL in the Notifications tab of the TradingView alerts UI.
You’ll find the URL on the Pineconnector documentation website.
EA CONFIGURATION
1) The Pyramiding in the EA on Metatrader must be set to 2 if you want to trade with 2 TPs => as it's opening 2 trades.
If you only want 1 TP, set the EA Pyramiding to 1.
Regarding the other EA settings, please refer to the Pineconnector documentation on their website.
2) In the EA, you can set a risk (= position size type) in %/lots/USD, as in the TradingView backtest settings.
KEY FEATURES
I) Modular Indicator Connection
* plug in your existing indicator into the template.
* Only two lines of code are needed for full compatibility.
Step 1: Create your connector
Adapt your indicator with only 2 lines of code and then connect it to this strategy template.
To do so:
1) Find in your indicator where the conditions print the long/buy and short/sell signals.
2) Create an additional plot as below
I'm giving an example with a Two moving averages cross.
Please replicate the same methodology for your indicator, whether it's a MACD , ZigZag , Pivots , higher-highs, lower-lows, or whatever indicator with clear buy and sell conditions.
//@version=5
indicator("Supertrend", overlay = true, timeframe = "", timeframe_gaps = true)
atrPeriod = input.int(10, "ATR Length", minval = 1)
factor = input.float(3.0, "Factor", minval = 0.01, step = 0.01)
= ta.supertrend(factor, atrPeriod)
supertrend := barstate.isfirst ? na : supertrend
bodyMiddle = plot(barstate.isfirst ? na : (open + close) / 2, display = display.none)
upTrend = plot(direction < 0 ? supertrend : na, "Up Trend", color = color.green, style = plot.style_linebr)
downTrend = plot(direction < 0 ? na : supertrend, "Down Trend", color = color.red, style = plot.style_linebr)
fill(bodyMiddle, upTrend, color.new(color.green, 90), fillgaps = false)
fill(bodyMiddle, downTrend, color.new(color.red, 90), fillgaps = false)
buy = ta.crossunder(direction, 0)
sell = ta.crossunder(direction, 0)
//////// CONNECTOR SECTION ////////
Signal = buy ? 1 : sell ? -1 : 0
plot(Signal, title = "Signal", display = display.data_window)
//////// CONNECTOR SECTION ////////
Important Notes
🔥 The Strategy Template expects the value to be exactly 1 for the bullish signal and -1 for the bearish signal
Now, you can connect your indicator to the Strategy Template using the method below or that one.
Step 2: Connect the connector
1) Add your updated indicator to a TradingView chart
2) Add the Strategy Template as well to the SAME chart
3) Open the Strategy Template settings, and in the Data Source field, select your 🔌Connector🔌 (which comes from your indicator)
Note it doesn’t have to be named 🔌Connector🔌 - you can name it as you want - however, I recommend an explicit name you can easily remember.
From then, you should start seeing the signals and plenty of other stuff on your chart.
🔥 Note that whenever you update your indicator values, the strategy statistics and visuals on your chart will update in real-time
II) Customizable Risk Management
- Choose between percentage or USD modes for maximum drawdown.
- Set max consecutive losing days and max losing streak length.
- I used the code from my friend @JosKodify for the maximum losing streak. :)
Will halt the EA and backtest orders fill whenever either of the safeguards above are “broken”
III) Intraday Risk Management
- Limit the maximum intraday losses both in percentage or USD.
- Option to set a maximum number of intraday trades.
- If your EA gets halted on an intraday chart, auto-restart it the next day.
IV) Spread and Account Filters
- Trade only if the spread is below a certain pip value.
- Set requirements based on account balance or equity.
V) Order Types and Position Sizing
- Choose between market, limit, or stop orders.
- Set your position size directly in the template.
Please use the position size from the “Inputs” and not the “Properties” tab.
Reason : The template sends the order on the same candle as the entry signals - at those entry signals candles, the position size isn’t computed yet, and the template can’t then send it to Pineconnector.
However, you can use the position size type (USD, contracts, %) from the “Properties” tab for backtesting.
In the EA, you can define the position size type for your orders in USD or lots or %.
VI) Advanced Take-Profit and Stop-Loss Options
- Choose to set your SL/TP in either pips or percentages.
- Option for multiple take-profit levels and trailing stop losses.
- Move your stop loss to break even +/- offset in pips for “risk-free” trades.
VII) Logger
The Pineconnector commands are logged in the TradingView logger.
You'll find more information about it in this TradingView blog post .
WHY YOU MIGHT NEED THIS TEMPLATE
1) Transform your indicator into a Pineconnector trading bot more easily than before
Connect your indicator to the template
Create your alerts
Set your EA settings
2) Save Time
Auto-generated alert messages for Pineconnector.
I tested them all, and I checked with the support team what could/can’t be done
3) Be in Control
Manage your trading risks with advanced features.
4) Customizable
Fits various trading styles and asset classes.
REQUIREMENTS
* Make sure you have your Pineconnector license ID.
* Create your alerts with the Pineconnector webhook URL
* If there is any issue with the template, ask me in the comments section - I’ll answer quickly.
BACKTEST RESULTS FROM THIS POST
1) I connected this strategy template to a dummy Supertrend script.
I could have selected any other indicator or concept for this script post.
I wanted to share an example of how you can quickly upgrade your strategy, making it compatible with Pineconnector.
2) The backtest results aren't relevant for this educational script publication.
I used realistic backtesting data but didn't look too much into optimizing the results, as this isn't the point of why I'm publishing this script.
This strategy is a template to be connected to any indicator - the sky is the limit. :)
3) This template is made to take 1 trade per direction at any given time.
Pyramiding is set to 1 on TradingView.
The strategy default settings are:
* Initial Capital: 100000 USD
* Position Size: 1 contract
* Commission Percent: 0.075%
* Slippage: 1 tick
* No margin/leverage used
WHAT’S COMING NEXT FOR YOU GUYS?
I’ll make the same template for ProfitView, then for AutoView, and then for Alertatron.
All of those are free and open-source.
I have no affiliations with any of those companies - I'm publishing those templates as they will be useful to many of you.
Dave
Risk Management and Positionsize - MACD exampleMastering Risk Management
Risk management is the cornerstone of successful trading, and it's often the difference between turning a profit and suffering a loss. In light of its importance, I share a risk management tool which you can use for your trading strategies. The script not only assists in position sizing but also comes with built-in technical features that help in market timing. Let's delve into the nitty-gritty details.
Input Parameter: MarginFactor
One of the key features of the script is the MarginFactor input parameter. This element lets you control the portion of your equity used for placing each trade. A MarginFactor of -0.5 means 50% of your total equity will be deployed in placing the position size. Although Tradingview has a built-in option to adjust position sizing in a same way, I personally prefer to have the logic in my pinecode script. The main reason is userexperience in managing and testing different settings for different charts, timeframes and instruments (with the same strategy).
Stoploss and MarginFactor
If your strategy has a 4% stop-loss, you can choose to use only 50% of your equity by setting the MarginFactor to -0.5. In this case, you are effectively risking only 2% of your total capital per trade, which aligns well with the widely-accepted rule of thumb suggesting a 1-2% risk per trade. Similar if your stoploss is only 1% you can choose to change the MarginFactor to 1, resulting in a positionsize of 200% of your equity. The total risk would be again 2% per trade if your stoploss is set to 1%.
Max Drawdown and MarginFactor
Your MarginFactor setting can also be aligned with the maximum drawdown of your strategy, seen during a backtested period of 2-3 years. For example, if the max drawdown is 15%, you could calibrate your MarginFactor accordingly to limit your risk exposure.
Option to Toggle Number of Contracts
The script offers the option to toggle between using a percentage of equity for position sizing or specifying a fixed number of contracts. Utilizing a percentage of equity might yield unrealistic backtest results, especially over longer periods. This occurs because as the capital grows, the absolute position size also increases, potentially inflating the accumulated returns generated by the backtester. On the other hand, setting a fixed number of contracts as your position size offers a more stable and realistic ROI over the backtested period, as it removes the compounding effect on position sizes.
Key Features Strategy
MACD High Time Frame Entry and Exit Logic
The strategy employs a high time frame MACD (Moving Average Convergence Divergence) to make entry and exit decisions. You can easily adjust the timeframe settings and MACD settings in the inputsection to trade on lower timeframes. For more information on the HTF MACD with dynamic smoothing see:
Moving Average High Time Frame Filter
To reduce market 'noise', the strategy incorporates a high time frame moving average filter. This ensures that the trades are aligned with the dominant market trend (trading the trend). In the inputsection traders can easily switch between different type of moving averages. For more information about this HTF filter see:
Dynamic Smoothing
The script includes a feature for dynamic smoothing. The script contains The timeframeToMinutes(tf) function to convert any given time frame into its equivalent in minutes. For example, a daily (D) time frame is converted into 1440 minutes, a weekly (W) into 10,080 minutes, and so forth. Next the smoothing factor is calculated by dividing the minutes of the higher time frame by those of the current time frame. Finally, the script applies a Simple Moving Average (SMA) over the MACD, SIGNAL, and HIST values, MA filter using the dynamically calculated smoothing factor.
User Convenience: One of the major benefits is that traders don't need to manually adjust the smoothing factor when switching between different time frames. The script does this dynamically.
Visual Consistency: Dynamic smoothing helps traders to more accurately visualize and interpret HTF indicators when trading on lower time frames.
Time Frame Restriction: It's crucial to note that the operational time frame should always be lower than the time frame selected in the input sections for dynamic smoothing to function as intended.
By incorporating this dynamic smoothing logic, the script offers traders a nuanced yet straightforward way to adapt High Time Frame indicators for lower time frame trading, enhancing both adaptability and user experience.
Limitations: Exit Strategy
It's crucial to note that the script comes with a simplified exit strategy, devoid of features like a stop-loss, trailing stop-loss or multiple take profits. This means that while the script focuses on entries and risk management, it might result in higher losses if market conditions unexpectedly turn unfavorable.
Conclusion
Effective risk management is pivotal for trading success, and this TradingView script is designed to give you a better idea how to implement positions sizing with your preferred strategy. However, it's essential to note that this tool should not be considered financial advice. Always perform your due diligence and consult with financial advisors before making any trading decisions.
Feel free to use this risk management tool as building block in your trading scripts, Happy Trading!
Power Trend v1.0Background to the tool
The tool was built out of frustration. Having traded for many years with a reasonable level of success I was always frustrated that my trading never went up a level. The world of trading is filled with people having so much more success than me and this level of FOMO really bothered me and resulted in inconsistency and countless hours sitting in front of a screen, hoping for the best. I also became a little bit of an indicator junkie - was there a holy grail indicator out there for me? I always felt that as a retail trader I was behind the curve. I started to investigate how the major market participants trade and make money and I was astounded at the level of success that they get from creating strategies and sticking to it. The market is driven largely by a "black boxes" which, for us retail traders are outside of our ability to access. I wanted to build a tool that could give me a traders edge.
Another factor that has always bothered me was when reading investing books there is a general assumption that a standard entry, say 8/13 cross over, works on all stocks. However, it is not the case and it can be frustrating for a trader using a set up and not realizing that the set up was/is the problem, not the trader. This realization alone has made a huge impact on my trading. The big boxes that control the market know this already.
Also, a lot of indicators that are available don’t take advantage of the backtesting capability provided in Tradingview. It is fairly simple to find 8-9 trades where a set up worked and then fall into the trade of assuming that it cannot fail. Knowing which set ups work and how frequently it will print will change the way that you trade.
The goal with the tool is to identify setups that have worked in the past with a high degree of profitability, high profit factor and low drawdown and using the planning tool allows you to customize the setup to find exactly what you are looking for across any tradeable asset on TradingView.
Over the past 20 years I have realized the following:
1) Not all entries and signals work the same on all stocks and knowing the historical performance of a strategy is critical
2) Not having a plan in advance lowers your probability of success
3) Developing consistency in analysis is critical
4) Developing confidence in your own plan is more important than whose trades you try to copy
5) Having 30 indicators does not help you trade better - it leads to more frustration
So here is the product of these realisations:
1) The tool looks across the most common entry strategies (RMA / EMA / SMA / HMA / WMA cross on 5 dimensions of type and 5 common crossovers) and can be used on 19 different time frames giving you guidance on what the best set up is for the stock you are analysing
2) It incorporates volatility into the strategy – when stocks are trading outside of a predetermined volatility band, a trade will not be entered. This accommodates traders who tend to get shaken out of trades too early.
3) It looks at the impact of “buying the dip” – often a common strategy employed by many traders which now can be backtested and reviewed to see if it actually helped or hindered the trade.
4) It measures your trade plan against your R – what you are willing to risk – and calculates your target profit based on your R multiple
5) It provides a non repaint signal on your base strategy and provides you with signals to trade smaller or shorter signals within the bigger strategy.
There are some additional visual tools:
• Squeeze signals - I am a big fan of the TTM squeeze however the Squeeze by itself can be hard to trade. Seeing a squeeze fire long on a chart can add to trade confidence.
• Seeing zones of support and resistance rather than single lines can also give you some leeway in terms of not getting pushed out of a trade too soon.
The backtester is always reviewed on a multi year period to get an understanding of win rate %, profit ratio and average duration of trade. As an option trader knowing that a high probability move is playing out allows me to make sure that I don’t undercut the time frame for the expiration of the option relative to the historical average duration of a trade. Backtesting on shorter times is unrealistic.
Key benefits
1) It will save you a ton of time. I don’t have to sit in front of a screen watching ticks each day. I can plan for an entry, set an alert for a trade and when the conditions are met the TradingView system sends me a message and I will go and confirm a trade, execute it, set my alerts for control and move on with my life.
2) It allows me to review trade ideas in a consistent manner using the best trade plan and set up for a stock.
3) It forces me to be patient and not panic (always a good thing). With an adjustable volatility feature I can modify the volatility band in the trade plan to accommodate choppy market conditions.
4) It looks at both sides of the market (long and short) and you can calculate the impact of being market neutral or having a directional bias.
I hope this tool helps you to achieve some degree of peace in your trading.
MA trading Tool V2.0Background to the tool
The tool was built out of frustration. Having traded for many years with a reasonable level of success I was always frustrated that my trading never went up a level. The world of trading is filled with people having so much more success than me and this level of FOMO really bothered me and resulted in inconsistency and countless hours sitting in front of a screen, hoping for the best. I also became a little bit of an indicator junkie - was there a holy grail indicator out there for me? I always felt that as a retail trader I was behind the curve. I started to investigate how the major market participants trade and make money and I was astounded at the level of success that they get from creating strategies and sticking to it. The market is driven largely by a "black boxes" which, for us retail traders are outside of our ability to access. I wanted to build a tool that could give me a traders edge.
Another factor that has always bothered me was when reading investing books there is a general assumption that a standard entry, say 8/13 cross over, works on all stocks. However, it is not the case and it can be frustrating for a trader using a set up and not realizing that the set up was/is the problem, not the trader. This realization alone has made a huge impact on my trading. The big boxes that control the market know this already.
Also, a lot of indicators that are available don’t take advantage of the backtesting capability provided in Tradingview. It is fairly simple to find 8-9 trades where a set up worked and then fall into the trade of assuming that it cannot fail. Knowing which set ups work and how frequently it will print will change the way that you trade.
The goal with the tool is to identify setups that have worked in the past with a high degree of profitability, high profit factor and low drawdown and using the planning tool allows you to customize the setup to find exactly what you are looking for across any tradeable asset on TradingView.
Over the past 20 years I have realized the following:
1) Not all entries and signals work the same on all stocks and knowing the historical performance of a strategy is critical
2) Not having a plan in advance lowers your probability of success
3) Developing consistency in analysis is critical
4) Developing confidence in your own plan is more important than whose trades you try to copy
5) Having 30 indicators does not help you trade better - it leads to more frustration
So here is the product of these realisations:
1) The tool looks across the most common entry strategies (RMA / EMA / SMA / HMA / WMA cross on 5 dimensions of type and 5 common crossovers) and can be used on 19 different time frames giving you guidance on what the best set up is for the stock you are analysing
2) It incorporates volatility into the strategy – when stocks are trading outside of a predetermined volatility band, a trade will not be entered. This accommodates traders who tend to get shaken out of trades too early.
3) It looks at the impact of “buying the dip” – often a common strategy employed by many traders which now can be backtested and reviewed to see if it actually helped or hindered the trade.
4) It measures your trade plan against your R – what you are willing to risk – and calculates your target profit based on your R multiple
5) It provides a non repaint signal on your base strategy and provides you with signals to trade smaller or shorter signals within the bigger strategy.
6) This includes elements from the Squeeze Tool namely the histogram and. stochastic entry and exit criteria
There are some additional visual tools:
• Squeeze signals - I am a big fan of the TTM squeeze however the Squeeze by itself can be hard to trade. Seeing a squeeze fire long on a chart can add to trade confidence.
• Seeing zones of support and resistance rather than single lines can also give you some leeway in terms of not getting pushed out of a trade too soon.
The backtester is always reviewed on a 2 to 3 year period to get an understanding of win rate %, profit ratio and average duration of trade. As an option trader knowing that a high probability move is playing out allows me to make sure that I don’t undercut the time frame for the expiration of the option relative to the historical average duration of a trade. Backtesting on shorter times is unrealistic.
Key benefits
1) It will save you a ton of time. I don’t have to sit in front of a screen watching ticks each day. I can plan for an entry, set an alert for a trade and when the conditions are met the TradingView system sends me a message and I will go and confirm a trade, execute it, set my alerts for control and move on with my life.
2) It allows me to review trade ideas in a consistent manner using the best trade plan and set up for a stock.
3) It forces me to be patient and not panic (always a good thing). With an adjustable volatility feature I can modify the volatility band in the trade plan to accommodate choppy market conditions.
4) It looks at both sides of the market (long and short) and you can calculate the impact of being market neutral or having a directional bias.
I hope this tool helps you to achieve some degree of peace in your trading.
To get access to the tool, please contact the author.
AltAlgoHello Traders ,
the AltAlgo is here! – our brand new trading multitool. It is an all-in-one backtesting and alert script based on our proprietary strategy and algorithms. Including many useful customizable features.
About one year ago we published our AltTrader Scalp & Swing indicator. We made a backtester for it named AltTrader BackTester Swing&Scalp. This pair of indicator and strategy turned out to be a great success. After one year of constant development and updates, we are proud to present to you our latest AltAlgo – our newest strategy script with the ability to send alerts! No more copying inputs from strategy to indicator to set alerts. Now you will prepare a strategy and create alerts in one script. We also added some new features.
AltAlgo is a great tool that helps you find the best possible entries for your trades. It's not a limited strategy for only some timeframe periods or assets. You can use it on Crypto, Forex, Stocks, Indices, and basically any chart that is available on TradingView. It is suitable for short-term scalping, mid-term and long-term swing trading, both spot and futures.
AltAlgo combines many algorithms to provide the best possible entry for you. It uses ATR, Supertrend, many different moving averages, and our proprietary system of rules and calculations to plot an ideal BUY or SELL signal for you, across all markets.
The main features/settings of the AltAlgo include :
Specific testing period - you can specify the start and end date to backtest, also the time
Power - the main variable responsible for the AltAlgo sensitivity and the amount of signals
AltFilters - these filters aim to detect the trend and prevent sending signals when the price consolidates
Supertrend - Multi time frame implementation of the famous Supertrend filter to follow the trend direction even more
Only long / only short mode - could be useful for spot trading or to create separate strategies for longs and shorts
COOS (close on opposite signal) - you can choose for the strategy to close open trades when an opposite signal comes in or not
Breakeven - if you want the AltAlgo to move stop loss to BE on chosen take profit level
Trailing stop-loss - it uses the moving target method for trailing from a chosen take profit level
Four levels for taking a profit - separate take profit levels for long and short direction, all calculations can be set in %, pips or ATR
DCA entry - function to improve your entry price and create an entry ZONE in case the price goes in the opposite direction
MultiMA - you can choose one of the 27 moving averages added to filter signals (above MA only BUY, below MA only SELL)
Sessions - we have predefined main trading sessions (America, Europe, Asia) or you can use custom settings based on any day of the week, any hours and time zones
External filter - you can use your own script to filter signals given by the AltAlgo
Miscellaneous - long/short trade background coloring, showing peak profit, etc.
Notice alerts – AltAlgo can send you additional alerts informing you about running trade like take profit or stop loss hits, flips, etc.
External output - you can use the AltAlgo as a signal provider for your other script
Alerts - fully customizable alerts for BUY/SELL signals, supporting entry zone (DCA), automatic ticker renaming (for example BTCUSDTPERP to BTCUSDT), and the option to add top and bottom lines of free text
As you can read, the AltAlgo is packed with lots of useful features, which make it irreplaceable in your trading journey. We put all our knowledge and experience to make this tool and we hope it would be useful for you and you make a lot of profits with it.
We also have a kind, amazing and helpful community of AltAlgo users, which help each other every day and share their knowledge and setups in their trading journey.
Strategy properties used:
Initial capital - 10000
Order size - 10%
Commission - 0.04%
ONWAY Indicator PV6The ONWAY indicator is a comprehensive and earnestly designed tool aimed to increase confidence in a traders market bias. ONWAY analyzes market trends, market strength, and price action near key pivot levels to form a bias on future price action. Of course, it is fundamentally impossible to predict the future, but we all try it now don't we. Gain an edge in the markets and add ONWAY to your tool box.
ONWAY Functionality
Confirmation Signals: ONWAY provides real-time, non-repaint BUY and SELL signals upon the active timeframes candle close.
Targets and Stops: ONWAY will, upon signal confirmation, set a target and stop loss.
Position Management: ONWAY will monitor its current position, if one exists, and attempt to tighten the stop loss if possible.
ONWAY Details
Symbols and Timeframes: ONWAY is restricted to approved symbols and timeframes indicated by the 🟢 icon next to "Optimized:" and "ONWAY Timeframe:" on the dashboard. If an unapproved combination of symbol and timeframe is in use, ONWAY will be disabled (no signals will be visible). See author's instructions for the current ONWAY watchlist or to submit a symbol/timeframe request.
Position Details: Apart from the on chart signals and position plot, the ONWAY dashboard will indicate the current position, entry price, target price, and stop price.
Stop Loss: ONWAY has a unique stop loss/exit strategy that has proved, based on our calculations, to be advantageous. If price reaches or exceeds the stop loss, ONWAY will not close the position unless there is a candle close on the active timeframe exceeding the stop level. This is known as a soft stop loss and identified on the dashboard as "(Soft)" next to the stop price. Additionally, the stop loss will change throughout the position, following the low, for a long position or high, for a short position, within a given period, but the soft stop loss will not exceed beyond a 1:1 risk to reward ratio (the risk will always be equal to or less than the potential reward). It is importance to be aware that the soft stop is utilized at this 1:1 threshold as well. On the plus side, the changing stop loss will impose a risk free position if it finds itself between the entry price and target price. At this stage the soft stop is no longer utilized, the stop loss can only approach the target price, and profit is uhhh.....certain (I don't think the mods will like that word 😉). If the soft stop is no longer in use, the dashboard will indicate this with "(Hard)" next to the stop price.
Position Sizing: The position sizing used for the backtested results is displayed on the dashboard next to "Strategy Lot Size:". This position size is provided solely as a reference for the backtest results. The choice of a position size is left to the users discretion.
Backtest Results: With any strategy, backtesting is an excellent way to judge performance and viability, but it is important to recognize that past performance does not confirm repeatability in future market conditions.
Updates: ONWAY updates its acceptable symbols frequently to account for everchanging market conditions. This includes adding new symbols, rejecting previously compatible symbols, and modifying the optimal window for current symbols.
Acceptability Criteria: The criteria for a symbol to be deemed acceptable requires that its backtested results deliver a win rate greater than 70%, profit factor greater than 1.2, and its equity chart appear favorable. These metrics are available to users by clicking on "Strategy Tester" located on the bottom panel of the chart view.
Accessibility: To gain access to ONWAY, see the author's instructions below.
Use of this script implies that you acknowledge that past performance does not necessarily indicate future results and that guarantees are not possible in this trading realm.
