Buy The Dip - ENGThis script implements a grid trading strategy for long positions in the USDT market. The core idea is to place a series of buy limit orders at progressively lower prices below an initial entry point, aiming to lower the average entry price as the price drops. It then aims to exit the entire position when the price rises a certain percentage above the average entry price.
Here's a detailed breakdown:
1. Strategy Setup (`strategy` function):
`'거미줄 자동매매 250227'`: The name of the strategy.
`overlay = true`: Draws plots and labels directly on the main price chart.
`pyramiding = 15`: Allows up to 15 entries in the same direction (long). This is essential for grid trading, as it needs to open multiple buy orders.
`initial_capital = 600`: Sets the starting capital for backtesting to 600 USDT.
`currency = currency.USDT`: Specifies the account currency as USDT.
`margin_long/short = 0`: Doesn't define specific margin requirements (might imply spot trading logic or rely on exchange defaults if used live).
`calc_on_order_fills = false`: Strategy calculations happen on each bar's close, not just when orders fill.
2. Inputs (`input`):
Core Settings:
`lev`: Leverage (default 10x). Used to calculate position sizes.
`Investment Percentage %`: Percentage of total capital to allocate to the initial grid (default 80%).
`final entry Percentage %`: Percentage of the *remaining* capital (100 - `Investment Percentage %`) to use for the "semifinal" entry (default 50%). The rest goes to the "final" entry.
`Price Adjustment Length`: Lookback period (default 4 bars) to determine the initial `maxPrice`.
`price range`: The total percentage range downwards from `maxPrice` where the grid orders will be placed (default -10%, meaning 10% down).
`tp`: Take profit percentage above the average entry price (default 0.45%).
`semifinal entry price percent`: Percentage drop from `maxPrice` to trigger the "semifinal" larger entry (default -12%).
`final entry price percent`: Percentage drop from `maxPrice` to trigger the "final" larger entry (default -15%).
Rounding & Display:
`roundprice`, `round`: Decimal places for rounding price and quantity calculations.
`texts`, `label_style`: User interface preferences for text size and label appearance on the chart.
Time Filter:
`startTime`, `endTime`: Defines the date range for the backtest.
3. Calculations & Grid Setup:
`maxPrice`: The highest price point for the grid setup. Calculated as the lowest low of the previous `len` bars only if no trades are open. If trades are open, it uses the entry price of the very first order placed in the current sequence (`strategy.opentrades.entry_price(0)`).
`minPrice`: The lowest price point for the grid, calculated based on `maxPrice` and `range1`.
`totalCapital`: The amount of capital (considering leverage and `per1`) allocated for the main grid orders.
`coinRatios`: An array ` `. This defines the *relative* size ratio for each of the 11 grid orders. Later orders (at lower prices) will be progressively larger.
`totalRatio`: The sum of all ratios (66).
`positionSizes`: An array calculated based on `totalCapital` and `coinRatios`. It determines the actual quantity (size) for each of the 11 grid orders.
4. Order Placement Logic (`strategy.entry`):
Initial Grid Orders:
Runs only if within the specified time range and no position is currently open (`strategy.opentrades == 0`).
A loop places 11 limit buy orders (`Buy 1` to `Buy 11`).
Prices are calculated linearly between `maxPrice` and `minPrice`.
Order sizes are taken from the `positionSizes` array.
Semifinal & Final Entries:
Two additional, larger limit buy orders are placed simultaneously with the grid orders:
`semifinal entry`: At `maxPrice * (1 - semifinal / 100)`. Size is based on `per2`% of the capital *not* used by the main grid (`1 - per1`).
`final entry`: At `maxPrice * (1 - final / 100)`. Size is based on the remaining capital (`1 - per2`% of the unused portion).
5. Visualization (`line.new`, `label.new`, `plot`, `plotshape`, `plotchar`):
Grid Lines & Labels:
When a position is open (`strategy.opentrades > 0`), horizontal lines and labels are drawn for each of the 11 grid order prices and the "final" entry price.
Lines extend from the bar where the *first* entry occurred.
Labels show the price and planned size for each level.
Dynamic Coloring: If the price drops below a grid level, the corresponding line turns green, and the label color changes, visually indicating that the level has been reached or filled.
Plotted Lines:
`maxPrice` (initial high point for the grid).
`strategy.position_avg_price` (current average entry price of the open position, shown in red).
Target Profit Price (`strategy.position_avg_price * (1 + tp / 100)`, shown in green).
Markers:
A flag marks the `startTime`.
A rocket icon (`🚀`) appears below the bar where the `final entry` triggers.
A stop icon (`🛑`) appears below the bar where the `semifinal entry` triggers.
6. Exit Logic (`strategy.exit`, `strategy.entry` with `qty=0`):
Main Take Profit (`Full Exit`):
Uses `strategy.entry('Full Exit', strategy.short, qty = 0, limit = target2)`. This places a limit order to close the entire position (`qty=0`) at the calculated take profit level (`target2 = avgPrice * (1 + tp / 100)`). Note: Using `strategy.entry` with `strategy.short` and `qty=0` is a way to close a long position, though `strategy.exit` is often clearer. This exit seems intended to apply whenever any part of the grid position is open.
First Order Trailing Stop (`1st order Full Exit`):
Conditional: Only active if `trail` input is true AND the *last* order filled was "Buy 1" (meaning only the very first grid level was entered).
Uses `strategy.exit` with `trail_points` and `trail_offset` based on ATR values to implement a trailing stop loss/profit mechanism for this specific scenario.
This trailing stop order is cancelled (`strategy.cancel`) if any subsequent grid orders ("Buy 2", etc.) are filled.
Final/Semifinal Take Profit (`final Full Exit`):
Conditional: Only active if more than 11 entries have occurred (meaning either the "semifinal" or "final" entry must have triggered).
Uses `strategy.exit` to place a limit order to close the entire position at the take profit level (`target3 = avgPrice * (1 + tp / 100)`).
7. Information Display (Tables & UI Label):
`statsTable` (Top Right):
A comprehensive table displaying grouped information:
Market Info (Entry Point, Current Price)
Position Info (Avg Price, Target Price, Unrealized PNL $, Unrealized PNL %, Position Size, Position Value)
Strategy Performance (Realized PNL $, Realized PNL %, Initial/Total Balance, MDD, APY, Daily Profit %)
Trade Statistics (Trade Count, Wins/Losses, Win Rate, Cumulative Profit)
`buyAvgTable` (Bottom Left):
* Shows the *theoretical* entry price and average position price if trades were filled sequentially up to each `buy` level (buy1 to buy10). It uses hardcoded percentage drops (`buyper`, `avgper`) based on the initial `maxPrice` and `coinRatios`, not the dynamically changing actual average price.
`uiLabel` (Floating Label on Last Bar):
Updates only on the most recent bar (`barstate.islast`).
Provides real-time context when a position is open: Size, Avg Price, Current Price, Open PNL ($ and %), estimated % drop needed for the *next* theoretical buy (based on `ui_gridStep` input), % rise needed to hit TP, and estimated USDT profit at TP.
Shows "No Position" and basic balance/trade info otherwise.
In Summary:
This is a sophisticated long-only grid trading strategy. It aims to:
1. Define an entry range based on recent lows (`maxPrice`).
2. Place 11 scaled-in limit buy orders within a percentage range below `maxPrice`.
3. Place two additional, larger buy orders at deeper percentage drops (`semifinal`, `final`).
4. Calculate the average entry price as orders fill.
5. Exit the entire position for a small take profit (`tp`) above the average entry price.
6. Offer a conditional ATR trailing stop if only the first order fills.
7. Provide extensive visual feedback through lines, labels, icons, and detailed information tables/UI elements.
Keep in mind that grid strategies can perform well in ranging or slowly trending markets but can incur significant drawdowns if the price trends strongly against the position without sufficient retracements to hit the take profit. The leverage (`lev`) input significantly amplifies both potential profits and losses.
Mean
Consecutive Bearish Candle Strategy█ STRATEGY DESCRIPTION
The "Consecutive Bearish Candle Strategy" is a momentum-based strategy designed to identify potential reversals after a sustained bearish move. It enters a long position when a specific number of consecutive bearish candles occur and exits when the price shows strength by exceeding the previous bar's high. This strategy is optimized for use on various timeframes and instruments.
█ SIGNAL GENERATION
1. LONG ENTRY
A Buy Signal is triggered when:
The close price has been lower than the previous close for at least `Lookback` consecutive bars. This indicates a sustained bearish move, suggesting a potential reversal.
The signal occurs within the specified time window (between `Start Time` and `End Time`).
2. EXIT CONDITION
A Sell Signal is generated when the current closing price exceeds the high of the previous bar (`close > high `). This indicates that the price has shown strength, potentially confirming the reversal and prompting the strategy to exit the position.
█ ADDITIONAL SETTINGS
Lookback: The number of consecutive bearish bars required to trigger a Buy Signal. Default is 3.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for markets with frequent momentum shifts.
It performs best in volatile conditions where price movements are significant.
Backtesting results should be analysed to optimize the `Lookback` parameter for specific instruments.
4 Bar Momentum Reversal strategy█ STRATEGY DESCRIPTION
The "4 Bar Momentum Reversal Strategy" is a mean-reversion strategy designed to identify price reversals following a sustained downward move. It enters a long position when a reversal condition is met and exits when the price shows strength by exceeding the previous bar's high. This strategy is optimized for indices and stocks on the daily timeframe.
█ WHAT IS THE REFERENCE CLOSE?
The Reference Close is the closing price from X bars ago, where X is determined by the Lookback period. Think of it as a moving benchmark that helps the strategy assess whether prices are trending upwards or downwards relative to past performance. For example, if the Lookback is set to 4, the Reference Close is the closing price 4 bars ago (`close `).
█ SIGNAL GENERATION
1. LONG ENTRY
A Buy Signal is triggered when:
The close price has been lower than the Reference Close for at least `Buy Threshold` consecutive bars. This indicates a sustained downward move, suggesting a potential reversal.
The signal occurs within the specified time window (between `Start Time` and `End Time`).
2. EXIT CONDITION
A Sell Signal is generated when the current closing price exceeds the high of the previous bar (`close > high `). This indicates that the price has shown strength, potentially confirming the reversal and prompting the strategy to exit the position.
█ ADDITIONAL SETTINGS
Buy Threshold: The number of consecutive bearish bars needed to trigger a Buy Signal. Default is 4.
Lookback: The number of bars ago used to calculate the Reference Close. Default is 4.
Start Time and End Time: The time window during which the strategy is allowed to execute trades.
█ PERFORMANCE OVERVIEW
This strategy is designed for trending markets with frequent reversals.
It performs best in volatile conditions where price movements are significant.
Backtesting results should be analysed to optimize the Buy Threshold and Lookback parameters for specific instruments.
Strength Volatility Killer - The Quant ScienceStrength Volatility Killer - The Quant Science™ is based on a special version of RSI (Relative Strength Index), created with the simple average and standard deviation.
DESCRIPTION
The algorithm analyses the market and opens positions following three different volatility entry conditions. Each entry has a specific and personal exit condition. The user can setting trailing stop loss from user interface.
USER INTERFACE SETTING
Configures the algorithm from the user interface.
AUTO TRADING COMPLIANT
With the user interface, the trader can easily set up this algorithm for automatic trading.
BACKTESTING INCLUDED
The trader can adjust the backtesting period of the strategy before putting it live. Analyze large periods such as years or months or focus on short-term periods.
NO LIMIT TIMEFRAME
This algorithm can be used on all timeframes.
GENERAL FEATURES
Multi-strategy: the algorithm can apply long strategy or short strategy.
Built-in alerts: the algorithm contains alerts that can be customized from the user interface.
Integrated indicator: indicator is included.
Backtesting included: quickly automatic backtesting of the strategy.
Auto-trading compliant: functions for auto trading are included.
ABOUT BACKTESTING
Backtesting refers to the period 13 June 2022 - today, ticker: AVAX/USDT, timeframe 5 minutes.
Initial capital: $1000.00
Commission per trade: 0.03%
Mean Reverse Grid Algorithm - The Quant ScienceMean Reverse Grid Algorithm - The Quant Science™ is a dynamic grid algorithm that follows the trend and run a mean reverting strategy on average percentage yield variation.
DESCRIPTION
Trades on different price levels of the grid, following the trend. The grid consists of 10 levels, 5 higher and 5 lower. The grids together create a channel, this channel represents the total percentage change where the algorithm works. The channel also represents the average change yields of the asset, identified during analysis with the "Yield Trend Indicator".
The algorithm can be set long or short.
1. Long algorithm: opens long positions with 20% of the capital every time the price crossunder a lower grid, for a maximum total of 5 simultaneous trades. Trades are closed each time the price crossover a higher grid.
2. Short algorithm: opens short positions with 20% of the capital every time the price crossover a higher grid, for a maximum total of 5 simultaneous trades. Trades are closed each time the price crossunder a lower grid.
USER INTERFACE SETTING
The user configures the percentage value of each grid from the user interface.
AUTO TRADING COMPLIANT
With the user interface, the trader can easily set up this algorithm for automatic trading. Automating it is very simple, activate the alert functions and enter the links generated by your broker.
BACKTESTING INCLUDED
With the user interface, the trader can adjust the backtesting period of the strategy before putting it live. You can analyze large periods such as years or months or focus on short-term periods.
NO LIMIT TIMEFRAME
This algorithm can be used on all timeframes and is ideal for lower timeframes.
GENERAL FEATURES
Multi-strategy: the algorithm can apply either the long strategy or the short strategy.
Built-in alerts: the algorithm contains alerts that can be customized from the user interface.
Integrated grid: the grid indicator is included.
Backtesting included: automatic backtesting of the strategy is generated based on the values set.
Auto-trading compliant: functions for auto trading are included.
ABOUT BACKTESTING
Backtesting refers to the period 1 August 2022 - today, ticker: ETH/USDT, timeframe 1H.
Initial capital: $1000.00
Commission per trade: 0.03%
ETF 3-Day Reversion StrategyIntroduction: This strategy is a modification of the “3-day Mean Reversion Strategy” from the book "High Probability ETF Trading" by Larry Connors and Cesar Alvarez. In the book, the authors discuss a high-probability ETF mean reversion strategy for a 1-day time-frame with these simple rules:
The price must be above the 200 day SMA and below the 5 day SMA.
The low of today must be lower than the low of yesterday (must be true for 3 consecutive days)
The high of today must be lower than the high of yesterday (must be true for 3 consecutive days)
If the 3 rules above are true, then buy on the close of the current day.
Exit when the closing price crosses above the 5 day SMA.
In practice and in backtesting, I’ve found that the strategy consistently works better when using an EMA for the trend-line instead of an SMA. So, this script uses an EMA for the trend-line. I’ve also made the length of the exit EMA adjustable.
How it works:
The Strategy will buy when the buy conditions above are true. The strategy will sell when the closing price crosses over the Exit Moving Average
Plots:
Green line = Exit Moving Average (Default 5 Day EMA)
Blue line = 5 Day EMA (Used as Entry Criteria)
Disclaimer: Open-source scripts I publish in the community are largely meant to spark ideas that can be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
[cache_that_pass] 1m 15m Function - Weighted Standard DeviationTradingview Community,
As I progress through my journey, I have come to the realization that it is time to give back. This script isn't a life changer, but it has the building blocks for a motivated individual to optimize the parameters and have a production script ready to go.
Credit for the indicator is due to @rumpypumpydumpy
I adapted this indicator to a strategy for crypto markets. 15 minute time frame has worked best for me.
It is a standard deviation script that has 3 important user configured parameters. These 3 things are what the end user should tweak for optimum returns. They are....
1) Lookback Length - I have had luck with it set to 20, but any value from 1-1000 it will accept.
2) stopPer - Stop Loss percentage of each trade
3) takePer - Take Profit percentage of each trade
2 and 3 above are where you will see significant changes in returns by altering them and trying different percentages. An experienced pinescript programmer can take this and build on it even more. If you do, I ask that you please share the script with the community in an open-source fashion.
It also already accounts for the commission percentage of 0.075% that Binance.US uses for people who pay fees with BNB.
How it works...
It calculates a weighted standard deviation of the price for the lookback period set (so 20 candles is default). It recalculates each time a new candle is printed. It trades when price lows crossunder the bottom of that deviation channel, and sells when price highs crossover the top of that deviation channel. It works best in mid to long term sideways channels / Wyckoff accumulation periods.
Hophop Reversion Strategy
█ OVERVIEW
Mean reversion is a financial term assuming that an asset's price will tend to converge to the average price over time.
Due to the trending nature of the crypto markets, mean reversion on a high timeframe could be pretty dangerous. When it comes to running mean reversion strategy on low timeframe, commission and slippage may cost more than strategy gains.
In this strategy, I tried to achieve being conservative in the trending market while avoiding trades if necessary and trading high probability reversion opportunities .
█ CONCEPTS
Strategy is build based on the combination of the momentum and the historical / implied volatility; when the price exceeds the potential volatility range, the strategy places the orders, and the target point is the mean of the expected range high and range low.
The range low and high lines displayed on the chart shows where to short or long, to make sure that the orders are limit orders; orders are placed 0.5% above/below the ranges!
Key information about the strategy
• All the orders are limit entry
• 0.02% commission is included in the backtest
• 30 ticks set for Verify Price Limit for Orders
• 30 ticks set for Slippage
• Initial version does not include the money management and hard stops hence you need to be extra cautious in trending markets
• Restricted to be used for BTC and ETH for 15 min timeframe
█ Ozet
Ortalamaya dönme, bir varlığın fiyatının zaman içinde ortalama fiyata yakınsama eğiliminde olacağını varsayan bir finansal terimdir.
Kripto piyasalarının trend egilimli doğası nedeniyle, yüksek zaman diliminde ortalamaya dönüş oldukça tehlikeli olabilir.
Ortalama geri dönüş stratejisini düşük zaman diliminde calistirmak söz konusu olduğunda, komisyon ve kayma, strateji kazanımlarından daha pahalıya mal olabilir.
Bu stratejide, gerektiğinde alım satımlardan kaçınırken ve yüksek olasılıklı ortalamaya dönüş fırsatlarını degerlendiren, trend olan piyasada ise isleme girerken temkinli olmasi uzerine calistim
█ Aciklama
Strateji, momentum ve tarihsel / zımni oynaklığın birleşimine dayalı olarak inşa edilmistir; fiyat potansiyel oynaklık aralığını aştığında, strateji emirleri verir ve hedef nokta, beklenen yüksek aralığın ve düşük aralığın ortalamasıdır.
Grafikte görüntülenen aralık alt ve üst satırları,
Stratejiye ait onemli bilgiler/b]
• Tüm emirler limit emirdir girişlidir
• Backtest performansinda %0.02 komisyon dahildir
• Limit Emir fiyat dogrulamasi icin 30 tick bekleme kullanilmistir
• Slippage için 30 tick bekleme kullanilmistir
• İlk sürüm para yönetimini ve stoploss içermez, bu nedenle trend olan piyasalarda ekstra dikkatli olmanız gerekir.
• 15 dakikalık zaman dilimi ile BTC ve ETH için kullanımla sınırlıdır
Emirlerin limit emir olduğundan emin olmak için nerede short veya long isleme girilecegini gosteren cizgilerin %0.5 üstünde/altında verilir!
Jaws Mean Reversion [Strategy]This very simple strategy is an implementation of PJ Sutherlands' Jaws Mean reversion algorithm. It simply buys when a small moving average period (e.g. 2) is below
a longer moving average period (e.g. 5) by a certain percentage and closes when the small period average crosses over the longer moving average.
If you are going to use this, you may wish to apply this to a range of investment assets using a screener for setups, as the amount signals are low. Alternatively, you may wish to tweak the settings to provide more signals.
Context can be found here:
LINK
Mean Reversion w/ Bollinger BandsThis is a more advanced version of my original mean reversion script.
It employs the famous Bollinger Bands.
This robot will buy when price falls below the lower Bollinger Band, and sell when price moves above the upper Bollinger Band.
I've only tested it on the S&P 500, though you could try it out on other assets to see the backtest performance.
During the recent COVID-19 bear market drop, it produced several buy signals on the S&P which I followed, and made some nice gains so far.
I still think this would make a better investing strategy (buy undervalued / sell over-valued), rather than a trading strategy.
I use this robot for my long term portfolio.
YJ Mean ReversionMean reversion strategy, based upon the price deviation (%) from a chosen moving average (bars). Do note that the "gains" are always relative to your starting capital, so if you set a smaller starting capital (e.g. $10000) your gains will look bigger. Also when the strategy tester has finished calculating, check the "Open P/L", as there could still be open trades.
Some Tips:
- Was designed firstly to work on an index like the S&P 500 , which over time tends to go up in value.
- Avoid trading too frequently (e.g. Daily, Weekly), to avoid getting eaten by fees.
- If you change the underlying asset, or time frame, tweaking the moving average may be necessary.
- Can work with a starting capital of just $1000, optimise the settings as necessary.
- Accepts floating point values for the amount of units to purchase (e.g. Bitcoin ).
- If price of units exceeds available capital, script will cancel the buy.
- Adjusted the input parameters to be more intuitive.
MCI and VCI - Modified CCI FormulasFor private peeps only
- Takes a modified version of the CCI formula into 2 parts
VCI - Volume Channel Index (Yellow Histogram)
- Measures accurate accumulation and distribution levels and times
MCI - Modified Channel Index
- Measures (when compared to VCI) levels where clearly buys are interested vs not interested.
Example:
If VCI > MCI
- Shows buyer's are more than interested in buying, you've either hit a bottom or heavy resistance
if MCI > VCI
- Show's buyer's aren't interested and will most likely result in a dump/lower price
Great for monitoring accumulation and distribution, these auto buy and sells look for the transition points over 0, works on EVERY commodity/stock/FOREX/Crypto
Results are from trading 1 BTC x25 leveraging. Not all trades will get in if put in at limit, but it does survive with profits after the massive 0.075 fee (results shown are after fees)
Nick's Momentum Trading Strategy - Beats Buy and Hold manifolds!This script works on the principle of short-term mean reversion and long term trend following, and uses minimal parameters to ensure no overfitting.
The scripts beats buy and hold for almost all major pairs that satisfy the following conditions:
- are trading on multiple exchanges as either ALTBTC or ALTUSD pairs
- have good volume available with them
- have an established history - (try not to use this script with really new pairs)
To run the script, follow these rules:
- script should be run on ALTBTC pair
- set first parameter such that when multiplied with resolution of chart, we get a whole day/week/month, etc. e.g. for a '4H' chart, set this to multiples of 6. The reason being that most algorithmic rebalancing in cryptosphere happens at these times, and we want to make effective use of this.
- set second parameter in the range of 1-6 - this is the smoothing factor (ema) we want to apply to our indicator (governed by our first parameter) - more smoothing = lesser trades. See what works for you.
- Last parameter is a filter condition. Just check/uncheck it once to see if market works better with this on or off.
- If script does not beat buy and hold on this pair (rarely), don't use script on this pair at all.
Access available only to friends. I do code trading strategies on request - so, let me know if you have a good set of rules to create a strategy.
Moving Average Mean Reversion StrategyA basic mean-reversion strategy. Shorts when the close is 10% above the MA, and goes long when it's 10% below the MA.
EMA, SMA Mean ReversionInvite Only: But everyone will be accepted
Invite-only in order to understand demand and interest in this type of strategy. All requests are welcome and will be accepted.
Full Source is available
A blog post containing full source code and commentary of the strategy is available on the backtest-rookies website. To comply with house rules, I cannot post a direct link here. (Hint add ".com" to backtest-rookies)
Overview
The strategy uses two moving averages to represent the historical mean and a slightly smoothed version of the current price action. It will place long or short trades when the fast EMA moves far away from the historical mean (the slow SMA).
Features
Set Backtest Date ranges
Enter when EMA is x% away from the mean
Independent inputs for the long and short side
Only enter when the EMA has started to reverse. (Input)
Optional Stop loss
Limit trades to a single direction to "Buy the dip" or "Sell the top"
