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Algorithmic Trading vs AI Trading1. Definition and Core Concepts Algorithmic Trading (Algo Trading): Algorithmic trading refers to the use of predefined, rule-based computer programs that execute trading orders based on quantitative criteria such as price, volume, time, and other market parameters. The algorithms are explicitly programmed to follow certain logic—for instance, “buy 100 shares of stock X if its price drops by 2% within an hour.” Key characteristics of algorithmic trading: Rule-based: Every instruction is manually coded and deterministic. Speed and efficiency: Algorithms can execute trades in milliseconds, far faster than human capability. Backtesting: Traders can test strategies against historical data to optimize performance. Risk reduction: Algorithms reduce the influence of human emotions such as fear and greed. Common algorithmic trading strategies include: Trend-following strategies: Buying or selling assets based on moving averages or momentum. Arbitrage strategies: Exploiting price differences between markets or assets. Market-making strategies: Placing simultaneous buy and sell orders to capture spreads. Mean reversion strategies: Assuming that prices will revert to their historical average. AI Trading (Artificial Intelligence Trading): AI trading, on the other hand, involves the use of machine learning, deep learning, natural language processing (NLP), and other AI techniques to identify trading opportunities, make predictions, and adapt strategies over time. Unlike traditional algorithms, AI trading systems are capable of learning from data, discovering patterns that may not be apparent to humans, and adjusting their behavior autonomously. Key characteristics of AI trading: Adaptive learning: AI models improve over time by analyzing past trades and market data. Pattern recognition: Machine learning can detect complex, nonlinear relationships in data. Unstructured data analysis: AI can process news articles, social media, financial reports, and macroeconomic indicators to inform decisions. Predictive capabilities: AI models aim to forecast market trends, volatility, and asset price movements. Examples of AI trading techniques include: Reinforcement learning: AI agents learn to maximize returns by trial and error in a simulated market environment. Neural networks: Deep learning models capture intricate patterns in historical price data for predictive trading. Sentiment analysis: NLP algorithms gauge market sentiment from news, earnings calls, or social media. 2. Key Differences Feature Algorithmic Trading AI Trading Decision-making Rule-based, deterministic Data-driven, adaptive Flexibility Limited to predefined rules Learns and adapts to new data Data types Structured market data (prices, volumes) Structured + unstructured data (news, social media, alternative datasets) Learning ability No self-learning Machine learning enables continuous improvement Complexity Moderate to high (depends on strategy) High; often requires advanced ML/DL models Predictive power Based on statistical models, historical patterns Can predict trends, volatility, and market sentiment Human intervention Required to update rules Minimal; AI adapts autonomously Example use case High-frequency trading (HFT), arbitrage Portfolio optimization, predictive trading, sentiment-based strategies 3. Advantages and Limitations Algorithmic Trading Advantages: Speed: Executes trades in milliseconds, taking advantage of fleeting market inefficiencies. Consistency: Removes emotional biases in trading. Transparency: Traders know exactly what rules are being followed. Backtesting efficiency: Easy to test strategies against historical data. Algorithmic Trading Limitations: Rigidity: Cannot adapt to new market conditions unless manually updated. Limited data utilization: Cannot process unstructured data like news or social media sentiment. Predictive limitations: Works well in stable, rule-following markets but struggles in highly volatile or unprecedented conditions. AI Trading Advantages: Adaptive and intelligent: Learns from evolving market conditions. Handles complex data: Capable of integrating multiple data sources for trading decisions. Predictive capability: Can forecast price movements and volatility. Potential for higher alpha: Sophisticated AI models can uncover hidden trading opportunities. AI Trading Limitations: Complexity and cost: Requires advanced computational resources and expertise in data science. Transparency issues: Deep learning models are often “black boxes,” making decision rationale unclear. Overfitting risk: AI models may perform well on historical data but fail in live markets if not properly validated. Data dependency: Quality and quantity of data directly affect performance. 4. Applications in Financial Markets Algorithmic Trading Applications: High-Frequency Trading (HFT): Buying and selling within milliseconds to profit from tiny price discrepancies. Institutional Trading: Execution of large orders while minimizing market impact. Arbitrage and statistical strategies: Capitalizing on predictable price differences across assets and markets. AI Trading Applications: Predictive analytics: Forecasting stock prices, forex trends, or commodity movements. Sentiment-driven trading: Using news and social media data to guide buy/sell decisions. Portfolio optimization: AI models help balance risk and returns in investment portfolios. Algorithmic strategy enhancement: AI can optimize existing algorithms by fine-tuning parameters based on real-time learning. 5. Future Outlook The evolution from algorithmic trading to AI trading reflects the broader trend in finance toward data-driven, intelligent decision-making. While algorithmic trading continues to dominate areas like high-frequency execution and market-making, AI trading is gaining traction for predictive analytics, adaptive strategies, and processing alternative data sources. Hybrid approaches are also emerging, where AI augments traditional algorithmic strategies. For instance, an algorithmic trading system may follow predefined rules but uses AI to dynamically adjust parameters based on market conditions, enhancing performance without sacrificing the reliability of deterministic logic. As AI technologies—such as reinforcement learning, transformer models, and multi-agent simulations—become more sophisticated, AI trading is expected to move from experimental use cases to mainstream adoption, potentially reshaping investment management, hedge fund strategies, and even retail trading. 6. Conclusion In summary, algorithmic trading is a rule-based, deterministic approach relying on speed and predefined strategies, ideal for stable, quantifiable market conditions. AI trading, in contrast, is adaptive, data-driven, and capable of learning and evolving over time, providing predictive power and the ability to analyze complex, unstructured datasets. Both have unique advantages and limitations, and the future of trading is likely to see a convergence where AI enhances algorithmic strategies, creating smarter, faster, and more resilient financial systems. Understanding these differences is crucial for traders, investors, and financial technologists who aim to leverage modern technology for sustainable market advantage. While algorithms execute with precision, AI brings intelligence to execution, marking the next frontier in financial innovation.
NSE:AXISBANKEducation
by TechnicalExpress
Intraday Trading vs. Swing TradingIntroduction Trading styles define how a trader interacts with the market—time horizon, risk appetite, capital usage, psychology, and even lifestyle. Among all styles, intraday trading and swing trading are the two most popular for active traders, especially in equity, derivatives, forex, and crypto markets. While both aim to profit from price movements, they differ sharply in time frame, strategy, stress level, and skill requirements. Choosing the right one is less about returns and more about who you are as a trader. 1. Intraday Trading: Overview Intraday trading involves buying and selling financial instruments within the same trading day. All positions are squared off before the market closes, eliminating overnight risk. Key Characteristics Holding period: Minutes to hours Positions: Open and closed within the same day Leverage: High (especially in derivatives) Frequency: Multiple trades per day Objective: Capture small price movements Instruments Commonly Traded Index futures & options (Nifty, Bank Nifty) Highly liquid stocks Forex pairs Cryptocurrencies (24×7 markets) 2. Swing Trading: Overview Swing trading aims to capture medium-term price “swings” over several days to weeks. Traders hold positions overnight and sometimes through volatile sessions. Key Characteristics Holding period: 2 days to several weeks Positions: Carried overnight Leverage: Low to moderate Frequency: Few trades per month Objective: Capture trend segments Instruments Commonly Traded Stocks (cash market) Futures (with hedging) ETFs Crypto & commodities 3. Time Frame and Market Engagement Intraday Trading Requires constant screen time Most active during: Market open (first 60–90 minutes) Major news events High-volume periods Traders must react instantly to price action Swing Trading Less screen dependency Analysis typically done: After market hours On weekends Execution may take only a few minutes per day Bottom line: Intraday trading is time-intensive. Swing trading is time-efficient. 4. Risk Profile and Volatility Exposure Intraday Trading Risks Sudden spikes and fake breakouts Slippage during high volatility Overtrading Emotional decision-making Brokerage & transaction costs However, intraday traders avoid: Overnight gap risk Unexpected global events while holding positions Swing Trading Risks Overnight gaps due to: Earnings announcements Global cues Geopolitical events Wider stop losses Longer drawdown periods Risk difference: Intraday risk is intense but short-lived. Swing trading risk is slower but persistent. 5. Capital Requirements and Cost Structure Intraday Trading Lower capital due to leverage Higher costs because of: Frequent trades Brokerage, STT, exchange fees Profitability depends heavily on cost control Swing Trading Higher capital preferred Lower transaction costs Better reward-to-risk ratios over time Important insight: Many intraday traders are profitable before costs but lose after expenses. Swing traders are less affected by this trap. 6. Strategy and Technical Approach Intraday Trading Strategies Scalping VWAP trading Opening range breakout Momentum trading Option gamma plays Indicators used: VWAP RSI (short period) EMA (5, 9, 20) Volume profile Order flow Swing Trading Strategies Trend following Pullback entries Breakout retests Mean reversion Sector rotation Indicators used: Daily & weekly moving averages MACD RSI (14-period) Support & resistance Fibonacci retracements 7. Psychological Demands Intraday Trading Psychology High stress Quick decision-making Requires emotional detachment Prone to revenge trading Mental fatigue is common Swing Trading Psychology Requires patience Comfort with open P&L swings Discipline to hold winners Less emotional noise Reality check: Most traders fail in intraday trading due to psychological overload, not lack of strategy. 8. Lifestyle Compatibility Intraday Trading Suits: Full-time traders People who enjoy fast decision cycles Those who thrive under pressure Traders with disciplined routines Swing Trading Suits: Working professionals Business owners Part-time traders People who value flexibility 9. Profit Potential and Consistency Intraday Trading Potential for daily income Compounding possible High variance in results Small mistakes can erase weeks of gains Swing Trading Slower but steadier growth Larger profits per trade Easier to maintain consistency Better for long-term capital growth Key truth: Consistency is easier in swing trading than intraday trading. 10. Which One Should You Choose? Ask yourself these questions: Can I sit in front of the screen for hours daily? Can I handle rapid losses without emotional reactions? Do I prefer fast action or structured planning? Is trading my primary income source? Choose Intraday Trading if: You can give full-time attention You have strict discipline You enjoy short-term action You accept higher stress Choose Swing Trading if: You want work-life balance You prefer analytical planning You are building capital steadily You want lower psychological pressure Conclusion Intraday trading and swing trading are not “better” or “worse”—they are different tools for different personalities. Intraday trading rewards speed, focus, and emotional control Swing trading rewards patience, structure, and consistency Most successful traders eventually migrate toward swing trading as their capital and experience grow, while a small elite excels in intraday trading through strict discipline and process-driven execution. The best approach is not choosing the most exciting style—but the one you can execute flawlessly, repeatedly, and calmly.
NSE:TATASTEELEducation
by TechnicalExpress
Algo, Quant & Data-Driven Trading1. What is Algorithmic Trading? Algorithmic trading (algo trading) is the execution of trades automatically using pre-defined rules or instructions coded into a computer system. These rules may involve price, time, volume, technical indicators, or market conditions. Key Characteristics of Algo Trading Rule-Based Execution You define a rule — for example: “Buy Nifty futures when RSI crosses below 30 and reverses above 35.” Once coded, the algorithm runs these rules without emotional interference. Speed & Efficiency Computers can analyze market data and execute orders in milliseconds — far faster than any human. Backtesting Before Deployment Algos can be tested on past market data to evaluate: Returns Drawdowns Win/loss ratios Risk exposures Reduced Human Error Since execution is automated, biases like fear, greed, hesitation, revenge trading, and overtrading are minimized. Common Algo Trading Strategies Trend Following Algorithms (moving averages, breakout systems) Mean Reversion Models (RSI, Bollinger Band reversals) Arbitrage Algorithms (cash–futures arbitrage, index arbitrage) Scalping Bots (ultra-short-term trades) Execution Algos (VWAP, TWAP, POV for institutions) Who Uses Algo Trading? Hedge funds Prop trading firms Banks HNIs and retail traders using API platforms (Zerodha, Dhan, Fyers, etc.) Market makers Algo trading is mainly about automating the process and ensuring executions happen as planned. 2. What is Quantitative Trading? Quantitative trading (quant trading) goes deeper than algos. It uses mathematics, statistics, econometrics, probability models, and programming to design trading strategies. While algo trading focuses on execution, quant trading focuses on research, model building, and predictive analytics. Features of Quant Trading Data-Driven Strategy Design Quants use large datasets — sometimes decades of tick-by-tick data — to identify patterns. Mathematical Models Models include: Time-series analysis Stochastic calculus Machine learning Factor modelling Risk modelling Monte-Carlo simulations Systematic and Scientific Approach Strategies are created, tested, validated statistically, and deployed based on mathematical confidence. Large Data Sets Quants analyze: Price, volume, and order book data Options Greeks Fundamental indicators Macroeconomic data Alternative data (web traffic, satellite images, social media sentiment) Common Quant Strategies Statistical Arbitrage Pairs trading, cointegration models, mean reversion baskets. Factor-Based Investing Value, growth, quality, momentum, volatility factors. Volatility Trading Options models, volatility surface analysis, VIX-based strategies. Machine Learning Models Classification and regression to predict direction, volatility, or regime changes. Optimization Algorithms Portfolio optimization using Markowitz, Black-Litterman, risk parity. Quant Roles Quant trading involves teams such as: Quant researchers Quant developers Data scientists Risk modelers Execution quants In short, quant trading is the brain, and algo trading is the hands that execute. 3. What is Data-Driven Trading? While algo and quant trading use predefined models, data-driven trading takes the concept further by integrating: Big data Machine learning Artificial intelligence (AI) Alternative datasets Predictive analytics Here, the goal is to let data reveal patterns rather than humans designing them manually. Key Inputs in Data-Driven Trading Market Data — price, order book, volume, volatility Fundamental Data — PE, EPS, ROE, balance sheet patterns News & Sentiment Data — sentiment analysis using NLP Alternative Data Social media Satellite images (crop yield, shipping) Google searches E-commerce traffic Geo-location data Machine Learning Methods Used Regression models Random Forests Gradient Boosting Neural networks Deep learning (LSTM for time-series) Reinforcement learning Why Data-Driven Trading Works Markets are becoming increasingly complex, influenced by: Liquidity flows Global macro events Corporate actions Social media reactions Humans cannot process all this in real time — but machines can. 4. How Algo, Quant & Data-Driven Trading Fit Together These three approaches are interconnected: Quant Trading = Strategy Brain Mathematical research, data analysis, and model creation. Algo Trading = Strategy Execution Engine Automates orders, reduces cost and slippage, ensures consistency. Data-Driven Trading = Modern Enhancement Layer Adds data intelligence and predictive power through AI and big data. Together they form a cycle: Data → Quant Research → Model → Backtest → Algo Code → Deployment → Live Trading → Feedback Loop This feedback loop ensures improvement and adaptation to market conditions. 5. Tools Used in Algo, Quant & Data-Driven Trading Programming Languages Python (most popular) R C++ (for HFT) Java MATLAB Libraries & Frameworks NumPy, Pandas, Scikit-learn TensorFlow, PyTorch Statsmodels Backtrader, Zipline QuantLib Trading APIs Zerodha Kite API Dhan API Interactive Brokers Alpaca Binance API Data Platforms NSE/BSE feeds Bloomberg Reuters Tick-by-tick data vendors 6. Advantages of Modern Trading Techniques Emotion-free trading Decisions are consistent at all times. Backtest + forward test validation Reduces guesswork and improves confidence. Scalability A strategy that works on one index can be replicated across markets. High-speed execution Essential for intraday, scalping, arbitrage. Better risk management Stop loss, position sizing, hedging, volatility filters can be coded in directly. Discovery of new patterns AI can find signals humans never notice. 7. Risks & Challenges Overfitting A model may perform excellently in backtest but fail in live markets. Data Quality Issues Incomplete or noisy data produces bad strategies. Black-Box Models AI predictions may not explain why a trade is taken. Latency & Slippage Poor infrastructure can ruin otherwise good models. Regulatory Constraints SEBI in India requires compliance for automated execution. 8. The Future: AI-First Trading Markets will shift increasingly toward: Reinforcement-learning-based strategies Self-optimizing algorithms Real-time sentiment AI High-speed alternate data processing Human traders will transition from manually trading to supervising machines. Conclusion Algo, Quant, and Data-Driven trading represent the evolution of modern markets. Algo trading automates execution. Quant trading builds mathematically robust strategies. Data-driven trading enhances prediction using AI and big data. Together, they enable trading that is fast, intelligent, adaptive, and emotion-free. Whether you trade equities, derivatives, currencies, or global markets, these methods help you understand market behaviour through science rather than speculation.
NSE:HDFCBANKEducation
by TechnicalExpress
How Brokers, Market Makers & Algos Trigger Your Stop-Loss! Hello Traders! Ever felt like the market hits your stop-loss and then flies in your direction? You’re not alone. It’s not always a coincidence. Today, let’s decode how brokers, market makers, and algorithms hunt retail stop-losses and how you can protect yourself by trading smarter. The Hidden Game Behind Stop-Loss Hunting Liquidity Pools Below Swing Lows/Highs: Retail traders often place stop-losses near obvious support and resistance. Smart money knows this — they create a quick fake move to trigger these levels and grab liquidity. Algos Detect Retail Patterns: Algorithms scan chart structures, volume profiles, and order book imbalances. If too many stop orders sit below a zone, algos exploit it with a quick flush. Market Makers Need Orders: They profit from spreads and volume. By triggering stops, they fill larger institutional orders or create better entry zones for big players. How to Avoid Getting Trapped Avoid Obvious SL Placement → Don’t place stops right at swing low/high or support/resistance — give it a little buffer. Use Structure-Based Stops → Place SL where your trade idea is invalidated, not just where price might come. Wait for Confirmation, Not Impulse → Enter after a strong confirmation candle or retest. Don’t jump in just because price touches a zone. Watch for Liquidity Grabs → If price quickly breaks support and reverses — it’s likely a trap. Mark that level as a future opportunity zone. Rahul’s Tip “Algos aren’t evil — they’re just smarter. So be smarter too. Stop-loss hunting is real — but if you trade with structure and logic, they can’t touch you.” Conclusion The market isn’t always random. There are systems, patterns, and traps designed to shake out weak hands. Understanding how stop-loss hunting works can help you survive longer and trade smarter . Trade like a sniper, not like bait. Have you ever been stop-hunted? Share your story in the comments — let’s help each other grow! If you found this post valuable, don't forget to LIKE and FOLLOW ! I regularly share real-world trading setups, actionable strategies, and learning-focused content — all from real trading experience, not theory. Stay connected if you're serious about growing as a trader!
NSE:NIFTYEducation
by TraderRahulPal
2
How Algo Bots Target Retail SL – Learn to Beat Them!Hello Traders! Have you ever seen your stop-loss get hit by just a few points and then the market moves in the direction you expected? That’s not a coincidence — it’s often the work of Algo Bots and big players trying to trap retail traders . These bots are designed to trick traders by moving prices to hit SLs before starting the real move. Let’s understand how these bots work — and how you can avoid getting trapped. How Algos Hunt Retail Stop-Losses They Target Common SL Zones: Algo bots look for areas where many traders place their stop-loss — like just below support or above resistance. They Trick with Fake Breakouts: You may see a fast move above or below a level — but it’s just to hit SLs and then reverse. This is called a stop hunt . They React Fast: Bots can place thousands of trades in a second. They use their speed to catch traders off guard. How to Beat the Bots – Pro Tips Avoid Obvious SL Levels: Don’t keep SL right at support or resistance. Place it a little beyond where bots won’t expect it. Use Structure-Based SLs: Look at price structure and place SLs based on key swing highs/lows — not just round numbers. Wait for Confirmation: Don’t enter as soon as a level breaks. Wait for retest or a strong candle signal. Mark Smart Zones: Learn to spot liquidity areas and imbalance zones — that’s where big players usually trade after bots do their job. Rahul’s Tip The market isn’t cheating you — it’s just smarter. Learn how it works and you’ll trade with more confidence and better results. Conclusion Algo bots are fast and smart — but not unbeatable. If you place SLs wisely, trade with structure, and wait for confirmation, you’ll stop being trapped and start trading like the smart money. Has your SL ever been hunted like this? Let’s talk in the comments and help each other grow!
BITSTAMP:BTCUSDEducation
by TraderRahulPal
2
Manual Trading vs. Algo Trading: What’s the Future?Hello Traders! In today’s post, we’ll explore a hot topic in the trading world – Manual Trading vs. Algo Trading , and discuss the pros and cons of each. These two approaches to trading have been gaining popularity, but the question remains: which one is better, and what does the future hold for both? What is Manual Trading ? Manual trading is the traditional form of trading where the trader makes all the decisions. This includes identifying entry and exit points , using technical indicators , and analyzing the market to make informed decisions. Traders who use manual trading rely heavily on their experience , emotion , and intuition to decide when to buy or sell. What is Algo Trading ? On the other hand, Algo trading uses computer algorithms and pre-programmed instructions to execute trades. It’s based on a set of rules, such as price , volume , and time , to determine when a trade should be placed. This method eliminates human emotion, and trades are executed with precision and speed, often in milliseconds . Algo traders use advanced tools like artificial intelligence (AI) , machine learning , and big data to build smarter trading strategies. Pros of Manual Trading Human Element : Manual traders can rely on their intuition, experience, and emotions to make informed decisions. This helps them adjust to market nuances and situations that algorithms may miss. Flexibility : Manual traders have the ability to make on-the-spot decisions based on changing market conditions. Emotional Control : Although emotions can be a downside, a skilled manual trader knows how to manage emotions effectively, which allows them to make calculated decisions. Pros of Algo Trading Speed and Efficiency : Algo trading can process large amounts of data quickly, making trades in milliseconds. This can be advantageous in fast-moving markets. Reduced Emotional Bias : Since the algorithm follows strict rules, there’s no emotional interference, making the process more rational and systematic. Backtesting : With algo trading , traders can backtest strategies against historical data to see how the algorithm would have performed, helping to fine-tune strategies. 24/7 Trading : Algo trading can run continuously, taking advantage of global markets and never missing trading opportunities. Cons of Manual Trading Time-Consuming : Manual trading requires a lot of attention and focus, which can be mentally exhausting, especially during volatile markets. Emotional Impact : Emotions such as fear and greed can affect a trader’s decision-making process, leading to mistakes. Limited to Available Time : Traders are limited by time and must be physically present to execute trades. Cons of Algo Trading Technical Issues : Algorithms can fail or face technical glitches, leading to unexpected losses. Lack of Adaptability : Algorithms are designed to follow rules, which means they may not adapt well to unexpected market events or major news. Over-Optimization Risk : Over-optimized strategies may perform well in backtests but can fail in real market conditions. The Future of Trading As technology continues to advance, the future of trading will likely see more integration of AI , big data , and machine learning in both manual and algo trading . While algo trading will continue to dominate for its speed, efficiency, and ability to trade large volumes, manual trading still holds value for traders who rely on their judgment, intuition, and ability to adapt to rapidly changing market conditions. Conclusion: Manual trading and algo trading each have their unique advantages. If you’re someone who enjoys making quick decisions and analyzing the market based on real-time information, manual trading might be your best fit. However, if you prefer speed , automation , and trading without emotional bias, algo trading could be the way to go. What are your thoughts on Manual Trading vs. Algo Trading ? Share your experience and insights in the comments below! Let’s learn from each other!
NSE:NIFTYEducation
by TraderRahulPal
2
AJANTA PHARMA LTDBuy Ajanta above: 3351 only. All important points are marked. 𝐃𝐢𝐬𝐜𝐥𝐚𝐢𝐦𝐞𝐫: 𝐈𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 𝐢𝐧 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐢𝐞𝐬 𝐦𝐚𝐫𝐤𝐞𝐭 𝐚𝐫𝐞 𝐬𝐮𝐛𝐣𝐞𝐜𝐭 𝐭𝐨 𝐦𝐚𝐫𝐤𝐞𝐭 𝐫𝐢𝐬𝐤𝐬, 𝐫𝐞𝐚𝐝 𝐚𝐥𝐥 𝐭𝐡𝐞 𝐫𝐞𝐥𝐚𝐭𝐞𝐝 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐬 𝐜𝐚𝐫𝐞𝐟𝐮𝐥𝐥𝐲 𝐛𝐞𝐟𝐨𝐫𝐞 𝐢𝐧𝐯𝐞𝐬𝐭𝐢𝐧𝐠. 𝐒𝐭𝐨𝐜𝐤𝐬 𝐬𝐮𝐠𝐠𝐞𝐬𝐭𝐞𝐝 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐠𝐫𝐨𝐮𝐩 𝐚𝐫𝐞 𝐟𝐨𝐫 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧 𝐩𝐮𝐫𝐩𝐨𝐬𝐞. 𝐖𝐞 𝐝𝐨𝐧𝐭 𝐦𝐚𝐤𝐞 𝐚𝐧𝐲 𝐩𝐫𝐨𝐟𝐢𝐭𝐬 𝐟𝐫𝐨𝐦 𝐭𝐡𝐢𝐬 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐬𝐡𝐚𝐫𝐞𝐝 𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐥𝐲 𝐨𝐟 𝐟𝐫𝐞𝐞 𝐨𝐟 𝐜𝐨𝐬𝐭.
NSE:AJANTPHARMLong
by sreekumars
Exide IndustriesBuy above 515. All important points are marked. 𝐃𝐢𝐬𝐜𝐥𝐚𝐢𝐦𝐞𝐫: 𝐈𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 𝐢𝐧 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐢𝐞𝐬 𝐦𝐚𝐫𝐤𝐞𝐭 𝐚𝐫𝐞 𝐬𝐮𝐛𝐣𝐞𝐜𝐭 𝐭𝐨 𝐦𝐚𝐫𝐤𝐞𝐭 𝐫𝐢𝐬𝐤𝐬, 𝐫𝐞𝐚𝐝 𝐚𝐥𝐥 𝐭𝐡𝐞 𝐫𝐞𝐥𝐚𝐭𝐞𝐝 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐬 𝐜𝐚𝐫𝐞𝐟𝐮𝐥𝐥𝐲 𝐛𝐞𝐟𝐨𝐫𝐞 𝐢𝐧𝐯𝐞𝐬𝐭𝐢𝐧𝐠. 𝐒𝐭𝐨𝐜𝐤𝐬 𝐬𝐮𝐠𝐠𝐞𝐬𝐭𝐞𝐝 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐠𝐫𝐨𝐮𝐩 𝐚𝐫𝐞 𝐟𝐨𝐫 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧 𝐩𝐮𝐫𝐩𝐨𝐬𝐞. 𝐖𝐞 𝐝𝐨𝐧𝐭 𝐦𝐚𝐤𝐞 𝐚𝐧𝐲 𝐩𝐫𝐨𝐟𝐢𝐭𝐬 𝐟𝐫𝐨𝐦 𝐭𝐡𝐢𝐬 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐬𝐡𝐚𝐫𝐞𝐝 𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐥𝐲 𝐨𝐟 𝐟𝐫𝐞𝐞 𝐨𝐟 𝐜𝐨𝐬𝐭.
NSE:EXIDEINDLong
by sreekumars
HCL TechnologiesBreakout at 1830 will create new highs. Positive candle closing above 1830 will be a buy Target will be 1899. All important points are marked. 𝐃𝐢𝐬𝐜𝐥𝐚𝐢𝐦𝐞𝐫: 𝐈𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 𝐢𝐧 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐢𝐞𝐬 𝐦𝐚𝐫𝐤𝐞𝐭 𝐚𝐫𝐞 𝐬𝐮𝐛𝐣𝐞𝐜𝐭 𝐭𝐨 𝐦𝐚𝐫𝐤𝐞𝐭 𝐫𝐢𝐬𝐤𝐬, 𝐫𝐞𝐚𝐝 𝐚𝐥𝐥 𝐭𝐡𝐞 𝐫𝐞𝐥𝐚𝐭𝐞𝐝 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐬 𝐜𝐚𝐫𝐞𝐟𝐮𝐥𝐥𝐲 𝐛𝐞𝐟𝐨𝐫𝐞 𝐢𝐧𝐯𝐞𝐬𝐭𝐢𝐧𝐠. 𝐒𝐭𝐨𝐜𝐤𝐬 𝐬𝐮𝐠𝐠𝐞𝐬𝐭𝐞𝐝 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐠𝐫𝐨𝐮𝐩 𝐚𝐫𝐞 𝐟𝐨𝐫 𝐞𝐝𝐮𝐜𝐚𝐭𝐢𝐨𝐧 𝐩𝐮𝐫𝐩𝐨𝐬𝐞. 𝐖𝐞 𝐝𝐨𝐧𝐭 𝐦𝐚𝐤𝐞 𝐚𝐧𝐲 𝐩𝐫𝐨𝐟𝐢𝐭𝐬 𝐟𝐫𝐨𝐦 𝐭𝐡𝐢𝐬 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐬𝐡𝐚𝐫𝐞𝐝 𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐥𝐲 𝐨𝐟 𝐟𝐫𝐞𝐞 𝐨𝐟 𝐜𝐨𝐬𝐭.
NSE:HCLTECHLong
by sreekumars
19 Jul 2024 - 5 swings on 18th, lower volatile day today vs N50BankNifty Stance Neutral ➡️ Last week we published a report stating our neutral stance, and that held up quite well as we stayed range bound this week. Technically we lost 0.23% ~ 119pts to end the week at 52221. NiftyIT was the main reason N50 swung up and down this week, esp on the 18th. BankNifty was not spared either, we had 5 swings that particular day and anyone who ran a non-directional straddle would have faced the heat. Luckily my algos went into some technical error & I got stopped out by 09.52 with a minor loss. We dropped 231pts ~ 0.44% in the first 4 minutes, then rose 502pts ~ 0.96% by 10.11, then fell by 484pts ~ 0.92% by 11.31. Then we had a super rally of 595pts ~ 1.14% till 14.27. From this peak, we fell 226pts ~ 0.43% till close. It makes me believe such intensity of madness usually appears at TOPS. BN was quite decent today, we fell only 0.67% today compared to 1.095 on N50, but we do not have enough clues to go short yet. We would like to begin the next week with a neutral stance and then take it up from there.  The budget is on Jul 23 and the volatility will definitely go up on Monday, I have not decided whether to run the algos on Monday & Tuesday or not.
NSE:BANKNIFTY
by viswaram
Day 37: Day Trading JournalDay 37: After a gap of about 15 days during which I was not trading, and trying to figure out if I can find a better algo strategy or trying to find out if manual trading is better. Since the algo that I had deployed was doing good in paper trade but real trade is a way different battle since the market gives you the worst of price at both times - entry and exit, it resulted in a breakeven after a month, now I decided to focus on my manual trading instead. I realised that when trading manually, decision making is very complex and cannot be fit in a simple algorithm even when I am looking at just one indicator. The brain actually analyses many different things without even we realising. Most of the time, brain picks up correct entry signals. I tested out my strategy by paper trading and backtesting on different stocks than which I normally follow and I found that the strategy is good and I do good and I am profitable in that. The issue however arises when real money is involved as the most difficult part to master i.e. our emotions, are not involved in backtesting or paper trading. We need to trade live only for that and build that mindset. Nothing else can prepare me for that, so its all real trade for me now, very small positions but rigorously testing the same strategy on different stocks untill I have mastered the emotions.
NSE:INDUSINDBK
by Hiren1
Day 36: Day Trading JournalDay 36 : Today, I put a halt on algo trading as well as day trading. After algo day trading for @14 days, I realised that I was not making profit, whatever profit had accrued was taken away by market. So I went back to my algo backtesting and stress tested it for worst case conditions and that actually came very close to my real time trading. Then I realised that day trading is not gonna make money. So I have decided to stop day trading and figure out a way to trade swing, now to see whether to follow the hourly chart or the daily chart and if any rule can be applied to it. For the next few days I will focus on trying to find that, meanwhile any trade I do manually will be a swing (holding overnight) to see how it goes.
NSE:INDUSINDBK
by Hiren1
Day 35 of Live algo Day Trading JournalDay 35: Not much movement today. Remained negative during the day. I am increasingly getting this feeling that intraday is not worth pursuing. The time market is open is very less, the first half hour or the full hour goes just in trying to figure out where the market wants to go and by the time you think what you'd like to do, it stalls and gets into a range. By the time it ends, the market timings are over and everybody wants to pack up. Where is the time to chase any strategy, where is the time for any strategy to develop. I think I will try looking for a swing trading strategy and see if that is profitable or not. Progress/Setback : Nothing much on both counts, will have to explore more.
NSE:INDUSINDBK
by Hiren1
Day 34 of Live Algo Day Trading JournalDay 34: What a bad day it has turned out to be. Market gyrations has given me losses for all the trades taken. The algo was correct, the logic captured the direction of the market, however the intraday pullback of the market was beyond its normal limits (or as per the set calculated limits over a certain time) and kept hitting SL everytime. Took four trades, all wrong. Setback: today's market has made me thinking if I should incoporate something else in the logic to figure out the major direction and take trades only in that direction? This will keep me occupied for the weekend.
NSE:INDUSINDBK
by Hiren1
Day 33 of Live Algo Day Trading JournalDay 33: Good day but did not turn out good for me today. Algo gave me entry in the morning, turned out profitable, but subsequent two entries took away all the profit. Overall a negative with minor loss. Progress: Stuck to the algo even when was sure that we are near the support and should have taken profit. So, no manual trading today. Learning/ to ponder: When market takes away your earnings, should you solely rely on algo ? I am thinking of changing it to stop it once a desired profit target has been achieved and take no more trades...... What are your thoughts on this ? Let it run or stop and get out ?
NSE:INDUSINDBK
by Hiren1
Day 32 of Live Algo Day Trading JournalDay 32: Day opened good, however could not sustain bullish momentum and gave up all the gains. Algo waited till it got the pullback. First trade turned out to be loss making as market played mischief and got me out. On the second trade also market tried to play mischief but the sellers were very strong and did not allow it to go beyond my SL and hence was saved. Getting to the target was a long patient wait. Had to wait for two and half hours to get it. For most of the time market just tried to push beyond the limits and kept on fooling people on both sides but my algo kept on holding. Finally at last it did go in my direction and got profit. Progress/Learnings: PATIENCE pays. Got jittery at a time when market was close to my stop loss and thought it might get hit but did not do anything manually and held on. Patience and faith on the system pays.
NSE:INDUSINDBK
by Hiren1
Day 31 of Live Algo Day Trading JournalDay 31: Day opened bullish, my stock opened and immediately ran up to its high point. Algo did not trigger as it is deisgned to get in on a pullback. Got an entry; market tried to cheat but could not succeed and was saved. However later on the market cheated and threw me out with a minor profit. Algo again got triggered but in the wrong direction, suffered small loss. Third time algo got triggered but came out at parity. Progress : Did not do any manual trade today even when felt like taking a bullish position manually(in hindsight looks like I was correct), but idea is to build the habit of only algo trading, no manual trading, so good, did not get into temptation.
NSE:INDUSINDBK
by Hiren1
Day 30 of Live Algo Day Trading JournalDay 30: Made a mistake today. Market was bearish in the morning and algo took a position which went on to be incorrect. Again algo took another position and this also turned out to be a loss making trade. After the fall over last few days, I was bullish while the algo signals were all bearish. So, after two wrong trades by my algo, I thought let me take the next trade manually and I went long. Market punished and threw me out. Then again I took two more trades (even when algo was quiet and did not give any signal), made up the loss that I had made in my manual trade. Overall, a loss making day, losing bit less than 1%, but the worst part was losing my patience and getting overconfident on my skills. Bad idea. Setback: Manual trade.... i think due to getting overconfident by looking at the chart, getting influenced by a certain call, bad impact...
NSE:INDUSINDBK
by Hiren1
Day 29 of Live Algo Day Trading JournalDay 29: Good day today. After falling for almost week, market took a breather and the bulls tried to put a stop to selling. My algo got me into a bullish position in the morning and the momentum gave me my target pretty soon. Had to be away so did not trade later on, but the algo did very well on paper trades. Had a not so good day yesterday and was not feeling good yet continued with the algo without touching it during the day, turned out to be a wise decision. Progress: Overcame the sadness of losing yesterday and did not do any manual trade, let the algo run its course.
NSE:INDUSINDBK
by Hiren1
08 May 2024 - Banks and IT were in RED, but Nifty closed flatBankNifty Analysis - Stance Bearish⬇️ BN was unable to defend the 48115 support resistance today and it was an advantage Bears. The overall price action was neutral and that might be related to the expiry trades. There were a couple of instances where BN moved 100+ pts in minutes - this really had an impact on the algo trades, but happy that major damage was not done. Both BN and NiftyIT closed the day in the RED, whereas Nifty50 ended the day flat. If IT is also going the banks' way, we might have further downsides.  For tomorrow, I wish to hold on to the Bearish view and then go neutral if we spend considerable time above 48101. The algos did pretty well today and ended up generating a whopping profit of 54085 INR.
NSE:BANKNIFTYShort
by viswaram
07 May 2024 - BankNifty stance changed to Bearish, more cuts ???BankNifty Analysis - Stance Bearish⬇️ BankNifty also fell in a single legged manner right from the open till 11.35. The fall totally unchecked by the Bulls and in a way came as a surprise as the BTFD people usually do not let the markets fall in this fast and furious manner. BN showed more weakness than N50, partly because NiftyIT was in great GREEN shape today. At a specific point, BN was down 1% and NiftyIT was up 0.9% - a stark contrast. BN ended up breaking the 48661 levels we discussed yesterday and hence we changed the stance from neutral to bearish. The next level to watch out for is 48115 and then the critical support comes at 47465. Since global markets are staying green, the FII selling in our markets could be related to political uncertainty due to the ongoing elections. BN algos ended the day with gains of 12741 INR. The bulk of the gains started coming in only after 11.39.
NSE:BANKNIFTYShort
by viswaram