Trading in the stock market has always been a mix of strategy, psychology, and technology. With the rise of Machine Learning (ML), traders like me are now experimenting with AI-driven models to make better predictions. Recently, I built an LSTM-based prediction model to forecast Nifty50 5-minute candles and it has changed the way I trade.
In this post, I’ll walk you through the key aspects of my journey — from the difference between ML and algo trading to how I use my model for trading options and futures.
Table of Contents
1. Machine Learning Prediction vs Algorithmic Trading
Many people confuse ML-based prediction with algorithmic trading, but they are different:
- Algorithmic Trading: In algo trading, rules are predefined. For example, “If RSI < 30, Buy” or “If 50 EMA crosses 200 EMA, Sell.” It’s rule-based and deterministic.
- ML-based Prediction: In machine learning, the model learns patterns from historical data. Instead of hardcoded rules, the system predicts future price movement based on past OHLC values.
👉 My approach is ML-based. The model doesn’t rely on fixed rules but learns patterns from Nifty50’s historical data and predicts the next 5-minute candles.
2. What is LSTM?
LSTM (Long Short-Term Memory) is a special type of Recurrent Neural Network (RNN) that is excellent at learning sequential data like stock prices, which depend on time series. Unlike traditional neural networks, LSTMs can “remember” patterns over longer sequences, making them suitable for financial data prediction.
LSTM (Long Short-Term Memory) is a special type of Recurrent Neural Network (RNN) designed for sequential data like stock prices.
- Stocks move in sequences (time series).
- Traditional neural networks fail to capture long-term patterns.
- LSTM uses memory cells to remember important patterns over time.
👉 Example: If Nifty50 rises for 3 hours with small pullbacks, LSTM can capture this momentum better than a normal model.
That’s why LSTM is perfect for stock market predictions.
3. Why I Chose LSTM + My Coding Challenges
I chose LSTM because financial data is time-dependent. Prices today are influenced by what happened a few minutes ago. LSTMs handle this temporal relationship better than simple models like Linear Regression.
Challenges I faced while coding:
- Data preprocessing: Handling missing candles and ensuring proper scaling.
- Model tuning: Choosing the right number of LSTM units, dense layers, dropout, and learning rate was tough.
- Overfitting: My model initially performed well on training data but poorly on test data.
👉 To solve this, I used hyperparameter tuning. The best parameters I found were:
- LSTM Units: 256
- Dense Units: 128
- Dropout: 0.3
- Learning Rate: 0.0005
- Batch Size: 64
This tuning significantly improved the accuracy of my predictions.
4. How LSTM Helped My Trading + Retraining the Model
The LSTM model has been a game-changer for my trading:
- It predicts 5-minute OHLC candles for the entire trading day.
- I can quickly spot trends (uptrend or downtrend) without relying only on indicators.
- Retraining is simple — I just update my dataset with the latest market data and retrain using the saved model architecture.
This means I don’t have to build a new model every time. I just refresh it with updated market data.
5. Five-Minute Predictions for the Whole Day
Instead of predicting only one step ahead, I trained my model to predict the full day’s 5-minute candles. This allows me to plan trades in advance, check possible reversals, and decide on entry/exit strategies before executing them.
6. Understanding Options and Futures in Trading
To apply ML predictions effectively, it’s important to understand the instruments:
- Options: A contract that gives the right (but not obligation) to buy/sell an asset at a certain price.
- Futures: A contract where you agree to buy/sell an asset at a future date, at a fixed price.
Both are derivative instruments of Nifty50, widely used for hedging and speculation.
7. How to Use Options Buy with ML Predictions
- If my LSTM model predicts a strong uptrend, I can buy Call Options.
- If it predicts a downtrend, I can buy Put Options.
Options buying is cheaper but comes with time decay risk, so the accuracy of short-term predictions (like 5-min candles) helps a lot.
8. How to Use Options Sell with ML Predictions
- If my model predicts sideways or stable markets, I can sell options (Call or Put).
- Options selling benefits from time decay and can generate consistent income when predictions confirm low volatility.
9. How to Use Futures with ML Predictions
Futures are more direct:
- If my model shows bullish signals, I can go long on Nifty Futures.
- If bearish, I can short Nifty Futures.
Unlike options, futures don’t suffer from time decay, but they require higher margins.
10. My Code + GitHub Repository
I’ve open-sourced my entire project so that others can learn, test, and improve it. 🚀
👉 GitHub Repo: Nifty50Trading-With-LSTM
It contains:
- Data preprocessing code
- LSTM model training + hyperparameter tuning
- Prediction + visualization scripts
Feel free to fork it and try your own experiments.
Final Thoughts
Building an LSTM-based prediction model has helped me understand market behavior better and given me a structured way to trade Nifty50. While it’s not perfect (no model is!), it provides an edge in decision-making when combined with proper risk management.
If you’re a trader curious about AI in trading, I strongly recommend experimenting with ML models — it’s a fascinating journey that blends finance and technology.
