Algo Trading Projects: Turning Ideas Into Trading Strategies with EPAT

What is an EPAT Project?

The Executive Programme in Algorithmic Trading (EPAT) is built for professionals who want to turn trading ideas into strategies. At the heart of the programme is the Algo Trading Project a hands-on project where each participant builds, tests, and documents a trading strategy using real market data.

It is a chance to bring together everything learned and produce a working proof of concept. Participants are mentored individually and receive structured feedback. Many of these algo trading projects are selected for publication and go on to influence live trading setups on our website.

Whether you are a finance professional learning code or a technologist entering the markets, the algo trading project helps turn your ideas into strategies and your strategies into experience.

Featured Work You Can Replicate

These blogs represent some of the top-performing EPAT Algo Trading and practical applications by participants. Each one comes with detailed code, real-market validation, and replicable strategy logic:

• Build a Trading Bot Using Interactive Brokers, Python, and ChatGPT: This tutorial by Pranav Lal shows how to integrate strategy logic with IBKR using Python and ChatGPT for live-trading. Ideal for building end-to-end trading systems.
• Python Strategy using Bollinger Bands: This algo trading project demonstrates how to generate and optimize Bollinger Band-based signals for equity trading, with emphasis on parameter tuning and backtesting.
• Machine Learning Strategy using Candlestick Patterns for Oil Futures: A strategy combining classic candlestick analysis with machine learning classifiers to trade ICE Brent futures.
• Deep Learning-based Price Prediction: Bala Murugan’s work on using convolutional networks to forecast stock price movement shows deep learning applied to real market features.

Don't miss this essential career guide:

Quant Roles You Should Know offers a comprehensive overview for anyone interested in quantitative finance, research, or trading roles.

Overview

EPAT Trading projects cover a wide range of algorithmic trading strategies and asset classes, demonstrating practical applications of quantitative finance concepts on real-market data.

Here are the most common algorithmic trading strategies and asset classes highlighted in the Algo Trading projects:

Common Algorithmic Trading Strategies

• Statistical Arbitrage and Pair Trading: This is a prominent strategy, frequently applied by EPATians. Projects focus on identifying and trading cointegrated stock pairs within the same sector to ensure high correlation and mean-reverting price behavior for optimal returns. Examples include:
  • Pair trading strategies across different sectors in the Indian markets
  • Statistical arbitrage in the Brazilian stock market
  • Pair trading in the Mexican stock market
  • Kalman Filter Techniques and Statistical Arbitrage in China's Futures Market
  • Crypto perpetual contract pair trading using Binance data
  • Dynamic selection of pairs for statistical arbitrage
  • Implementing statistical arbitrage in FX Markets
• Machine Learning (ML) and Artificial Intelligence (AI): These advanced techniques are widely used across various algo trading projects to enhance trading performance and make predictions. Applications include:
  • Intraday buy and sell signals for cryptocurrency using Scikit-Learn and VectorBt
  • Stock price prediction with ARIMA and LSTM
  • Portfolio asset allocation with machine learning
  • Building a long-only strategy for retail traders
  • Optimizing exit conditions with variable take profit and stop loss
  • Predicting price trends of metals
  • Predicting stock trends using technical analysis & Random Forests
  • Predicting Bank Nifty open price using Deep Learning
  • Dynamic asset allocation with Neural Networks for Nifty Bank Index stocks
  • Random Forest Regression for Forex trading using price & sentiment indicators
• Momentum Trading and Trend Following: Strategies to capture price movements based on trends or momentum.
  • Trading with low ADX and momentum indicators
  • Trend Following Strategy in Futures using TSMOM and Continuous Forecasts
  • Automation with 200 SMA & 50 EMA for intraday trading
  • Momentum-based live trading using IBridgePy & Python
  • Trading Index Options based on Index Momentum
  • Strategies using MACD, ST & ADX
• Gap Trading Strategy: An EPAT project focusing on gap trading in Indian equities, targeting low-volatility stocks. A long-only strategy entering at close and exiting next open, showing higher Sharpe ratios and reduced volatility.
  Gap Trading Strategy: Based on the Markov Rule
• Portfolio Management and Asset Allocation: Strategies for managing and allocating capital across assets.
  • Diversified ETF Portfolio: Backtesting to live trading
  • Rule-based Portfolios to beat market returns
  • Long-only, Low-frequency Asset-allocation Algorithms
  • Comparative portfolio allocation with pair trading strategies
  • Modern Portfolio Management using CAPM & Fama-French Model
• Options Trading and Volatility Strategies: Exploring advanced options methods.
  • Dispersion trading using options
  • Trading Index Options based on Momentum
  • Variance Risk Premium capture with Tail Protection
  • Dispersion Trading in Indian markets with Bank Nifty

Common Asset Classes

EPAT projects analyze and apply strategies across diverse asset classes:

• Equities / Stock Markets: A very common asset class, with projects focusing on various regional markets.
  • Indian Equities: Including specific indices like the Nifty 50 Index and Bank Nifty.
  • Brazilian Stock Market (B3)
  • Mexican Stock Market
  • China A-Share Stocks
  • General stock price prediction
• Cryptocurrency: A significant focus for projects involving machine learning and statistical arbitrage.
  • Generating intraday buy and sell signals for cryptocurrency
  • Crypto Perpetual Contract Pair Trading with Binance data
  • Order Flow Strategy for Crypto Markets (ETHBTC)
• Forex (FX) / Currency Markets: Explored for machine learning applications and arbitrage strategies.
  • Building Random Forest Regression models for Forex trading
  • Implementing pairs trading/statistical arbitrage in FX Markets
• Futures: Projects focusing on trend following and arbitrage.
  • China's Futures Market
  • Cash to Future Arbitrage
  • Exchange Traded Funds (ETFs): Used for portfolio diversification and pair trading.
• Options: Projects specifically dealing with options strategies.
  • Trading Index Options
  • Dispersion Trading Using Options
• Metals: Prediction of price trends for metals using Machine Learning is also a project topic.

Next steps

Try it out: Learning by Doing

The best way to evaluate whether this approach works for you is to read through the project and try it hands-on. Each step is built to teach you not just the “what,” but the “how” and “why.”
Start by replicating the code, observing how it behaves with different data, and making small modifications. That’s where real learning begins.

As Pranav Lal said in his article:
Giving a profitable strategy is not the focus. The focus is to demonstrate how to build one.Use it to learn. Backtest it. Modify it. Break it and fix it.
Because once you learn how to do that, you won’t need to rely on someone else’s strategy. You’ll be able to build and trust your own.

Go All In with EPAT

The Executive Programme in Algorithmic Trading (EPAT) equips professionals to become fully self-sufficient quant traders. Covering machine learning, time series forecasting, trading APIs, and live deployment, EPAT has helped thousands (including Pranav) launch and scale live strategies with confidence.

• Know more about EPAT:
• Book a 1:1 call to get your questions answered.
• Download the EPAT brochure to understand the curriculum, faculty, projects, and career outcomes.

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