TradStrat

TradStrat is a beginner-friendly Streamlit app for exploring simple trading strategies on historical daily price data. It is designed for learning how backtests work, not for predicting live markets or giving investment advice.

What it does

• Lets you choose a ticker, date range, strategy, and basic risk settings.
• Runs an event-driven backtest day by day, with next-day-open execution, commission, and fixed position sizing.
• Shows an equity curve, buy-and-hold benchmark, trade markers, drawdown chart, and summary metrics.
• Includes an offline sample dataset so you can try the app even when Yahoo Finance is unavailable.

Project Structure

• app.py - Streamlit UI and plotting.
• data_loader.py - Daily OHLCV fetch + validation + CSV cache.
• strategies.py - Strategy classes for moving-average crossover and mean reversion.
• engine.py - Event-driven backtesting loop and trade log generation.
• metrics.py - Return, risk, and trade-quality metrics.
• glossary.py - Plain-English definitions used by the UI help text.
• sample_data/aapl_sample.csv - Offline demo dataset.
• tests/test_metrics.py - Unit tests for the metrics module.

How To Run

Install dependencies:

/bin/python3 -m pip install --break-system-packages -r requirements.txt

Start the app:

cd "/media/elarion/6715dcac-8957-4b06-8f07-70d7f123b1b4/home/Projects/Trading Strategy Backtester/TradStrat"
/bin/python3 -m streamlit run app.py

If live Yahoo Finance data is not available in your environment, enable Use sample dataset (offline demo) in the sidebar.

Run the tests:

cd "/media/elarion/6715dcac-8957-4b06-8f07-70d7f123b1b4/home/Projects/Trading Strategy Backtester/TradStrat"
/bin/python3 -m unittest tests.test_metrics

Strategies

Moving Average Crossover

Buys when a short moving average rises above a long moving average and exits when the signal turns off.

Mean Reversion

Uses a rolling z-score. It enters when price is stretched below its recent average and exits when price moves back toward normal.

Metrics

• Total Return: percentage gain or loss over the full backtest.
• CAGR: annualized growth rate.
• Sharpe Ratio: average return relative to total volatility.
• Sortino Ratio: average return relative to downside volatility only.
• Max Drawdown: largest peak-to-trough decline.
• Max Drawdown Duration: longest time spent below a previous peak.
• Win Rate: percentage of profitable trades.
• Average Win / Average Loss: average profit and average loss per closed trade.
• Number of Trades: total closed trades.

Important Limitations

• This app uses daily data only, not intraday tick data.
• Execution is simplified: signals are generated from historical closes and trades are filled at the next day’s open.
• Slippage and commission are simplified and do not model real order-book behavior.
• It does not model latency, partial fills, spread dynamics, borrow costs, or market impact.
• The bundled sample dataset is synthetic and meant for demonstration only.
• Results are educational and should not be treated as trading advice.

Notes For Beginners

• A backtest is a simulation of how a strategy would have behaved in the past.
• An equity curve is the line showing how portfolio value changes over time.
• The buy-and-hold benchmark is the simplest comparison: buy once and hold through the whole period.
• Look-ahead bias is avoided by using only information available up to the current day and executing on the next open.