Algorithmic Trading with Real Python Hands-On Examples

Prepare data for EMA backtesting by downloading finance data (open, high, low, close), creating signal and EMA columns, and defining buy or sell rules to simulate trades.

Apply EMA backtesting in Python by downloading data, computing EMA and moving averages, and generating buy/sell signals. Evaluate performance using CAGR and profit, and visualize the EMA-based strategy and trades.

Backtest the MACD strategy by analyzing fast and slow moving averages, computing the difference to generate signals, and reviewing buy and sell actions and performance metrics.

Explain a bollinger band backtesting strategy based on mean reversion using moving averages and the upper and lower bands. Generate buy and sell signals with stop loss while testing performance.

Explore a Bollinger band plus ADX backtesting strategy that uses moving averages, standard deviation, and an ADX threshold to detect trends and guide buy-sell decisions.

Back test a three ema strategy with fast, middle, slow EMAs using Yahoo Finance data; buy when low<50 and yesterday’s close>50 at next day open, with stops and take profit.

Optimize MACD parameters through iterating parameter ranges, applying backtesting to evaluate results, and comparing performance metrics to refine the trading strategy.

Simulate a realistic Nasdaq-100 stock trading using a 3-ema channels strategy in Python. Show how buy and sell signals, position sizing, and equity curve updates are modeled.

Design a 3 ema channels screening system that adapts to Nasdaq 100 or S&P 500, uses ema-based entry criteria and price pullbacks, and fetches data from Yahoo Finance.

Discover an oversold RSI screening system to identify buy points when RSI hits 30, using moving averages and recent price action within a Nasdaq 100 or S&P 500 universe.