Trading Strategies Backtesting With Python

Learn to build trading systems with clear rules for entry, exit, and position sizing, and backtest them on extensive intraday data to ensure reliability and avoid look-ahead bias.

Explore moving averages as smoothing tools for price data, compare simple and exponential types, note their lag, and learn crossover strategies using fast and slow averages.

Develop a Python backtesting workflow by preparing currency data in a Jupyter notebook, cleaning date-time fields, and organizing data for a simple moving average crossover strategy.

Build indicators and dataframes for backtesting trading strategies in Python by defining moving average and range functions, then populate dataframes with parameters like account size and position size.

Implement a time exit in backtesting by closing trades after a chosen number of hours, using iteration counts to measure duration and adapt to data frequency.

Apply strategy 4.2 backtest by checking if current or previous bar size is four times the average and confirm price movement direction, then optimize take profit and stop loss.

Explore Bollinger bands, built from a center simple moving average and two standard deviation lines, and use lower and upper bands as basic buy and sell signals amid volatility.

Apply Bollinger bands with fast and slow simple moving averages to signal entries after retracements to the mean line, entering long from the lower band and short from the upper.