Quantitative trading is a type of market strategy that relies on mathematical and statistical models to identify and execute opportunities and trades.

This project aims to serve as a framework for developing and backtesting trading strategies, allowing for easy data visualisation and strategy performance comparison.

Presented in the script as a demonstration, an extremely basic, and likely unprofitable, simple moving average crossover strategy is provided. Said strategy buys when the 10-day moving average crosses the 20-day moving average, and sells when the reverse occurs.

Building upon this framework, much more complex, robust, and profitable strategies can be built, tested, and optimised.

💰 Develop and backtest trading strategies

🏦 Develop highly customised indicators

💲 Compare and analyse quant strategies

🧰 Develop a framework for backtesting trading strategies

☑️ Deep dive into Backtrader's library and understand both it's capabilities and limitations.

🧾 Further develop knowledge of Matplotlib

🤖 Backtest a simple moving average crossover trading strategy

• Backtrader
• Matplotlib

1. Obtain price data.

2. Create a signal generator within the init function of the strategy class:

 # signal generator
  def __init__(self):

      ma_fast = bt.ind.SMA(period = 10)
      ma_slow = bt.ind.SMA(period = 20)

      self.crossover = bt.ind.CrossOver(ma_fast, ma_slow)

3. Create buy and sell orders based upon the previously generate signals. For example:

 # executes order from the signals
  def next(self):
      if not self.position:         # if not already in a position
          if self.crossover > 0:    # if 10-day moving average crosses above 20-day moving average
              self.buy()            # take a long position
      elif self.crossover < 0:      # if 10-day moving average crosses below 20-day moving average
          self.close()              # close long position

3. Initialise the backtesting engine (Cerebro). Add the price data and strategy before setting inital conditions such as account size and risk amount per trade etc.

  cerebro = bt.Cerebro()

  # adds data to engine
  cerebro.adddata(data)
  # adds strategy to engine
  cerebro.addstrategy(MaCrossStrategy)

  # sets starting capital
  cerebro.broker.setcash(1000.0)
  # sets size per trade
  cerebro.addsizer(bt.sizers.PercentSizer, percents = 10)

4. Run the back test using:

  back = cerebro.run()

Distributed under the MIT License. See LICENSE for more information.

Twitter - @TraderTDF

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Project Link: https://github.com/Elisik/Quant-Trading-Strategy-Backtesting-Framework