mrtoronto / stock_backtester

Stock trading strategy back-tester

stock_backtester

General

Running backtest.py will perform a run of the script. The output will be a plot of the back-test.

Each run of the back-tester will use specified trading strategies to maintain a portfolio of stock over the specified timeframe. The results of all the strategies are plotted to be compared to each other and to the results of a "buy and hold" strategy on the S&P500 using the same amount of money.

Run description

1. Pull test ticker and S&P500 data from Yahoo! Finance or cache file created by process.

   • Changing the cache boolean to False will make the process pull fresh every run.
   • Cache folder and subfolders for each ticker will be created if necessary.
   • Partially complete cache data will be filled in from Yahoo! Finance.
     • If cache had data from '2017-01-01'-'2018-01-01' for a ticker and user requested data from '2017-01-01'-'2018-06-01' for the same ticker, process will pull data for '2018-01-01'-'2018-06-01' from Yahoo! Finance and update the stored cache file.

2. Derive indicators from the raw data.

   • Done in the indicators.py script.
   • Currently implemented indicators:
     • Daily price difference ($)
     • Daily price difference (%)
     • MACD and signal line

3. Run a "buy and hold" strategy on the S&P500 data

   • Buy and hold strategy buys all possible stock on the first day of run and holds through the run

4. Run the add_strategies function on the DataFrame to add data for any specified custom strategies.

   • Each 'strategy' lives in its own python script in the strategies folder.
   • A strategy follows the format below:
     • Iterate through a zip of any columns that'll be required for the strategy. For example:
       • MACD strategy iterates through zip(df['macd'], df['macd_signal'], df['Close']) as the strategy uses the MACD, the MACD's signal line and the day's Close price for each update.
   • Currently included strategies:
     • Buy and Hold strategy - Buys as much stock as possible immediately, holds for rest of run.
     • MACD Crossover Strategy
       • Relevant indicators:
         • MACD line == (12-day EMA - 26-day EMA)
         • Signal line == (9-day EMA of MACD)
       • When the MACD crosses above the signal line, its considered a bullish crossover.
       • When the MACD crosses below the signal line, its considered a bearish crossover.
       • In this strategy, the bot will buy and sell depending on whether a day is part of a bullish or bearish crossover at Close.
     • Scaled MACD Crossover Strategy
       • This strategy is the same as the MACD Crossover strategy except it buys/sells a set number of stock each day instead of 1.
     • Faded Scaled MACD Crossover Strategy
       • It doesn't appear this strategy preforms as well as normal scaled_MACD but feel free to try it.
       • This strategy is the same as the Scaled MACD crossover except instead of buying a set number per day, it has a constantly incremented current_scale factor.
         • This number is increased on bullish days and decreased on bearish days.
         • It is also used as the number of stock to buy on any given day.

5. Print net_worth columns to the console.

6. Run the plotting function (compare_plot_fn.py)

   • Relevant arguments are listed below:
     • save_image
       • By default this is blank and no image is saved
       • If its anything other than blank, the string will be used as the file name for an image of the plot.
     • names
       • List of strategy names from the add_strategies() function
   • The plotting function uses the names list to generate a twinx axis for each strategy.
   • Plots the net_worth of each strategy as time goes on.
     • Plots the S&P500 one a bit differently so its more of a benchmark.
   • Dynamically scales the Y axis based on the max and min values that appear in the run.
   • Adds an annotation with info on each strategy to the left side.

To do

• Add more strategies.