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Backtesting Fair Value Gap trading strategy

Backtesting of a fair value gap trading strategy on a rolling 300 data point input Executable with M1-M30 timeframes, selectable exchanges and assets i.e.: python3 main.py -a 'AVAX' -t '15m' -d '2021-11-03' -e 'binance'

TODO:

  • Move FVG shading anchor to start of detection date ☑

  • Filter out invalidated FVG zones at point of consumption ☑

  • Begin defining a set of entry strats depending on the last n-(10 to 20) candle movements ☑

  • Build in entry/exit position commands + fit to 1:1.5 or something ☑

  • Clean up a load of stuff for plug and play of strats, bit messy atm as i was just wanting to get soemthing working lol ☑

  • Introduce mutli-asset asyncio executions with complete PnL charting after

  • Come up with some better research into FVG delta qualifications

  • Introduce some proper risk/reward ratios

  • Backtested with 10x leverage and numbers are shown in the above PnL chart, cant be true surely? Got to be some fuckery going on

Backtest FVG detection The numbers Mason

Backtests:

  • ATOM - 10x Leverage - 1000USD -> 6521.90USD - M15 1 year (Trade EV, +EV: 324, -EV: 120) Config: -60 Rolling Window, 0.99/1.01, M15
  • SUSHI - 10x Leverage - 1000USD -> 9044.50USD - M15 1 year (Trade EV, +EV: 373, -EV: 143) Config: -60 Rolling Window, 0.99/1.01, M15