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

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