strategy_backtesting 0.1.0

Test your own strategies with python and a dataset. A hackclub siege project

PYTHON STRATEGY BACKTESTING

A cool Python backtesting tool.

The idea

You load CSV chart data into the library, build your strategy on top of it and SIMULATE it! 🥳

Installation

pip install strategy_backtesting

Install it with PIP and then USE IT!!

Keep in mind that all the CSV data need to follow this format:

timestamp,open,high,low,close,volume

where timestamp is in milliseconds and ordered from OLDEST to NEWEST.

Example

To start, let's import the required libraries:

import strategy_backtesting as sb
import pandas as pd

After that we will define the settings for our simulation

timespan - The number of data points (e.g., 1000)

buy_amount - What the simulator will buy if triggered

sell_amount - What the simulator will sell if triggered

settings = sb.Settings(timespan=1000, buy_amount=1, sell_amount=1)

Then we will create our chart object and apply the settings and CSV file (in this case the ETHUSDT chart data)

eth_chart = sb.ChartManager()
eth_chart.set_chart_settings(settings=settings)
eth_chart.set_chart_data(pd.read_csv("ETHUSDT_1h.csv").iloc[::-1])

Then I will add my strategy, for example: Buying it only if the RSI is in cricial area

buy_data = []
sell_data = []
oversold = 30
overbought = 70
for _, row in experiment_chart.iterrows():
    if not pd.notna(row["RSI"]):
        continue
    rsi = row["RSI"]
    if rsi < oversold:
        buy_data.append([row["timestamp"], row.get("close")])
    elif rsi > overbought:
        sell_data.append([row["timestamp"], row.get("close")])

Adding the strategy and simulating it

strategy = sb.Strategy(name="RSI Strategy")
strategy.set_strategy_buys(buys=buy_data)
strategy.set_strategy_sells(sells=sell_data)

simulation = sb.Simulation(chart=experiment_chart, settings=settings, strategy=strategy)
portfolio = simulation.simulate()

print("TOTAL PnL: "+str(round((portfolio["balance"].iloc[-1])-settings.default_money, 2))+"$")

simulation.graph(rsi=True, rsi_over=[oversold, overbought], ema=True)