A cool Python backtesting tool.
You load CSV chart data into the library, build your strategy on top of it and SIMULATE it! 🥳
pip install strategy_backtestingInstall 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.
To start, let's import the required libraries:
import strategy_backtesting as sb
import pandas as pdtimespan - 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 >gt; 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)Now let's say we want to apply our strategy with a live chart. But we also need the websocket data So let's initialize a session with::
df = pd.DataFrame() # Used for the data displaying
url = 'wss://stream.binance.com:9443/ws/'
symbol = 'ethusdt'
stream = '@aggTrade'
long_url = url+symbol+stream
session = sb.Session(websocketURL=long_url, strategy=strategyFunction)After we started the session with
session.start()But we also need a function that places our buy and sell marks:
def strategyFunction(data):
global df, simulated_assets, simulated_money
# Here we receive price data and while its running we get the indicators so we add every entry to our strategy list
append = {
"timestamp": data['T'],
"close": float(data['p']),
"signal": None
}
new = pd.DataFrame([append])
df = pd.concat([df, new], ignore_index=True)
# Here you can add your own strategy and add signal: buy/sell depending on your strategywe can define a live chart:
# Wait till connection is available with websocket
time.sleep(3)
session.live_chart(
df_getter=lambda: df, # DATAFRAME
interval=0, # UPDATE INTERVAL
max_p=10000, # MAX. DATA POINTS
show_ema=True, # EMA LINES
show_rsi=True, # RSI
rsi_levels=[30, 70], # RSI, 30 LOW, 70 UP
timeframe='1m' # TIMEFRAME
)
# KEEPING IT RUNNING
while(session.running):
time.sleep(1)FINISH! 🥳
