jrmeier/fast-trade

low code backtesting library utilizing pandas and technical analysis indicators

Hi there 👋

• 🔭 I’m working at FieldSync
• 💬 Talk to me about: Python, JavaScript, or algorithmic trading
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fast-trade's Issues

Error when run_backtest

print(validate_backtest(strategy_object))
datafile_path = "./archive/BTCUSDT1.csv"
result = run_backtest(strategy_object, datafile_path)

File "blablab\venv\lib\site-packages\fast_trade\build_data_frame.py", line 246, in standardize_df
new_df.open = pd.to_numeric(new_df.open)

AttributeError: 'DataFrame' object has no attribute 'open'

I cant run_backtest on my computer

So how do you write a strategy?

Give docs.

AutoML Support?

Is there a way to churn out multiple strategies without breaking a sweat by testing possible variations? It has been said that XGBoost and algorithms like them are useful for trading algorithms.
A secondary thought would be to get TA-Lib to churn out all their indicators based on the time frame provided in multiple granularity (similar to what is done with MA), and deciding on the Boolean output criteria (e.g. whether the performance after X days exceed y%).
Would there be a way to use fast-trade to accelerate the search?

sorry. please delete this issue

Add exit after X percent gain in current trade

Add a field that defines an exit after a trade is work an user defined amount.

#### Better run summary

Return a better run summary, try to match backtesting.py

Start 2004-08-19 00:00:00
End 2013-03-01 00:00:00
Duration 3116 days 00:00:00
Exposure [%] 94.29
Equity Final [$] 69665.12
Equity Peak [$] 69722.15
Return [%] 596.65
Buy & Hold Return [%] 703.46
Max. Drawdown [%] -33.61
Avg. Drawdown [%] -5.68
Max. Drawdown Duration 689 days 00:00:00
Avg. Drawdown Duration 41 days 00:00:00
numTrades 93
Win Rate [%] 53.76
Best Trade [%] 56.98
Worst Trade [%] -17.03
Avg. Trade [%] 2.44
Max. Trade len 121 days 00:00:00
Avg. Trade len 32 days 00:00:00