Not Enough Data to display!
Average Overall
| Not Enough Data! / Avg statistics not populated yet. |
|---|
The StrategyManual class represents a trading strategy for backtesting on a website. Here's a short description of what the strategy does:
It uses the talib and qtpylib libraries for technical analysis indicators. The strategy operates on 1-minute timeframe data and uses 5-minute timeframe data for support.
It defines a minimal return on investment (ROI) of 0.1 (10%).
It sets a stop loss of -0.1 (10% loss).
The strategy does not use a sell signal and does not restrict selling to profit only. It does not ignore the ROI when a buy signal is generated. It does not ignore buying on an expired candle after a certain number of minutes. There is no trailing stop implemented. It requires at least 20 startup candles for calculations. The strategy's main functions are: populate_indicators(): This function is responsible for populating technical indicators on the input dataframe. populate_buy_trend(): This function populates a "buy" column in the dataframe with values indicating whether a buy signal is present. populate_sell_trend(): This function populates a "sell" column in the dataframe with values indicating whether a sell signal is present. Please note that the provided code does not contain any specific logic for generating buy or sell signals. It seems to be a skeleton implementation where you would need to add your own custom logic to define the trading strategy's rules and conditions based on the available data and indicators.
It defines a minimal return on investment (ROI) of 0.1 (10%).
It sets a stop loss of -0.1 (10% loss).
The strategy does not use a sell signal and does not restrict selling to profit only. It does not ignore the ROI when a buy signal is generated. It does not ignore buying on an expired candle after a certain number of minutes. There is no trailing stop implemented. It requires at least 20 startup candles for calculations. The strategy's main functions are: populate_indicators(): This function is responsible for populating technical indicators on the input dataframe. populate_buy_trend(): This function populates a "buy" column in the dataframe with values indicating whether a buy signal is present. populate_sell_trend(): This function populates a "sell" column in the dataframe with values indicating whether a sell signal is present. Please note that the provided code does not contain any specific logic for generating buy or sell signals. It seems to be a skeleton implementation where you would need to add your own custom logic to define the trading strategy's rules and conditions based on the available data and indicators.
stoploss: -0.1 timeframe: 1m hash(sha256): d305b0e8831230236a6571018c8f4a4ec51b213f743098c0ae97c0f5f5fb8f43
Was not able to fetch indicators from Strategyfile.
last change: 2023-06-30 12:37:09