Not Enough Data to display!
Average Overall
| Not Enough Data! / Avg statistics not populated yet. |
|---|
The MLStrategy is a backtesting strategy implemented in Python using the Freqtrade bot framework. It analyzes the historical price and volume data of a trading pair and generates buy and sell signals based on technical indicators. Here is a brief description of what the strategy does:
The strategy defines a minimal return on investment (ROI) for different time intervals, such as 40 minutes, 30 minutes, 20 minutes, and 0 minutes.
It sets an optimal stop loss value of -0.10, which means that if the price drops by 10% from the entry point, the position will be sold.
The ticker interval is set to 5, indicating that the strategy operates on 5-minute candlestick data.
The slippage parameter is set to 0.01, accounting for transactional costs and price deviations. The ML_parse_ticker_dataframe function parses the ticker history and converts it into a DataFrame, organizing the data into columns such as close price, volume, open price, high price, low price, and date. The populate_indicators function adds various technical indicators to the DataFrame, including the RSI (Relative Strength Index) and Heikin-Ashi candlestick chart. The populate_buy_trend function generates a buy signal when the RSI indicator is below 35. The populate_sell_trend function generates a sell signal when the RSI indicator crosses above 70. Overall, the strategy aims to identify potential buying opportunities when the RSI is low and selling opportunities when the RSI is high. It utilizes additional indicators like Heikin-Ashi candles to enhance the analysis.
It sets an optimal stop loss value of -0.10, which means that if the price drops by 10% from the entry point, the position will be sold.
The ticker interval is set to 5, indicating that the strategy operates on 5-minute candlestick data.
The slippage parameter is set to 0.01, accounting for transactional costs and price deviations. The ML_parse_ticker_dataframe function parses the ticker history and converts it into a DataFrame, organizing the data into columns such as close price, volume, open price, high price, low price, and date. The populate_indicators function adds various technical indicators to the DataFrame, including the RSI (Relative Strength Index) and Heikin-Ashi candlestick chart. The populate_buy_trend function generates a buy signal when the RSI indicator is below 35. The populate_sell_trend function generates a sell signal when the RSI indicator crosses above 70. Overall, the strategy aims to identify potential buying opportunities when the RSI is low and selling opportunities when the RSI is high. It utilizes additional indicators like Heikin-Ashi candles to enhance the analysis.
stoploss: -0.1 timeframe: 5m hash(sha256): b5390f8348ddd1aa32aeb9c683fe13d343b5343393140da8949f4df840676469 indicators: high ha_high ha_low close rsi ha_close open low ha_open
No similar strategies found. (based on used indicators)
last change: 2024-04-28 09:02:52