Not Enough Data to display!
Average Overall
| Not Enough Data! / Avg statistics not populated yet. |
|---|
The DefaultStrategy class is a trading strategy that uses various technical indicators to generate buy and sell signals. It consists of three main methods: populate_indicators, populate_buy_trend, and populate_sell_trend. The populate_indicators method adds several technical indicators to the given DataFrame.
These indicators include ADX (Average Directional Index), MACD (Moving Average Convergence Divergence), MINUS_DI, PLUS_DI, RSI (Relative Strength Index), Stochastic Fast, Bollinger Bands, and EMA (Exponential Moving Average).
The indicators are calculated based on the provided data from the exchange.
The populate_buy_trend method generates the buy signal for the DataFrame based on the calculated indicators. It uses conditions such as low RSI, low Stochastic Fast, high ADX, and positive PLUS_DI to identify potential buying opportunities. If the conditions are met, the 'buy' column in the DataFrame is set to 1. The populate_sell_trend method generates the sell signal for the DataFrame based on the indicators. It uses conditions such as RSI crossing above 70, Stochastic Fast crossing above 70, high ADX, and positive MINUS_DI to identify potential selling opportunities. If the conditions are met, the 'sell' column in the DataFrame is set to 1. Overall, the DefaultStrategy class combines multiple indicators to determine when to buy and sell in a trading strategy.
These indicators include ADX (Average Directional Index), MACD (Moving Average Convergence Divergence), MINUS_DI, PLUS_DI, RSI (Relative Strength Index), Stochastic Fast, Bollinger Bands, and EMA (Exponential Moving Average).
The indicators are calculated based on the provided data from the exchange.
The populate_buy_trend method generates the buy signal for the DataFrame based on the calculated indicators. It uses conditions such as low RSI, low Stochastic Fast, high ADX, and positive PLUS_DI to identify potential buying opportunities. If the conditions are met, the 'buy' column in the DataFrame is set to 1. The populate_sell_trend method generates the sell signal for the DataFrame based on the indicators. It uses conditions such as RSI crossing above 70, Stochastic Fast crossing above 70, high ADX, and positive MINUS_DI to identify potential selling opportunities. If the conditions are met, the 'sell' column in the DataFrame is set to 1. Overall, the DefaultStrategy class combines multiple indicators to determine when to buy and sell in a trading strategy.
startup_candle_count : 20 bb_lowerband: -0.021% bb_middleband: -0.132% bb_upperband: -0.243%
stoploss: -0.1 timeframe: 5m hash(sha256): 3b2bd31210a46f777d47e0a80f0723036b642abbadf4e9cf8d4d8ba64f598814 indicators: macdsignal upper adx minus_di lower mid fastk bb_middleband rsi macd bb_lowerband bb_upperband plus_di fastd ema10 macdhist
Similar Strategies: (based on used indicators)
Strategy: Fureza, Similarity Score: 94.12%
Strategy: LetsWinStratv3, Similarity Score: 94.12%
Strategy: STRATEGY_EXPERIMENTAL, Similarity Score: 94.12%
Strategy: default_strategy_123, Similarity Score: 94.12%
Strategy: default_strategy_278, Similarity Score: 94.12%
Strategy: default_strategy_280, Similarity Score: 94.12%
Strategy: default_strategy_4, Similarity Score: 94.12%
Strategy: default_strategy_406, Similarity Score: 94.12%
Strategy: default_strategy_41, Similarity Score: 94.12%
Strategy: hyperoptable_strategy_5, Similarity Score: 94.12%
Strategy: strategy_test_v2, Similarity Score: 94.12%
Strategy: strategy_test_v2_1, Similarity Score: 94.12%
Strategy: strategy_test_v2_16, Similarity Score: 94.12%
Strategy: strategy_test_v2_6, Similarity Score: 94.12%
Strategy: strategy_test_v3, Similarity Score: 94.12%
last change: 2023-07-04 00:29:45