The FOffset Strategy is a trading strategy implemented as a subclass of the IStrategy class. It focuses on using various technical indicators to determine entry and exit points for trading. The key components of the strategy are as follows:
Indicators Population: The populate_indicators function calculates and adds multiple technical indicators to the provided DataFrame.
It includes: Exponential Moving Averages (EMA) with different time periods.
Elliott Wave Oscillator (EWO).
Relative Strength Index (RSI) with a time period of 14. missing_data, which counts the number of candles with zero or negative volume over a specified startup period. Bollinger Bands and related metrics such as percent within the bands and band width. Stochastic Fast (STOCHF) indicators. Exponential Moving Averages (EMA) and Simple Moving Averages (SMA) with various time periods. Entry Signal Calculation: The populate_entry_trend function calculates entry signals based on the populated indicators. It involves: Calculating a specific EMA (moving average) based on the base_nb_candles_buy parameter. Setting conditions for entering a trade, including: The closing price being below a certain offset from the calculated EMA. EWO (Elliott Wave Oscillator) being above a specified threshold. RSI (Relative Strength Index) being below a specified threshold. The missing_data condition to avoid trading during periods with insufficient data. Overall, the strategy aims to identify potential buying opportunities based on technical analysis indicators like EMA, EWO, and RSI. It combines these indicators to generate entry signals that suggest when to initiate a trade. The strategy's implementation includes considerations for avoiding trading in situations with insufficient data. It's important to note that this is a simplified summary, and the strategy's effectiveness would depend on various market conditions and parameter settings.
It includes: Exponential Moving Averages (EMA) with different time periods.
Elliott Wave Oscillator (EWO).
Relative Strength Index (RSI) with a time period of 14. missing_data, which counts the number of candles with zero or negative volume over a specified startup period. Bollinger Bands and related metrics such as percent within the bands and band width. Stochastic Fast (STOCHF) indicators. Exponential Moving Averages (EMA) and Simple Moving Averages (SMA) with various time periods. Entry Signal Calculation: The populate_entry_trend function calculates entry signals based on the populated indicators. It involves: Calculating a specific EMA (moving average) based on the base_nb_candles_buy parameter. Setting conditions for entering a trade, including: The closing price being below a certain offset from the calculated EMA. EWO (Elliott Wave Oscillator) being above a specified threshold. RSI (Relative Strength Index) being below a specified threshold. The missing_data condition to avoid trading during periods with insufficient data. Overall, the strategy aims to identify potential buying opportunities based on technical analysis indicators like EMA, EWO, and RSI. It combines these indicators to generate entry signals that suggest when to initiate a trade. The strategy's implementation includes considerations for avoiding trading in situations with insufficient data. It's important to note that this is a simplified summary, and the strategy's effectiveness would depend on various market conditions and parameter settings.
stoploss: -0.99 timeframe: 5m hash(sha256): 2753786fdebeac939ec7e8705b321e7b1a0cba00136cc011feb6f052a029dd6d indicators: rsi_buy dir upper ema30 EWO ewo_high close ewo_low bb_lowerband bb_percent ema5 fastd_rsi fastk ma_sell_val ma_buy_val volume low_offset high_offset missing_data fastd base_nb_candles_buy rsi_14 high mid sma30 sma50 fastk_rsi ema100 bb_width base_nb_candles_sell sma21 ema3 ema200 sma5 lower bb_middleband rsi ema21 bb_upperband sma3 low ema50 rsi_25
Similar Strategies: (based on used indicators)
Strategy: EGCE, Similarity Score: 97.73%
Strategy: EGCESO, Similarity Score: 97.73%
Strategy: FExit, Similarity Score: 97.73%
last change: 2024-04-29 19:36:18