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The "FrankenStrat" strategy is a backtesting strategy that aims to generate buy and sell signals based on various technical indicators and conditions. Here is a breakdown of the strategy:
"populate_indicators" method:
It populates the dataframe with different indicators, such as informative_1h and normal_tf_indicators. "populate_buy_trend" method:
It sets conditions for buying based on different combinations of indicators and criteria.
The strategy includes multiple buy conditions, each separated by the "|" symbol.
Each buy condition consists of a set of logical statements and comparisons using the dataframe's columns.
Examples of buy conditions include checking the close price against certain moving averages, EWO (Elliott Wave Oscillator), volume, RSI (Relative Strength Index), and Bollinger Bands. If any of the buy conditions are met, the corresponding row in the "buy" column of the dataframe is set to 1. "populate_sell_trend" method: It sets conditions for selling based on different combinations of indicators and criteria. The strategy includes multiple sell conditions, each stored in the "conditions" list. Similar to the buy conditions, each sell condition consists of logical statements and comparisons. Examples of sell conditions include checking the close price against Bollinger Bands, EWO, and volume. If any of the sell conditions are met, the corresponding row in the "sell" column of the dataframe is set to 1. Overall, the strategy combines various technical indicators and criteria to generate buy and sell signals. The specific conditions used in the strategy may vary, but the goal is to identify potential buying and selling opportunities in the market.
The strategy includes multiple buy conditions, each separated by the "|" symbol.
Each buy condition consists of a set of logical statements and comparisons using the dataframe's columns.
Examples of buy conditions include checking the close price against certain moving averages, EWO (Elliott Wave Oscillator), volume, RSI (Relative Strength Index), and Bollinger Bands. If any of the buy conditions are met, the corresponding row in the "buy" column of the dataframe is set to 1. "populate_sell_trend" method: It sets conditions for selling based on different combinations of indicators and criteria. The strategy includes multiple sell conditions, each stored in the "conditions" list. Similar to the buy conditions, each sell condition consists of logical statements and comparisons. Examples of sell conditions include checking the close price against Bollinger Bands, EWO, and volume. If any of the sell conditions are met, the corresponding row in the "sell" column of the dataframe is set to 1. Overall, the strategy combines various technical indicators and criteria to generate buy and sell signals. The specific conditions used in the strategy may vary, but the goal is to identify potential buying and selling opportunities in the market.
startup_candle_count : 200 ema_50_1h: 0.001% rsi_slow: 0.002% hma_50: -0.001%
Unable to parse Traceback (Logfile Exceeded Limit)
stoploss: -0.99 timeframe: 5m hash(sha256): 7d19b13ad2b9d649b4f41d7e444db5448f89edef8ac5616605897d3cd0892d64 indicators: upper tema ema_200 ema_50 close sma_5 bb_lowerband ema_200_1h ewo rsi_fast volume smaHigh ATR ssl_up sslDown open hlv volume_mean_slow ema_50_1h smaLow high sslUp mid ssl_down_1h hma_50 ssl_down rsi_1h lower rsi_slow ema_12 bb_middleband rsi ema_length bb_upperband ema_slow ema_26 low ssl_up_1h
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last change: 2024-08-02 09:06:08