Not Enough Data to display!
Average Overall
| Not Enough Data! / Avg statistics not populated yet. |
|---|
The BigZ04_TSL2 strategy is a trading strategy implemented as a class in Python. It is designed to be used for backtesting on a trading platform or website. Here is a short description of what the strategy does:
The populate_indicators function takes a DataFrame of market data and additional metadata as input.
It applies several indicators to the DataFrame, including informative_1h indicators and normal time frame (tf) indicators.
The function merges the informative_1h indicators into the DataFrame and returns the modified DataFrame.
The populate_buy_trend function is responsible for determining the buy conditions based on the populated indicators. It defines multiple conditions using logical operators such as AND (&) and OR (|). Each condition represents a specific set of criteria that need to be met for a buy signal. The conditions consider various factors, including price movements, moving averages (ema), Bollinger Bands (bb), relative strength index (rsi), and trading volume. If any of the conditions are met, a buy signal is generated. The strategy appears to have multiple buy conditions, numbered from 0 to 6. Each condition has its own set of criteria that must be satisfied for a buy signal. These criteria involve comparisons between price, indicators, and volume. Overall, the strategy combines multiple technical indicators and conditions to generate buy signals based on different patterns and market conditions.
It applies several indicators to the DataFrame, including informative_1h indicators and normal time frame (tf) indicators.
The function merges the informative_1h indicators into the DataFrame and returns the modified DataFrame.
The populate_buy_trend function is responsible for determining the buy conditions based on the populated indicators. It defines multiple conditions using logical operators such as AND (&) and OR (|). Each condition represents a specific set of criteria that need to be met for a buy signal. The conditions consider various factors, including price movements, moving averages (ema), Bollinger Bands (bb), relative strength index (rsi), and trading volume. If any of the conditions are met, a buy signal is generated. The strategy appears to have multiple buy conditions, numbered from 0 to 6. Each condition has its own set of criteria that must be satisfied for a buy signal. These criteria involve comparisons between price, indicators, and volume. Overall, the strategy combines multiple technical indicators and conditions to generate buy signals based on different patterns and market conditions.
startup_candle_count : 1000 Recursive Analysis found no issues while using 1000 startup_candle_count
stoploss: -0.99 timeframe: 5m hash(sha256): 07a3f7510462bb5b9dc0048f7b182a779d3ab32eb5632a8196d7a5ac23552111 indicators: upper ema_200 ema_50 close sma_5 bb_lowerband ema_200_1h ma_sell_val volume open volume_mean_slow mid macd rsi_1h bb_lowerband_1h hist atr lower ema_12 signal bb_middleband rsi bb_upperband ema_26 low close_1h
Similar Strategies: (based on used indicators)
Strategy: BigZ04_TSL1, Similarity Score: 96.3%
Strategy: BigZ04_TSL3, Similarity Score: 96.3%
Strategy: BigZ04_TSL3_2, Similarity Score: 96.3%
Strategy: BigZ04_TSL4, Similarity Score: 96.3%
Strategy: BigZ04_TSL4_2, Similarity Score: 96.3%
Strategy: BigZ04_TSL4_24, Similarity Score: 96.3%
Strategy: BigZ04_TSL4_69, Similarity Score: 96.3%
Strategy: BigZ04_TSL4_761, Similarity Score: 96.3%
Strategy: Discord_BigZ04_TSL3, Similarity Score: 96.3%
Strategy: Discord_BigZ04_TSL3a, Similarity Score: 96.3%
Strategy: big, Similarity Score: 96.3%
Strategy: big_3, Similarity Score: 96.3%
Strategy: BigZ04, Similarity Score: 88.89%
Strategy: BigZ04HO, Similarity Score: 88.89%
Strategy: BigZ04HO2, Similarity Score: 88.89%
Strategy: BigZ04_2, Similarity Score: 88.89%
Strategy: BigZ04_4, Similarity Score: 88.89%
Strategy: BigZ04_6, Similarity Score: 88.89%
Strategy: BigZ04_845, Similarity Score: 88.89%
Strategy: BigZ06, Similarity Score: 88.89%
last change: 2023-07-03 18:37:39