Not Enough Data to display!
Average Overall
| Not Enough Data! / Avg statistics not populated yet. |
|---|
The BigZ04_TSL4 strategy is designed for backtesting trading strategies. Here's a brief description of what the strategy does:
The strategy consists of two main methods: populate_indicators and populate_buy_trend. In the populate_indicators method, various indicators are calculated and added to the input dataframe.
These indicators are derived from different timeframes and are used to provide additional information for the strategy.
In the populate_buy_trend method, the strategy defines several buy conditions using a series of logical statements.
These conditions are checked against the data in the dataframe to determine whether a buy signal should be generated. Here are some important parts of the buy conditions: Condition 1: Checks if the close price is above the 200-day exponential moving average (ema_200), the 200-day exponential moving average of the 1-hour timeframe (ema_200_1h), and certain price levels derived from Bollinger Bands. It also considers the relative strength index (rsi_1h), volume factors, and price patterns. Condition 2: Considers the close price, Bollinger Bands, and volume factors to determine if a buy signal should be generated. Condition 3: Looks at the close price, Bollinger Bands, relative strength index (rsi), and volume factors to determine the buy signal. Condition 4: Considers the 1-hour timeframe relative strength index (rsi_1h), close price, Bollinger Bands, and volume factors to generate a buy signal. Condition 5: Checks if the close price is above the 200-day exponential moving average (ema_200) and the 200-day exponential moving average of the 1-hour timeframe (ema_200_1h). It also considers the relationship between the exponential moving averages (ema_26 and ema_12), the open price, MACD indicator, Bollinger Bands, and volume factors. These are some of the key components of the buy conditions implemented in the strategy. The strategy likely generates buy signals based on these conditions when the criteria are met.
These indicators are derived from different timeframes and are used to provide additional information for the strategy.
In the populate_buy_trend method, the strategy defines several buy conditions using a series of logical statements.
These conditions are checked against the data in the dataframe to determine whether a buy signal should be generated. Here are some important parts of the buy conditions: Condition 1: Checks if the close price is above the 200-day exponential moving average (ema_200), the 200-day exponential moving average of the 1-hour timeframe (ema_200_1h), and certain price levels derived from Bollinger Bands. It also considers the relative strength index (rsi_1h), volume factors, and price patterns. Condition 2: Considers the close price, Bollinger Bands, and volume factors to determine if a buy signal should be generated. Condition 3: Looks at the close price, Bollinger Bands, relative strength index (rsi), and volume factors to determine the buy signal. Condition 4: Considers the 1-hour timeframe relative strength index (rsi_1h), close price, Bollinger Bands, and volume factors to generate a buy signal. Condition 5: Checks if the close price is above the 200-day exponential moving average (ema_200) and the 200-day exponential moving average of the 1-hour timeframe (ema_200_1h). It also considers the relationship between the exponential moving averages (ema_26 and ema_12), the open price, MACD indicator, Bollinger Bands, and volume factors. These are some of the key components of the buy conditions implemented in the strategy. The strategy likely generates buy signals based on these conditions when the criteria are met.
stoploss: -0.99 timeframe: 5m hash(sha256): ab53e5ca85b7bb9d64da8aeeaf912f3062c2d552dac7ec00e35fd3ef55fe39d3 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_TSL2, 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_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 16:26:40