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The strategy, named "CombinedBinHAndClucV8XH," is designed for backtesting trading strategies. It consists of three main components: indicator population, buy trend determination, and sell trend determination. Indicator Population:
The strategy starts by populating various indicators using the provided DataFrame and metadata.
It merges informative indicators from the 1-hour timeframe with the current timeframe, ensuring data continuity.
Buy Trend Determination: The strategy defines multiple buy conditions, each represented as a set of logical expressions.
These conditions involve comparisons between different price indicators and moving averages. The conditions also consider factors such as price dips, volume, and relative strength index (RSI). If any of the buy conditions are met, the corresponding row in the DataFrame is marked as a buy signal. Sell Trend Determination: Similar to the buy trend determination, the strategy defines sell conditions. These conditions involve comparisons between the closing price and the upper Bollinger Band, as well as the RSI. If any of the sell conditions are met, the corresponding row in the DataFrame is marked as a sell signal. Overall, the strategy aims to generate buy and sell signals based on a combination of technical indicators, price patterns, and volume analysis. It provides a systematic approach for evaluating trading strategies during backtesting.
It merges informative indicators from the 1-hour timeframe with the current timeframe, ensuring data continuity.
Buy Trend Determination: The strategy defines multiple buy conditions, each represented as a set of logical expressions.
These conditions involve comparisons between different price indicators and moving averages. The conditions also consider factors such as price dips, volume, and relative strength index (RSI). If any of the buy conditions are met, the corresponding row in the DataFrame is marked as a buy signal. Sell Trend Determination: Similar to the buy trend determination, the strategy defines sell conditions. These conditions involve comparisons between the closing price and the upper Bollinger Band, as well as the RSI. If any of the sell conditions are met, the corresponding row in the DataFrame is marked as a sell signal. Overall, the strategy aims to generate buy and sell signals based on a combination of technical indicators, price patterns, and volume analysis. It provides a systematic approach for evaluating trading strategies during backtesting.
startup_candle_count : 200 ema_50_1h: 0.001% ema_100_1h: -0.041%
stoploss: -0.99 timeframe: 5m hash(sha256): c31f2e1a690ff03d2020b0755754bfe070fece8b44692fa02ed0cf7ce7b745cf indicators: sma_200_1h upper ema_200 ema_50 close sma_5 tail bb_lowerband mfi ema_200_1h bbdelta volume smaHigh ATR ema_100_1h ssl_up closedelta sslDown open hlv volume_mean_slow ema_50_1h smaLow sma_200 high sslUp mid ssl_down_1h ssl_down rsi_1h ema_100 lower ema_12 bb_middleband rsi sma_200_dec bb_upperband ema_slow ema_26 low ssl_up_1h sma_200_dec_1h
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last change: 2023-11-02 22:55:00