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The Hacklemore3 strategy is a trading strategy that utilizes several indicators and conditions to determine when to enter and exit trades. Here is a short description of what the strategy does:
Populate Indicators: This function calculates various indicators based on the provided dataframe, including the slow moving average of volume, the RMI (Relative Momentum Index), SAR (Stop and Reverse), rolling maximum and minimum prices, and trend indicators. It returns the modified dataframe with these indicators added.
Populate Buy Trend: This function determines the conditions for entering a buy trade.
It checks for various conditions such as an upward trend, RMI above a threshold, increasing RMI values, rising closing prices, SAR below the closing price, and low volume compared to the previous slow moving average.
If all the conditions are met, the 'buy' column in the dataframe is set to 1. Populate Sell Trend: This function determines the conditions for exiting a buy trade. If there is an active trade, it calculates the current profit ratio and compares it with certain thresholds. If the profit is negative, it checks for a downward trend, RMI below a threshold, and positive volume. If the profit is positive, it checks for the current price exceeding a certain percentage of the maximum price, decreasing closing prices, and decreasing high prices. If any of these conditions are met, the 'sell' column in the dataframe is set to 1. The code snippets at the end seem to be unrelated to the main strategy and may be part of other functions or classes.
Populate Buy Trend: This function determines the conditions for entering a buy trade.
It checks for various conditions such as an upward trend, RMI above a threshold, increasing RMI values, rising closing prices, SAR below the closing price, and low volume compared to the previous slow moving average.
If all the conditions are met, the 'buy' column in the dataframe is set to 1. Populate Sell Trend: This function determines the conditions for exiting a buy trade. If there is an active trade, it calculates the current profit ratio and compares it with certain thresholds. If the profit is negative, it checks for a downward trend, RMI below a threshold, and positive volume. If the profit is positive, it checks for the current price exceeding a certain percentage of the maximum price, decreasing closing prices, and decreasing high prices. If any of these conditions are met, the 'sell' column in the dataframe is set to 1. The code snippets at the end seem to be unrelated to the main strategy and may be part of other functions or classes.
startup_candle_count : 50 rmi: -13.860%
stoploss: -0.99 timeframe: 5m hash(sha256): 212cb501946658afc3a6df39f69545087390846bd83761df2bc1f9a906ddb15e indicators: min high dn_trend upper volume rmi lower bids asks up_trend close price max runmode low volume_mean_slow sar
Similar Strategies: (based on used indicators)
Strategy: Enchilada, Similarity Score: 94.44%
Strategy: Hacklemore, Similarity Score: 94.44%
Strategy: HacklemoreX, Similarity Score: 94.44%
last change: 2023-07-01 13:34:34