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Strategy: mabStra_2
Downloaded: 20220724
Stoploss: -0.128


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The mabStra strategy is a trading strategy implemented in Python using the Freqtrade library. It is designed to be used for backtesting trading strategies on financial data. The strategy uses various moving average indicators to generate buy and sell signals.

Here are the key components of the strategy: The strategy calculates several moving averages, including buy-mojoMA, buy-fastMA, buy-slowMA, sell-mojoMA, sell-fastMA, and sell-slowMA, using the ta.SMA function from the TA-Lib library.

For the buy signal, the strategy checks the following conditions: The ratio of buy-mojoMA to buy-fastMA is greater than buy_div_min and less than buy_div_max.

The ratio of buy-fastMA to buy-slowMA is greater than buy_div_min and less than buy_div_max. If these conditions are met, a buy signal is generated. For the sell signal, the strategy checks the following conditions: The ratio of sell-fastMA to sell-mojoMA is greater than sell_div_min and less than sell_div_max. The ratio of sell-slowMA to sell-fastMA is greater than sell_div_min and less than sell_div_max. If these conditions are met, a sell signal is generated. The strategy also defines a minimal ROI (return on investment) table, which specifies the desired returns at different stages of the trade. It sets a stop loss value (stoploss) to limit potential losses. The strategy operates on a 4-hour timeframe (timeframe). Overall, the mabStra strategy uses moving average ratios to determine optimal entry and exit points for trades. It aims to capture profitable trading opportunities based on these indicators.

startup_candle_count : 50
Recursive Analysis found no issues while using 50 startup_candle_count
Unable to parse Traceback (Logfile Exceeded Limit)
stoploss: -0.128
timeframe: 4h
hash(sha256): 42259ffa018090f156256b03865cd2c11cc8c62c837e6acf83ca3fb252e780c4
indicators:
buymojoMA buyfastMA buyslowMA sellfastMA sellslowMA
sellmojoMA

No similar strategies found. (based on used indicators)

last change: 2024-08-02 08:00:01