Strategy: TaSearch5m_2
Downloaded: 20221105
Stoploss: -0.05
Downloaded: 20221105
Stoploss: -0.05
The "TaSearch5m" strategy is a trading strategy implemented in Python using the Freqtrade library. It is designed to backtest trading strategies on 5-minute timeframe data. Here is a breakdown of what the strategy does:
The strategy uses the "TaSearch" class for technical analysis calculations.
It initializes two variables, "n" and "p," which are set to 144 and 2, respectively.
The "minimal_roi" variable defines the minimum return on investment required for a trade to be considered successful.
In this case, it is set to 0.01 (1%). The "stoploss" variable represents the maximum acceptable loss for a trade before it is stopped. In this case, it is set to -0.05 (-5%). The "populate_indicators" method is responsible for populating the technical indicators of the DataFrame. It renames the columns of the DataFrame and calls the "find_extremes" method from the "TaSearch" class. The "buy_past_rsi" method identifies buying opportunities based on the past behavior of the RSI (Relative Strength Index) indicator. It iterates over the DataFrame rows in reverse order and checks if the "ex_min_percentage" value is less than the negative value of "p." If so, it counts the occurrences of RSI values below 30 in the previous 48 rows and assigns a count to the "buy_past_rsi" column. The "buy_stride" method identifies buying opportunities based on the current RSI value and the occurrence of extreme minimum percentage values in the past. It iterates over the DataFrame rows in reverse order and checks if the RSI value is between 25 and 40. If so, it looks for occurrences of extreme minimum percentage values less than the negative value of "p" in the previous 24 rows. If found, it calculates the difference between the current row index and the found row index and assigns it to the "buy_stride" column. It also assigns the corresponding "buy_past_rsi" value to the current row. The "populate_buy_trend" method populates the "buy" column of the DataFrame based on certain conditions. It sets the "buy" value to 1 if both the "buy_stride" and "buy_past_rsi" values meet specific criteria. The "populate_sell_trend" method populates the "sell" column of the DataFrame based on the RSI value. It sets the "sell" value to 1 if the RSI value is greater than 70. Overall, the strategy identifies potential buying opportunities based on the past behavior of the RSI indicator and the occurrence of extreme minimum percentage values. It sets the "buy" flag when specific criteria are met and the "sell" flag when the RSI value is high. These flags can be used to generate trading signals for further analysis and decision-making.
It initializes two variables, "n" and "p," which are set to 144 and 2, respectively.
The "minimal_roi" variable defines the minimum return on investment required for a trade to be considered successful.
In this case, it is set to 0.01 (1%). The "stoploss" variable represents the maximum acceptable loss for a trade before it is stopped. In this case, it is set to -0.05 (-5%). The "populate_indicators" method is responsible for populating the technical indicators of the DataFrame. It renames the columns of the DataFrame and calls the "find_extremes" method from the "TaSearch" class. The "buy_past_rsi" method identifies buying opportunities based on the past behavior of the RSI (Relative Strength Index) indicator. It iterates over the DataFrame rows in reverse order and checks if the "ex_min_percentage" value is less than the negative value of "p." If so, it counts the occurrences of RSI values below 30 in the previous 48 rows and assigns a count to the "buy_past_rsi" column. The "buy_stride" method identifies buying opportunities based on the current RSI value and the occurrence of extreme minimum percentage values in the past. It iterates over the DataFrame rows in reverse order and checks if the RSI value is between 25 and 40. If so, it looks for occurrences of extreme minimum percentage values less than the negative value of "p" in the previous 24 rows. If found, it calculates the difference between the current row index and the found row index and assigns it to the "buy_stride" column. It also assigns the corresponding "buy_past_rsi" value to the current row. The "populate_buy_trend" method populates the "buy" column of the DataFrame based on certain conditions. It sets the "buy" value to 1 if both the "buy_stride" and "buy_past_rsi" values meet specific criteria. The "populate_sell_trend" method populates the "sell" column of the DataFrame based on the RSI value. It sets the "sell" value to 1 if the RSI value is greater than 70. Overall, the strategy identifies potential buying opportunities based on the past behavior of the RSI indicator and the occurrence of extreme minimum percentage values. It sets the "buy" flag when specific criteria are met and the "sell" flag when the RSI value is high. These flags can be used to generate trading signals for further analysis and decision-making.
Unable to parse Traceback (Logfile Exceeded Limit)
stoploss: -0.05 timeframe: 5m hash(sha256): fc60e720407a758eec4db6f9afa09408fd0938ce956eef64f82bdf395a8d2835
Was not able to fetch indicators from Strategyfile.
last change: 2024-07-27 23:41:32
Freqtrade Strategy Ninja
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX