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This strategy, version 1.0, is designed for backtesting trading strategies on a website. Here's a short description of what the strategy does:
The strategy uses various technical indicators to make buy and sell decisions. It operates on a daily timeframe and has a stoploss of -0.2 (20% loss).
The minimal return on investment (ROI) is set to 0.6 (60%).
The strategy imports the necessary libraries, including talib for technical analysis and qtpylib for indicators.
It implements the "kcx_strat_prod_plot" class, which is derived from the IStrategy interface. The strategy calculates several indicators, including the Simple Moving Average (SMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), Slow Stochastic, and Keltner Channel. These indicators are used to generate buy and sell signals. In terms of plotting, the strategy creates a main plot that includes the Keltner band with upper, lower, and middle bands. It also creates subplots for the MACD and Stochastics indicators. The "populate_indicators" function populates the indicators in the DataFrame. It calculates SMA, MACD, RSI, Slow Stochastic, and Keltner Channel values. The "populate_buy_trend" function determines the buy signal conditions. It triggers a buy signal when the close price is above the Keltner upper band and the MACD histogram is positive. The "populate_sell_trend" function determines the sell signal conditions. It triggers a sell signal when the close price is below the Keltner middle band or when the MACD histogram is negative and the Slow Stochastic value is below 50. Overall, this strategy aims to generate buy signals when the price is above the upper Keltner band and the MACD is positive, and sell signals when the price is below the middle Keltner band or when the MACD is negative and the Slow Stochastic is below 50. Please note that this is a brief description, and there may be additional details or considerations within the strategy code that are not covered here.
The minimal return on investment (ROI) is set to 0.6 (60%).
The strategy imports the necessary libraries, including talib for technical analysis and qtpylib for indicators.
It implements the "kcx_strat_prod_plot" class, which is derived from the IStrategy interface. The strategy calculates several indicators, including the Simple Moving Average (SMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), Slow Stochastic, and Keltner Channel. These indicators are used to generate buy and sell signals. In terms of plotting, the strategy creates a main plot that includes the Keltner band with upper, lower, and middle bands. It also creates subplots for the MACD and Stochastics indicators. The "populate_indicators" function populates the indicators in the DataFrame. It calculates SMA, MACD, RSI, Slow Stochastic, and Keltner Channel values. The "populate_buy_trend" function determines the buy signal conditions. It triggers a buy signal when the close price is above the Keltner upper band and the MACD histogram is positive. The "populate_sell_trend" function determines the sell signal conditions. It triggers a sell signal when the close price is below the Keltner middle band or when the MACD histogram is negative and the Slow Stochastic value is below 50. Overall, this strategy aims to generate buy signals when the price is above the upper Keltner band and the MACD is positive, and sell signals when the price is below the middle Keltner band or when the MACD is negative and the Slow Stochastic is below 50. Please note that this is a brief description, and there may be additional details or considerations within the strategy code that are not covered here.
stoploss: -0.2 timeframe: 5m hash(sha256): 6147cb1f703a4d679f0872bd94fb86676572f1c72e0b238a71bccbadb1251c3a
Was not able to fetch indicators from Strategyfile.
last change: 2022-07-02 19:54:08