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The MACDStrategy is a trading strategy implemented in Python for backtesting purposes. The strategy is designed to identify uptrends and downtrends in the market using two indicators: Moving Average Convergence Divergence (MACD) and Commodity Channel Index (CCI). Here's a breakdown of how the strategy works:
Uptrend Definition:
MACD (macd) is above MACD signal (macdsignal).
CCI (cci) is less than or equal to -50.
Downtrend Definition: MACD (macd) is below MACD signal (macdsignal).
CCI (cci) is greater than or equal to 100. The strategy calculates these indicators using the provided dataframe, which contains historical price data. It adds the calculated values of macd, macdsignal, macdhist, and cci to the dataframe. The strategy also defines the minimal return on investment (ROI) and stop loss values. The minimal_roi attribute sets the desired ROI for different time intervals, and the stoploss attribute sets the optimal stop loss level. The ticker_interval attribute specifies the desired interval for ticker data, which is set to '5m' (5 minutes) in this case. The strategy provides two methods, populate_buy_trend() and populate_sell_trend(), to populate the buy and sell signals, respectively, based on the calculated indicators. In the populate_buy_trend() method, a buy signal is generated when the macd is greater than macdsignal and the cci is less than or equal to -50. Similarly, in the populate_sell_trend() method, a sell signal is generated when the macd is less than macdsignal and the cci is greater than or equal to 100. By using this strategy and backtesting it on historical data, you can evaluate its performance and make informed decisions about its potential effectiveness in real trading scenarios.
CCI (cci) is less than or equal to -50.
Downtrend Definition: MACD (macd) is below MACD signal (macdsignal).
CCI (cci) is greater than or equal to 100. The strategy calculates these indicators using the provided dataframe, which contains historical price data. It adds the calculated values of macd, macdsignal, macdhist, and cci to the dataframe. The strategy also defines the minimal return on investment (ROI) and stop loss values. The minimal_roi attribute sets the desired ROI for different time intervals, and the stoploss attribute sets the optimal stop loss level. The ticker_interval attribute specifies the desired interval for ticker data, which is set to '5m' (5 minutes) in this case. The strategy provides two methods, populate_buy_trend() and populate_sell_trend(), to populate the buy and sell signals, respectively, based on the calculated indicators. In the populate_buy_trend() method, a buy signal is generated when the macd is greater than macdsignal and the cci is less than or equal to -50. Similarly, in the populate_sell_trend() method, a sell signal is generated when the macd is less than macdsignal and the cci is greater than or equal to 100. By using this strategy and backtesting it on historical data, you can evaluate its performance and make informed decisions about its potential effectiveness in real trading scenarios.
startup_candle_count : 30 Recursive Analysis found no issues while using 30 startup_candle_count
stoploss: -0.04 timeframe: 5m hash(sha256): d2ddf837cb605ff7da1c38cf13bec1bce0ef3c8e2628eab81757bb6fcfe73d94 indicators: macdsignal macd cci macdhist
No similar strategies found. (based on used indicators)
last change: 2023-08-02 12:41:10