The Nasos_Protect trading strategy is designed to backtest various trading scenarios using technical indicators. Here's a breakdown of what this strategy does:
Indicators Calculation (populate_indicators):
Calculates Exponential Moving Averages (EMAs) for different time periods (ma_buy_{val} and ma_sell_{val}). Computes the Hull Moving Average (hma_50) with a window size of 50.
Calculates the Exponential Moving Average (ema_100) with a time period of 100.
Computes the Simple Moving Average (sma_9) with a time period of 9.
Calculates the Elder's Force Index (EWO) using specified parameters. Computes the Relative Strength Index (rsi) with a time period of 14, as well as faster (rsi_fast) and slower (rsi_slow) versions. Computes the Stochastic Oscillator's fast %K and %D values (fastk and fastd). Calculates the Chande Trend Index (cti) and its rolling mean (cti_mean). Computes the Chande Momentum Oscillator (cmo) with a time period of 24. Buy Trend Conditions (populate_buy_trend): The strategy establishes conditions for potential buy signals based on several criteria: Fast RSI is below 35. Closing price is below a specific EMA multiplied by a low offset. EWO is above a certain threshold. RSI is below a specified level. Volume is greater than 0. Closing price is below a specific EMA multiplied by a high offset. The conditions are defined in three variations ('ewo1', 'ewo2', and 'ewolow'), each with different combinations of the above criteria. Sell Trend Conditions (populate_sell_trend): The strategy establishes conditions for potential sell signals: If the closing price is above both the 9-day Simple Moving Average and a specific EMA multiplied by a high offset, and RSI is above 50, and volume is greater than 0, and fast RSI is greater than slow RSI. If the closing price is below the Hull Moving Average, but above a specific EMA multiplied by a high offset, and volume is greater than 0, and fast RSI is greater than slow RSI. The strategy uses these conditions to generate buy and sell signals based on the calculated indicators and specified criteria, which can be used for backtesting different trading scenarios.
Calculates the Exponential Moving Average (ema_100) with a time period of 100.
Computes the Simple Moving Average (sma_9) with a time period of 9.
Calculates the Elder's Force Index (EWO) using specified parameters. Computes the Relative Strength Index (rsi) with a time period of 14, as well as faster (rsi_fast) and slower (rsi_slow) versions. Computes the Stochastic Oscillator's fast %K and %D values (fastk and fastd). Calculates the Chande Trend Index (cti) and its rolling mean (cti_mean). Computes the Chande Momentum Oscillator (cmo) with a time period of 24. Buy Trend Conditions (populate_buy_trend): The strategy establishes conditions for potential buy signals based on several criteria: Fast RSI is below 35. Closing price is below a specific EMA multiplied by a low offset. EWO is above a certain threshold. RSI is below a specified level. Volume is greater than 0. Closing price is below a specific EMA multiplied by a high offset. The conditions are defined in three variations ('ewo1', 'ewo2', and 'ewolow'), each with different combinations of the above criteria. Sell Trend Conditions (populate_sell_trend): The strategy establishes conditions for potential sell signals: If the closing price is above both the 9-day Simple Moving Average and a specific EMA multiplied by a high offset, and RSI is above 50, and volume is greater than 0, and fast RSI is greater than slow RSI. If the closing price is below the Hull Moving Average, but above a specific EMA multiplied by a high offset, and volume is greater than 0, and fast RSI is greater than slow RSI. The strategy uses these conditions to generate buy and sell signals based on the calculated indicators and specified criteria, which can be used for backtesting different trading scenarios.
Traceback (most recent call last): File "/freqtrade/freqtrade/main.py", line 42, in main return_code = args['func'](args) ^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/commands/optimize_commands.py", line 58, in start_backtesting backtesting.start() File "/freqtrade/freqtrade/optimize/backtesting.py", line 1401, in start min_date, max_date = self.backtest_one_strategy(strat, data, timerange) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/optimize/backtesting.py", line 1318, in backtest_one_strategy preprocessed = self.strategy.advise_all_indicators(data) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1378, in advise_all_indicators return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1378, inreturn {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1410, in advise_indicators return self.populate_indicators(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/Nasos_Protect.py", line 221, in populate_indicators dataframe['cti'] = pta.cti(dataframe["close"], length=20) ^^^ NameError: name 'pta' is not defined
stoploss: -0.028 timeframe: 5m hash(sha256): accea073cecb7ca93a543692c3fe4f649e58cfb49c63c98eff3271963bf4a2fc indicators: rsi_buy EWO ewo_high ewo_low high_offset_2 close cti_mean ewo_high_2 sell_signal fastk rsi_fast ma_sell_val ma_buy_val volume low_offset retries high_offset low_offset_2 fastd base_nb_candles_buy fast_d ___retries base_nb_candles_sell hma_50 buy buy_tag cti sma_9 ema_100 max_slippage rsi_slow rsi fast_k low cmo
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last change: 2024-08-03 03:03:12