The NASOSv5 strategy is a trading strategy implemented as a class in Python. It follows a backtesting approach for evaluating its performance. Here is a short description of what the strategy does:
The populate_indicators method is responsible for generating and merging various technical indicators based on the provided input data (dataframe) and metadata.
It prepares the data for further analysis and decision-making.
The populate_buy_trend method identifies potential buying opportunities based on specific conditions.
It checks for multiple criteria, such as the maximum price of the last few candles being below a certain threshold, the relative strength index (RSI) being below a specified value, the close price being below a moving average multiplied by an offset value, the Elder's Force Index (EWO) being above a certain threshold, and the volume being greater than zero. It assigns a "buy" signal and a corresponding tag to the qualifying data points. The populate_sell_trend method determines potential selling opportunities based on certain conditions. It checks if the close price is above a simple moving average (SMA) and a moving average multiplied by an offset value, the RSI is above 50, the volume is greater than zero, and the fast RSI is greater than the slow RSI. Alternatively, it also checks if the close price is below a Hull Moving Average (HMA) and above a moving average multiplied by an offset value, the volume is greater than zero, and the fast RSI is greater than the slow RSI. It assigns a "sell" signal to the qualifying data points. Overall, the NASOSv5 strategy aims to identify buy and sell signals based on a combination of technical indicators, moving averages, RSI, EWO, and volume. These signals can be used to develop trading strategies and backtest their performance on historical data.
It prepares the data for further analysis and decision-making.
The populate_buy_trend method identifies potential buying opportunities based on specific conditions.
It checks for multiple criteria, such as the maximum price of the last few candles being below a certain threshold, the relative strength index (RSI) being below a specified value, the close price being below a moving average multiplied by an offset value, the Elder's Force Index (EWO) being above a certain threshold, and the volume being greater than zero. It assigns a "buy" signal and a corresponding tag to the qualifying data points. The populate_sell_trend method determines potential selling opportunities based on certain conditions. It checks if the close price is above a simple moving average (SMA) and a moving average multiplied by an offset value, the RSI is above 50, the volume is greater than zero, and the fast RSI is greater than the slow RSI. Alternatively, it also checks if the close price is below a Hull Moving Average (HMA) and above a moving average multiplied by an offset value, the volume is greater than zero, and the fast RSI is greater than the slow RSI. It assigns a "sell" signal to the qualifying data points. Overall, the NASOSv5 strategy aims to identify buy and sell signals based on a combination of technical indicators, moving averages, RSI, EWO, and volume. These signals can be used to develop trading strategies and backtest their performance on historical data.
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/NASOSv5_16.py", line 250, in populate_indicators informative_15m = self.informative_15m_indicators(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/NASOSv5_16.py", line 216, in informative_15m_indicators informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50) ^^^^^^^^^^^^^^ NameError: name 'informative_1h' is not defined
stoploss: -0.168 timeframe: 5m hash(sha256): 18d329645e7a664aca7c777e83315187348d58de53d3be652cf5a878d07d565a indicators: rsi_buy EWO ema_200 ewo_high ema_50 ewo_low high_offset_2 close ewo_high_2 sell_signal profit_threshold rsi_fast ma_sell_val ma_buy_val volume low_offset retries high_offset low_offset_2 base_nb_candles_buy rsi_fast_buy ___retries base_nb_candles_sell hma_50 buy buy_tag sma_9 ema_100 close_15m lookback_candles max_slippage rsi_slow rsi low
Similar Strategies: (based on used indicators)
Strategy: Discord_2_NASOSv5, Similarity Score: 97.14%
Strategy: NASOSv5_antipump, Similarity Score: 97.14%
Strategy: NASOSv5_antipump_625, Similarity Score: 97.14%
Strategy: Nv5AP, Similarity Score: 97.14%
Strategy: NASOSv5, Similarity Score: 91.43%
Strategy: NASOSv5_mod1_DanMod, Similarity Score: 91.43%
Strategy: NASOSv5sera, Similarity Score: 91.43%
Strategy: NASOSvSH1, Similarity Score: 91.43%
Strategy: NHXv8, Similarity Score: 91.43%
Strategy: NHZv1, Similarity Score: 91.43%
Strategy: Salvation, Similarity Score: 91.43%
Strategy: min5v5, Similarity Score: 91.43%
Strategy: nasosv5, Similarity Score: 91.43%
Strategy: Discord_1_NASOSv5, Similarity Score: 88.57%
Strategy: NASOSv5_mod3, Similarity Score: 88.57%
Strategy: NASOSvX, Similarity Score: 88.57%
Strategy: NM1, Similarity Score: 88.57%
Strategy: NM2, Similarity Score: 88.57%
Strategy: SMAOffsetX1, Similarity Score: 88.57%
Strategy: tesla4, Similarity Score: 88.57%
Strategy: vinfast4, Similarity Score: 88.57%
Strategy: vinfast5, Similarity Score: 88.57%
Strategy: Discord_2_NASOSv4, Similarity Score: 85.71%
Strategy: Discord_3_NASOSv4, Similarity Score: 85.71%
Strategy: NASOSv4, Similarity Score: 85.71%
Strategy: NASOSv4HO, Similarity Score: 85.71%
Strategy: NASOSv4JP, Similarity Score: 85.71%
Strategy: NASOSv4N1, Similarity Score: 85.71%
Strategy: NASOSv4_2, Similarity Score: 85.71%
Strategy: NASOSv4_3, Similarity Score: 85.71%
Strategy: NASOSv4_563, Similarity Score: 85.71%
Strategy: NASOSv4_SMA, Similarity Score: 85.71%
Strategy: NASOSv4op, Similarity Score: 85.71%
Strategy: NASOSv5_marc, Similarity Score: 85.71%
Strategy: NASOSv6, Similarity Score: 85.71%
Strategy: NASOSv7, Similarity Score: 85.71%
Strategy: NASREZ, Similarity Score: 85.71%
Strategy: NBO, Similarity Score: 85.71%
Strategy: NBO_2, Similarity Score: 85.71%
Strategy: NHXv1, Similarity Score: 85.71%
Strategy: NHXv1test, Similarity Score: 85.71%
Strategy: NHXv815, Similarity Score: 85.71%
Strategy: NHXv83, Similarity Score: 85.71%
Strategy: NHZv2, Similarity Score: 85.71%
Strategy: SMAOffsetN1, Similarity Score: 85.71%
Strategy: Tesla4_2, Similarity Score: 85.71%
Strategy: GG_lambo, Similarity Score: 82.86%
Strategy: NASOSv5ho, Similarity Score: 82.86%
Strategy: NASOSv8, Similarity Score: 82.86%
Strategy: NM1v2, Similarity Score: 82.86%
Strategy: NM1v2b, Similarity Score: 82.86%
Strategy: NM5, Similarity Score: 82.86%
Strategy: NM5v2, Similarity Score: 82.86%
Strategy: NM5vtest, Similarity Score: 82.86%
Strategy: NM5vtest2, Similarity Score: 82.86%
Strategy: NM5vtest23, Similarity Score: 82.86%
Strategy: Offset_v10, Similarity Score: 82.86%
Strategy: Offset_v11, Similarity Score: 82.86%
Strategy: Offset_v2, Similarity Score: 82.86%
Strategy: Offset_v3, Similarity Score: 82.86%
Strategy: Offset_v4, Similarity Score: 82.86%
Strategy: Offset_v5, Similarity Score: 82.86%
Strategy: Offset_v6, Similarity Score: 82.86%
Strategy: Offset_v7, Similarity Score: 82.86%
Strategy: Offset_v8, Similarity Score: 82.86%
Strategy: Offset_v9, Similarity Score: 82.86%
Strategy: SMAOffsetX, Similarity Score: 82.86%
Strategy: SMAOffsetX2, Similarity Score: 82.86%
Strategy: tesla_4, Similarity Score: 82.86%
Strategy: tesla_5, Similarity Score: 82.86%
Strategy: tesla_6, Similarity Score: 82.86%
Strategy: test4, Similarity Score: 82.86%
last change: 2024-07-28 01:08:15