The provided code seems to define a trading strategy class named Nv2. This strategy involves populating various indicators and conditions for executing buy and sell signals in a trading context. Here's a short description of what the strategy does:
The populate_indicators function calculates and adds several technical indicators to the input DataFrame.
These indicators include moving averages (ma_buy and ma_sell), Hull Moving Average (hma_50), Exponential Moving Average (ema_100), Simple Moving Average (sma_9), Elliott Wave Oscillator (EWO), Relative Strength Index (rsi, rsi_fast, rsi_slow), and others.
These indicators are used as inputs for making trading decisions.
The populate_buy_trend function defines buy conditions based on combinations of indicators. The conditions are checked against the DataFrame's data, and if met, a "buy_tag" is added to the respective rows. The conditions involve factors such as RSI values, close prices compared to moving averages, EWO values, and volume thresholds. The populate_sell_trend function defines sell conditions. Similar to the buy conditions, these are based on various technical indicators, including close prices relative to moving averages, RSI values, volume thresholds, and the relationship between different moving averages. If the defined conditions are met, a "sell" signal is marked in the DataFrame. In summary, the Nv2 trading strategy involves calculating a range of technical indicators and then making buy and sell decisions based on combinations of these indicators. The strategy aims to capture potential market trends and reversals by evaluating the conditions specified in the code.
These indicators include moving averages (ma_buy and ma_sell), Hull Moving Average (hma_50), Exponential Moving Average (ema_100), Simple Moving Average (sma_9), Elliott Wave Oscillator (EWO), Relative Strength Index (rsi, rsi_fast, rsi_slow), and others.
These indicators are used as inputs for making trading decisions.
The populate_buy_trend function defines buy conditions based on combinations of indicators. The conditions are checked against the DataFrame's data, and if met, a "buy_tag" is added to the respective rows. The conditions involve factors such as RSI values, close prices compared to moving averages, EWO values, and volume thresholds. The populate_sell_trend function defines sell conditions. Similar to the buy conditions, these are based on various technical indicators, including close prices relative to moving averages, RSI values, volume thresholds, and the relationship between different moving averages. If the defined conditions are met, a "sell" signal is marked in the DataFrame. In summary, the Nv2 trading strategy involves calculating a range of technical indicators and then making buy and sell decisions based on combinations of these indicators. The strategy aims to capture potential market trends and reversals by evaluating the conditions specified in the code.
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 1335, in backtest_one_strategy results = self.backtest( ^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/optimize/backtesting.py", line 1213, in backtest data: Dict = self._get_ohlcv_as_lists(processed) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/optimize/backtesting.py", line 381, in _get_ohlcv_as_lists df_analyzed = self.strategy.ft_advise_signals(pair_data, {'pair': pair}) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1391, in ft_advise_signals dataframe = self.advise_entry(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1425, in advise_entry df = self.populate_entry_trend(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 225, in populate_entry_trend return self.populate_buy_trend(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/Nv2.py", line 225, in populate_buy_trend (dataframe['cti_mean'] > 0.4) TypeError: 'Series' object is not callable
stoploss: -0.04 timeframe: 5m hash(sha256): 8f2539d8b10423d7a1981e3b143a205c5de8b531717bcf9e29495d02b0b17c0a indicators: rsi_buy EWO ewo_high ewo_low high_offset_2 close cti_mean ewo_high_2 fastk rsi_fast ma_sell_val ma_buy_val volume low_offset high_offset low_offset_2 fastd base_nb_candles_buy fast_d base_nb_candles_sell hma_50 buy buy_tag cti sma_9 ema_100 rsi_slow rsi runmode fast_k low cmo
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last change: 2024-05-05 16:53:47