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The given strategy, called ViN (Value in Numbers), is designed to populate indicators, determine buy signals, and calculate sell signals for backtesting trading strategies. Here's a short description of what the strategy does:
The strategy first populates various indicators based on the provided DataFrame and metadata. These indicators include adjusted high and low prices, adjusted close-to-open change, streaks of consecutive up or down movements, minimum and maximum streak values, Bollinger Bands, and volume averages.
Next, the strategy identifies buy signals based on specific conditions.
For buy signals related to percentage change (pct), it checks conditions such as the number of days a stock has been listed, volume being greater than the average, streaks of minimum and maximum values, and certain percentage change thresholds.
For buy signals related to the lc2 indicator, it considers conditions like the number of days listed, volume being greater than the average, streaks of maximum values, and specific ratios involving lc2 and close prices. After identifying buy signals, the strategy populates tags indicating the type of buy signal. It uses the "buy_tag_pct" and "buy_tag_lc2" columns to represent the respective signals. If the strategy is "vin," it combines both types of signals. When determining sell signals, the strategy analyzes the current profit and trade information. It retrieves the relevant DataFrame for the trade, calculates the trade's length, checks recent buy signals, and considers streak values. If certain conditions are met, the strategy returns a sell signal. Overall, the ViN strategy focuses on identifying potential buying opportunities based on various indicators and conditions, while also considering sell signals based on trade information and profit levels.
Next, the strategy identifies buy signals based on specific conditions.
For buy signals related to percentage change (pct), it checks conditions such as the number of days a stock has been listed, volume being greater than the average, streaks of minimum and maximum values, and certain percentage change thresholds.
For buy signals related to the lc2 indicator, it considers conditions like the number of days listed, volume being greater than the average, streaks of maximum values, and specific ratios involving lc2 and close prices. After identifying buy signals, the strategy populates tags indicating the type of buy signal. It uses the "buy_tag_pct" and "buy_tag_lc2" columns to represent the respective signals. If the strategy is "vin," it combines both types of signals. When determining sell signals, the strategy analyzes the current profit and trade information. It retrieves the relevant DataFrame for the trade, calculates the trade's length, checks recent buy signals, and considers streak values. If certain conditions are met, the strategy returns a sell signal. Overall, the ViN strategy focuses on identifying potential buying opportunities based on various indicators and conditions, while also considering sell signals based on trade information and profit levels.
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/vin.py", line 136, in populate_buy_trend self.fill_custom_buy_info(df, metadata) File "/freqtrade/user_data/strategies/vin.py", line 89, in fill_custom_buy_info for index, buy_date in df_buy.iteritems(): ^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/generic.py", line 6204, in __getattr__ return object.__getattribute__(self, name) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ AttributeError: 'Series' object has no attribute 'iteritems'
stoploss: -1 timeframe: 5m hash(sha256): 656c07fc8fbd7a6a85868b3f081d96616c88051d650314a134a399980ccd3819 indicators: hc2_adj updown close buy_tag_pct volume lc2_adj date open buy_tag streak_s_min_change high candle_count_1d buy hlc3_adj streak_h candle_count buy_tag_lc2 ho2_adj buy_signals low streak_s_min streak_s_max
No similar strategies found. (based on used indicators)
last change: 2024-04-28 14:41:53