more replacements of ticker_interval
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@ -24,7 +24,7 @@ AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList']
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DRY_RUN_WALLET = 999.9
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MATH_CLOSE_PREC = 1e-14 # Precision used for float comparisons
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TICKER_INTERVALS = [
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TIMEFRAMES = [
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'1m', '3m', '5m', '15m', '30m',
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'1h', '2h', '4h', '6h', '8h', '12h',
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'1d', '3d', '1w',
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@ -57,7 +57,7 @@ CONF_SCHEMA = {
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'type': 'object',
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'properties': {
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'max_open_trades': {'type': 'integer', 'minimum': -1},
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'ticker_interval': {'type': 'string', 'enum': TICKER_INTERVALS},
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'ticker_interval': {'type': 'string', 'enum': TIMEFRAMES},
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'stake_currency': {'type': 'string', 'enum': ['BTC', 'XBT', 'ETH', 'USDT', 'EUR', 'USD']},
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'stake_amount': {
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"type": ["number", "string"],
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@ -178,9 +178,9 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
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:return: Returns df with one additional column, col_name, containing the cumulative profit.
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"""
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from freqtrade.exchange import timeframe_to_minutes
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ticker_minutes = timeframe_to_minutes(timeframe)
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# Resample to ticker_interval to make sure trades match candles
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_trades_sum = trades.resample(f'{ticker_minutes}min', on='close_time')[['profitperc']].sum()
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timeframe_minutes = timeframe_to_minutes(timeframe)
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# Resample to timeframe to make sure trades match candles
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_trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_time')[['profitperc']].sum()
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df.loc[:, col_name] = _trades_sum.cumsum()
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# Set first value to 0
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df.loc[df.iloc[0].name, col_name] = 0
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@ -121,7 +121,7 @@ class Backtesting:
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min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
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)
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# Adjust startts forward if not enough data is available
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timerange.adjust_start_if_necessary(timeframe_to_seconds(self.ticker_interval),
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timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
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self.required_startup, min_date)
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return data, timerange
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