cleanups
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@ -4,11 +4,9 @@ import logging
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import json
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import os
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from typing import Optional, List, Dict
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from pandas import DataFrame
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from freqtrade.exchange import get_ticker_history
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from freqtrade.optimize.hyperopt_conf import hyperopt_optimize_conf
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from pandas import DataFrame
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from freqtrade.analyze import populate_indicators, parse_ticker_dataframe
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logger = logging.getLogger(__name__)
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@ -50,10 +48,8 @@ def load_data(ticker_interval: int = 5, pairs: Optional[List[str]] = None,
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def preprocess(tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
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"""Creates a dataframe and populates indicators for given ticker data"""
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processed = {}
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for pair, pair_data in tickerdata.items():
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processed[pair] = populate_indicators(parse_ticker_dataframe(pair_data))
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return processed
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return {pair: populate_indicators(parse_ticker_dataframe(pair_data))
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for pair, pair_data in tickerdata.items()}
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def testdata_path() -> str:
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@ -111,14 +111,14 @@ def backtest(stake_amount: float, processed: Dict[str, DataFrame],
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if min_roi_reached(trade, row2.close, row2.date) or row2.sell == 1:
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current_profit_percent = trade.calc_profit_percent(rate=row2.close)
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current_profit_BTC = trade.calc_profit(rate=row2.close)
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current_profit_btc = trade.calc_profit(rate=row2.close)
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lock_pair_until = row2.Index
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trades.append(
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(
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pair,
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current_profit_percent,
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current_profit_BTC,
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current_profit_btc,
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row2.Index - row.Index
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)
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)
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@ -118,8 +118,7 @@ def optimizer(params):
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backtesting.populate_buy_trend = buy_strategy_generator(params)
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results = backtest(OPTIMIZE_CONFIG['stake_amount'], PROCESSED)
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result = format_results(results)
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result_explanation = format_results(results)
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total_profit = results.profit_percent.sum()
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trade_count = len(results.index)
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@ -135,20 +134,17 @@ def optimizer(params):
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loss = trade_loss + profit_loss
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_CURRENT_TRIES += 1
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result_data = {
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log_results({
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'loss': loss,
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'current_tries': _CURRENT_TRIES,
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'total_tries': TOTAL_TRIES,
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'result': result,
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}
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log_results(result_data)
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'result': result_explanation,
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})
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return {
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'loss': loss,
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'status': STATUS_OK,
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'result': result,
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'total_profit': total_profit,
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'avg_profit': results.profit_percent.mean() * 100.0,
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'result': result_explanation,
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}
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