adjust plot_dataframe to use btanalysis
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@ -8,6 +8,10 @@ import pandas as pd
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from freqtrade.misc import json_load
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# must align with columns in backtest.py
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BT_DATA_COLUMNS = ["pair", "profitperc", "open_time", "close_time", "index", "duration",
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"open_rate", "close_rate", "open_at_end", "sell_reason"]
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def load_backtest_data(filename) -> pd.DataFrame:
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"""
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@ -24,11 +28,7 @@ def load_backtest_data(filename) -> pd.DataFrame:
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with filename.open() as file:
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data = json_load(file)
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# must align with columns in backtest.py
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columns = ["pair", "profitperc", "open_time", "close_time", "index", "duration",
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"open_rate", "close_rate", "open_at_end", "sell_reason"]
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df = pd.DataFrame(data, columns=columns)
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df = pd.DataFrame(data, columns=BT_DATA_COLUMNS)
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df['open_time'] = pd.to_datetime(df['open_time'],
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unit='s',
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@ -41,6 +41,7 @@ from plotly.offline import plot
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from freqtrade import persistence
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from freqtrade.arguments import Arguments, TimeRange
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from freqtrade.data import history
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from freqtrade.data.btanalysis import load_backtest_data, BT_DATA_COLUMNS
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from freqtrade.exchange import Exchange
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from freqtrade.optimize.backtesting import setup_configuration
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from freqtrade.persistence import Trade
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@ -56,7 +57,8 @@ def load_trades(args: Namespace, pair: str, timerange: TimeRange) -> pd.DataFram
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trades: pd.DataFrame = pd.DataFrame()
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if args.db_url:
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persistence.init(_CONF)
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columns = ["pair", "profit", "opents", "closets", "open_rate", "close_rate", "duration"]
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columns = ["pair", "profit", "open_time", "close_time",
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"open_rate", "close_rate", "duration"]
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for x in Trade.query.all():
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print("date: {}".format(x.open_date))
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@ -71,33 +73,13 @@ def load_trades(args: Namespace, pair: str, timerange: TimeRange) -> pd.DataFram
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columns=columns)
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elif args.exportfilename:
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file = Path(args.exportfilename)
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# must align with columns in backtest.py
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columns = ["pair", "profit", "opents", "closets", "index", "duration",
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"open_rate", "close_rate", "open_at_end", "sell_reason"]
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if file.exists():
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with file.open() as f:
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data = json.load(f)
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trades = pd.DataFrame(data, columns=columns)
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trades = trades.loc[trades["pair"] == pair]
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if timerange:
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if timerange.starttype == 'date':
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trades = trades.loc[trades["opents"] >= timerange.startts]
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if timerange.stoptype == 'date':
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trades = trades.loc[trades["opents"] <= timerange.stopts]
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trades['opents'] = pd.to_datetime(
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trades['opents'],
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unit='s',
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utc=True,
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infer_datetime_format=True)
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trades['closets'] = pd.to_datetime(
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trades['closets'],
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unit='s',
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utc=True,
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infer_datetime_format=True)
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file = Path(args.exportfilename)
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if file.exists():
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load_backtest_data(file)
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else:
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trades = pd.DataFrame([], columns=columns)
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trades = pd.DataFrame([], columns=BT_DATA_COLUMNS)
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return trades
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@ -206,7 +188,7 @@ def extract_trades_of_period(dataframe, trades) -> pd.DataFrame:
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Compare trades and backtested pair DataFrames to get trades performed on backtested period
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:return: the DataFrame of a trades of period
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"""
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trades = trades.loc[trades['opents'] >= dataframe.iloc[0]['date']]
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trades = trades.loc[trades['open_time'] >= dataframe.iloc[0]['date']]
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return trades
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@ -279,7 +261,7 @@ def generate_graph(
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)
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trade_buys = go.Scattergl(
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x=trades["opents"],
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x=trades["open_time"],
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y=trades["open_rate"],
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mode='markers',
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name='trade_buy',
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@ -291,7 +273,7 @@ def generate_graph(
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)
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)
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trade_sells = go.Scattergl(
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x=trades["closets"],
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x=trades["close_time"],
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y=trades["close_rate"],
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mode='markers',
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name='trade_sell',
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