Merge pull request #1987 from freqtrade/plot_script_changes
Plot script changes
This commit is contained in:
Matthias
GitHub
commit
0908863e07
@ -359,7 +359,7 @@ ARGS_PLOT_DATAFRAME = (ARGS_COMMON + ARGS_STRATEGY +
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"refresh_pairs", "live"])
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ARGS_PLOT_PROFIT = (ARGS_COMMON + ARGS_STRATEGY +
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["pairs", "timerange", "export", "exportfilename"])
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["pairs", "timerange", "export", "exportfilename", "db_url", "trade_source"])
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class TimeRange(NamedTuple):
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@ -3,6 +3,7 @@ Helpers when analyzing backtest data
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"""
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import logging
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from pathlib import Path
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from typing import Dict
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import numpy as np
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import pandas as pd
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@ -101,6 +102,18 @@ def load_trades_from_db(db_url: str) -> pd.DataFrame:
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return trades
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def load_trades(config) -> pd.DataFrame:
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"""
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Based on configuration option "trade_source":
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* loads data from DB (using `db_url`)
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* loads data from backtestfile (using `exportfilename`)
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"""
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if config["trade_source"] == "DB":
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return load_trades_from_db(config["db_url"])
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elif config["trade_source"] == "file":
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return load_backtest_data(Path(config["exportfilename"]))
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def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> pd.DataFrame:
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"""
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Compare trades and backtested pair DataFrames to get trades performed on backtested period
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@ -109,3 +122,34 @@ def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> p
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trades = trades.loc[(trades['open_time'] >= dataframe.iloc[0]['date']) &
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(trades['close_time'] <= dataframe.iloc[-1]['date'])]
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return trades
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def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame], column: str = "close"):
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"""
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Combine multiple dataframes "column"
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:param tickers: Dict of Dataframes, dict key should be pair.
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:param column: Column in the original dataframes to use
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:return: DataFrame with the column renamed to the dict key, and a column
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named mean, containing the mean of all pairs.
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"""
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df_comb = pd.concat([tickers[pair].set_index('date').rename(
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{column: pair}, axis=1)[pair] for pair in tickers], axis=1)
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df_comb['mean'] = df_comb.mean(axis=1)
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return df_comb
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def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str) -> pd.DataFrame:
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"""
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Adds a column `col_name` with the cumulative profit for the given trades array.
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:param df: DataFrame with date index
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:param trades: DataFrame containing trades (requires columns close_time and profitperc)
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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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df[col_name] = trades.set_index('close_time')['profitperc'].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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# FFill to get continuous
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df[col_name] = df[col_name].ffill()
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return df
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@ -5,10 +5,8 @@ import gzip
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import logging
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import re
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from datetime import datetime
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from typing import Dict
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import numpy as np
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from pandas import DataFrame
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import rapidjson
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@ -41,24 +39,6 @@ def datesarray_to_datetimearray(dates: np.ndarray) -> np.ndarray:
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return dates.dt.to_pydatetime()
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def common_datearray(dfs: Dict[str, DataFrame]) -> np.ndarray:
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"""
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Return dates from Dataframe
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:param dfs: Dict with format pair: pair_data
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:return: List of dates
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"""
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alldates = {}
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for pair, pair_data in dfs.items():
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dates = datesarray_to_datetimearray(pair_data['date'])
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for date in dates:
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alldates[date] = 1
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lst = []
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for date, _ in alldates.items():
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lst.append(date)
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arr = np.array(lst)
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return np.sort(arr, axis=0)
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def file_dump_json(filename, data, is_zip=False) -> None:
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"""
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Dump JSON data into a file
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@ -1,8 +1,15 @@
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import logging
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from typing import List
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from pathlib import Path
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from typing import Dict, List, Optional
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import pandas as pd
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from pathlib import Path
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from freqtrade.arguments import Arguments
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from freqtrade.data import history
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from freqtrade.data.btanalysis import (combine_tickers_with_mean,
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create_cum_profit, load_trades)
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from freqtrade.exchange import Exchange
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from freqtrade.resolvers import ExchangeResolver, StrategyResolver
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logger = logging.getLogger(__name__)
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@ -16,7 +23,46 @@ except ImportError:
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exit(1)
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def generate_row(fig, row, indicators: List[str], data: pd.DataFrame) -> tools.make_subplots:
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def init_plotscript(config):
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"""
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Initialize objects needed for plotting
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:return: Dict with tickers, trades, pairs and strategy
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"""
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exchange: Optional[Exchange] = None
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# Exchange is only needed when downloading data!
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if config.get("live", False) or config.get("refresh_pairs", False):
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exchange = ExchangeResolver(config.get('exchange', {}).get('name'),
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config).exchange
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strategy = StrategyResolver(config).strategy
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if "pairs" in config:
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pairs = config["pairs"].split(',')
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else:
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pairs = config["exchange"]["pair_whitelist"]
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# Set timerange to use
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timerange = Arguments.parse_timerange(config["timerange"])
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tickers = history.load_data(
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datadir=Path(str(config.get("datadir"))),
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pairs=pairs,
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ticker_interval=config['ticker_interval'],
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refresh_pairs=config.get('refresh_pairs', False),
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timerange=timerange,
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exchange=exchange,
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live=config.get("live", False),
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)
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trades = load_trades(config)
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return {"tickers": tickers,
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"trades": trades,
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"pairs": pairs,
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"strategy": strategy,
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}
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def add_indicators(fig, row, indicators: List[str], data: pd.DataFrame) -> tools.make_subplots:
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"""
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Generator all the indicator selected by the user for a specific row
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:param fig: Plot figure to append to
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@ -44,9 +90,29 @@ def generate_row(fig, row, indicators: List[str], data: pd.DataFrame) -> tools.m
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return fig
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def plot_trades(fig, trades: pd.DataFrame):
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def add_profit(fig, row, data: pd.DataFrame, column: str, name: str) -> tools.make_subplots:
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"""
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Plot trades to "fig"
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Add profit-plot
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:param fig: Plot figure to append to
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:param row: row number for this plot
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:param data: candlestick DataFrame
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:param column: Column to use for plot
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:param name: Name to use
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:return: fig with added profit plot
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"""
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profit = go.Scattergl(
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x=data.index,
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y=data[column],
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name=name,
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)
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fig.append_trace(profit, row, 1)
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return fig
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def plot_trades(fig, trades: pd.DataFrame) -> tools.make_subplots:
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"""
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Add trades to "fig"
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"""
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# Trades can be empty
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if trades is not None and len(trades) > 0:
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@ -86,13 +152,9 @@ def plot_trades(fig, trades: pd.DataFrame):
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return fig
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def generate_graph(
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pair: str,
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data: pd.DataFrame,
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trades: pd.DataFrame = None,
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def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFrame = None,
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indicators1: List[str] = [],
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indicators2: List[str] = [],
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) -> go.Figure:
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indicators2: List[str] = [],) -> go.Figure:
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"""
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Generate the graph from the data generated by Backtesting or from DB
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Volume will always be ploted in row2, so Row 1 and 3 are to our disposal for custom indicators
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@ -186,7 +248,7 @@ def generate_graph(
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fig.append_trace(bb_upper, 1, 1)
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# Add indicators to main plot
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fig = generate_row(fig=fig, row=1, indicators=indicators1, data=data)
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fig = add_indicators(fig=fig, row=1, indicators=indicators1, data=data)
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fig = plot_trades(fig, trades)
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@ -199,12 +261,54 @@ def generate_graph(
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fig.append_trace(volume, 2, 1)
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# Add indicators to seperate row
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fig = generate_row(fig=fig, row=3, indicators=indicators2, data=data)
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fig = add_indicators(fig=fig, row=3, indicators=indicators2, data=data)
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return fig
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def generate_plot_file(fig, pair, ticker_interval) -> None:
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def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
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trades: pd.DataFrame) -> go.Figure:
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# Combine close-values for all pairs, rename columns to "pair"
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df_comb = combine_tickers_with_mean(tickers, "close")
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# Add combined cumulative profit
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df_comb = create_cum_profit(df_comb, trades, 'cum_profit')
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# Plot the pairs average close prices, and total profit growth
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avgclose = go.Scattergl(
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x=df_comb.index,
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y=df_comb['mean'],
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name='Avg close price',
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)
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fig = tools.make_subplots(rows=3, cols=1, shared_xaxes=True, row_width=[1, 1, 1])
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fig['layout'].update(title="Profit plot")
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fig.append_trace(avgclose, 1, 1)
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fig = add_profit(fig, 2, df_comb, 'cum_profit', 'Profit')
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for pair in pairs:
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profit_col = f'cum_profit_{pair}'
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df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col)
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fig = add_profit(fig, 3, df_comb, profit_col, f"Profit {pair}")
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return fig
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def generate_plot_filename(pair, ticker_interval) -> str:
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"""
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Generate filenames per pair/ticker_interval to be used for storing plots
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"""
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pair_name = pair.replace("/", "_")
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file_name = 'freqtrade-plot-' + pair_name + '-' + ticker_interval + '.html'
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logger.info('Generate plot file for %s', pair)
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return file_name
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def store_plot_file(fig, filename: str, auto_open: bool = False) -> None:
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"""
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Generate a plot html file from pre populated fig plotly object
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:param fig: Plotly Figure to plot
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@ -212,12 +316,8 @@ def generate_plot_file(fig, pair, ticker_interval) -> None:
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:param ticker_interval: Used as part of the filename
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:return: None
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"""
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logger.info('Generate plot file for %s', pair)
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pair_name = pair.replace("/", "_")
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file_name = 'freqtrade-plot-' + pair_name + '-' + ticker_interval + '.html'
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Path("user_data/plots").mkdir(parents=True, exist_ok=True)
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plot(fig, filename=str(Path('user_data/plots').joinpath(file_name)),
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auto_open=False)
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plot(fig, filename=str(Path('user_data/plots').joinpath(filename)),
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auto_open=auto_open)
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@ -1,14 +1,18 @@
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from unittest.mock import MagicMock
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from arrow import Arrow
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import pytest
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from arrow import Arrow
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from pandas import DataFrame, to_datetime
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from freqtrade.arguments import TimeRange
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from freqtrade.arguments import Arguments, TimeRange
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from freqtrade.data.btanalysis import (BT_DATA_COLUMNS,
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combine_tickers_with_mean,
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create_cum_profit,
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extract_trades_of_period,
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load_backtest_data, load_trades_from_db)
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from freqtrade.data.history import load_pair_history, make_testdata_path
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load_backtest_data, load_trades,
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load_trades_from_db)
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from freqtrade.data.history import (load_data, load_pair_history,
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make_testdata_path)
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from freqtrade.tests.test_persistence import create_mock_trades
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@ -74,3 +78,52 @@ def test_extract_trades_of_period():
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assert trades1.iloc[0].close_time == Arrow(2017, 11, 14, 10, 41, 0).datetime
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assert trades1.iloc[-1].open_time == Arrow(2017, 11, 14, 14, 20, 0).datetime
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assert trades1.iloc[-1].close_time == Arrow(2017, 11, 14, 15, 25, 0).datetime
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def test_load_trades(default_conf, mocker):
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db_mock = mocker.patch("freqtrade.data.btanalysis.load_trades_from_db", MagicMock())
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bt_mock = mocker.patch("freqtrade.data.btanalysis.load_backtest_data", MagicMock())
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default_conf['trade_source'] = "DB"
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load_trades(default_conf)
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assert db_mock.call_count == 1
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assert bt_mock.call_count == 0
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db_mock.reset_mock()
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bt_mock.reset_mock()
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default_conf['trade_source'] = "file"
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default_conf['exportfilename'] = "testfile.json"
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load_trades(default_conf)
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assert db_mock.call_count == 0
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assert bt_mock.call_count == 1
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def test_combine_tickers_with_mean():
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pairs = ["ETH/BTC", "XLM/BTC"]
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tickers = load_data(datadir=None,
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pairs=pairs,
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ticker_interval='5m'
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)
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df = combine_tickers_with_mean(tickers)
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assert isinstance(df, DataFrame)
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assert "ETH/BTC" in df.columns
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assert "XLM/BTC" in df.columns
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assert "mean" in df.columns
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def test_create_cum_profit():
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filename = make_testdata_path(None) / "backtest-result_test.json"
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bt_data = load_backtest_data(filename)
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timerange = Arguments.parse_timerange("20180110-20180112")
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df = load_pair_history(pair="POWR/BTC", ticker_interval='5m',
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datadir=None, timerange=timerange)
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cum_profits = create_cum_profit(df.set_index('date'),
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bt_data[bt_data["pair"] == 'POWR/BTC'],
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"cum_profits")
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assert "cum_profits" in cum_profits.columns
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assert cum_profits.iloc[0]['cum_profits'] == 0
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assert cum_profits.iloc[-1]['cum_profits'] == 0.0798005
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@ -4,10 +4,9 @@ import datetime
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from unittest.mock import MagicMock
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from freqtrade.data.converter import parse_ticker_dataframe
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from freqtrade.misc import (common_datearray, datesarray_to_datetimearray,
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file_dump_json, file_load_json, format_ms_time, shorten_date)
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from freqtrade.data.history import load_tickerdata_file, pair_data_filename
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from freqtrade.strategy.default_strategy import DefaultStrategy
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from freqtrade.data.history import pair_data_filename
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from freqtrade.misc import (datesarray_to_datetimearray, file_dump_json,
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file_load_json, format_ms_time, shorten_date)
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def test_shorten_date() -> None:
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@ -32,20 +31,6 @@ def test_datesarray_to_datetimearray(ticker_history_list):
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assert date_len == 2
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def test_common_datearray(default_conf) -> None:
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strategy = DefaultStrategy(default_conf)
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tick = load_tickerdata_file(None, 'UNITTEST/BTC', '1m')
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tickerlist = {'UNITTEST/BTC': parse_ticker_dataframe(tick, "1m", pair="UNITTEST/BTC",
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fill_missing=True)}
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dataframes = strategy.tickerdata_to_dataframe(tickerlist)
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dates = common_datearray(dataframes)
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assert dates.size == dataframes['UNITTEST/BTC']['date'].size
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assert dates[0] == dataframes['UNITTEST/BTC']['date'][0]
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assert dates[-1] == dataframes['UNITTEST/BTC']['date'].iloc[-1]
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def test_file_dump_json(mocker) -> None:
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file_open = mocker.patch('freqtrade.misc.open', MagicMock())
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json_dump = mocker.patch('rapidjson.dump', MagicMock())
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@ -1,21 +1,24 @@
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from copy import deepcopy
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from unittest.mock import MagicMock
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from plotly import tools
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import plotly.graph_objs as go
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from copy import deepcopy
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from plotly import tools
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from freqtrade.arguments import TimeRange
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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
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from freqtrade.plot.plotting import (generate_graph, generate_plot_file,
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generate_row, plot_trades)
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from freqtrade.data.btanalysis import create_cum_profit, load_backtest_data
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from freqtrade.plot.plotting import (add_indicators, add_profit,
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generate_candlestick_graph,
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generate_plot_filename,
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generate_profit_graph, init_plotscript,
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plot_trades, store_plot_file)
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from freqtrade.strategy.default_strategy import DefaultStrategy
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from freqtrade.tests.conftest import log_has, log_has_re
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def fig_generating_mock(fig, *args, **kwargs):
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""" Return Fig - used to mock generate_row and plot_trades"""
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""" Return Fig - used to mock add_indicators and plot_trades"""
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return fig
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@ -34,7 +37,27 @@ def generage_empty_figure():
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)
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||||
|
||||
|
||||
def test_generate_row(default_conf, caplog):
|
||||
def test_init_plotscript(default_conf, mocker):
|
||||
default_conf['timerange'] = "20180110-20180112"
|
||||
default_conf['trade_source'] = "file"
|
||||
default_conf['ticker_interval'] = "5m"
|
||||
default_conf["datadir"] = history.make_testdata_path(None)
|
||||
default_conf['exportfilename'] = str(
|
||||
history.make_testdata_path(None) / "backtest-result_test.json")
|
||||
ret = init_plotscript(default_conf)
|
||||
assert "tickers" in ret
|
||||
assert "trades" in ret
|
||||
assert "pairs" in ret
|
||||
assert "strategy" in ret
|
||||
|
||||
default_conf['pairs'] = "POWR/BTC,XLM/BTC"
|
||||
ret = init_plotscript(default_conf)
|
||||
assert "tickers" in ret
|
||||
assert "POWR/BTC" in ret["tickers"]
|
||||
assert "XLM/BTC" in ret["tickers"]
|
||||
|
||||
|
||||
def test_add_indicators(default_conf, caplog):
|
||||
pair = "UNITTEST/BTC"
|
||||
timerange = TimeRange(None, 'line', 0, -1000)
|
||||
|
||||
@ -49,20 +72,20 @@ def test_generate_row(default_conf, caplog):
|
||||
fig = generage_empty_figure()
|
||||
|
||||
# Row 1
|
||||
fig1 = generate_row(fig=deepcopy(fig), row=1, indicators=indicators1, data=data)
|
||||
fig1 = add_indicators(fig=deepcopy(fig), row=1, indicators=indicators1, data=data)
|
||||
figure = fig1.layout.figure
|
||||
ema10 = find_trace_in_fig_data(figure.data, "ema10")
|
||||
assert isinstance(ema10, go.Scatter)
|
||||
assert ema10.yaxis == "y"
|
||||
|
||||
fig2 = generate_row(fig=deepcopy(fig), row=3, indicators=indicators2, data=data)
|
||||
fig2 = add_indicators(fig=deepcopy(fig), row=3, indicators=indicators2, data=data)
|
||||
figure = fig2.layout.figure
|
||||
macd = find_trace_in_fig_data(figure.data, "macd")
|
||||
assert isinstance(macd, go.Scatter)
|
||||
assert macd.yaxis == "y3"
|
||||
|
||||
# No indicator found
|
||||
fig3 = generate_row(fig=deepcopy(fig), row=3, indicators=['no_indicator'], data=data)
|
||||
fig3 = add_indicators(fig=deepcopy(fig), row=3, indicators=['no_indicator'], data=data)
|
||||
assert fig == fig3
|
||||
assert log_has_re(r'Indicator "no_indicator" ignored\..*', caplog.record_tuples)
|
||||
|
||||
@ -95,8 +118,8 @@ def test_plot_trades(caplog):
|
||||
assert trade_sell.marker.color == 'red'
|
||||
|
||||
|
||||
def test_generate_graph_no_signals_no_trades(default_conf, mocker, caplog):
|
||||
row_mock = mocker.patch('freqtrade.plot.plotting.generate_row',
|
||||
def test_generate_candlestick_graph_no_signals_no_trades(default_conf, mocker, caplog):
|
||||
row_mock = mocker.patch('freqtrade.plot.plotting.add_indicators',
|
||||
MagicMock(side_effect=fig_generating_mock))
|
||||
trades_mock = mocker.patch('freqtrade.plot.plotting.plot_trades',
|
||||
MagicMock(side_effect=fig_generating_mock))
|
||||
@ -110,7 +133,7 @@ def test_generate_graph_no_signals_no_trades(default_conf, mocker, caplog):
|
||||
|
||||
indicators1 = []
|
||||
indicators2 = []
|
||||
fig = generate_graph(pair=pair, data=data, trades=None,
|
||||
fig = generate_candlestick_graph(pair=pair, data=data, trades=None,
|
||||
indicators1=indicators1, indicators2=indicators2)
|
||||
assert isinstance(fig, go.Figure)
|
||||
assert fig.layout.title.text == pair
|
||||
@ -131,8 +154,8 @@ def test_generate_graph_no_signals_no_trades(default_conf, mocker, caplog):
|
||||
assert log_has("No sell-signals found.", caplog.record_tuples)
|
||||
|
||||
|
||||
def test_generate_graph_no_trades(default_conf, mocker):
|
||||
row_mock = mocker.patch('freqtrade.plot.plotting.generate_row',
|
||||
def test_generate_candlestick_graph_no_trades(default_conf, mocker):
|
||||
row_mock = mocker.patch('freqtrade.plot.plotting.add_indicators',
|
||||
MagicMock(side_effect=fig_generating_mock))
|
||||
trades_mock = mocker.patch('freqtrade.plot.plotting.plot_trades',
|
||||
MagicMock(side_effect=fig_generating_mock))
|
||||
@ -147,7 +170,7 @@ def test_generate_graph_no_trades(default_conf, mocker):
|
||||
|
||||
indicators1 = []
|
||||
indicators2 = []
|
||||
fig = generate_graph(pair=pair, data=data, trades=None,
|
||||
fig = generate_candlestick_graph(pair=pair, data=data, trades=None,
|
||||
indicators1=indicators1, indicators2=indicators2)
|
||||
assert isinstance(fig, go.Figure)
|
||||
assert fig.layout.title.text == pair
|
||||
@ -178,12 +201,68 @@ def test_generate_graph_no_trades(default_conf, mocker):
|
||||
assert trades_mock.call_count == 1
|
||||
|
||||
|
||||
def test_generate_Plot_filename():
|
||||
fn = generate_plot_filename("UNITTEST/BTC", "5m")
|
||||
assert fn == "freqtrade-plot-UNITTEST_BTC-5m.html"
|
||||
|
||||
|
||||
def test_generate_plot_file(mocker, caplog):
|
||||
fig = generage_empty_figure()
|
||||
plot_mock = mocker.patch("freqtrade.plot.plotting.plot", MagicMock())
|
||||
generate_plot_file(fig, "UNITTEST/BTC", "5m")
|
||||
store_plot_file(fig, filename="freqtrade-plot-UNITTEST_BTC-5m.html")
|
||||
|
||||
assert plot_mock.call_count == 1
|
||||
assert plot_mock.call_args[0][0] == fig
|
||||
assert (plot_mock.call_args_list[0][1]['filename']
|
||||
== "user_data/plots/freqtrade-plot-UNITTEST_BTC-5m.html")
|
||||
|
||||
|
||||
def test_add_profit():
|
||||
filename = history.make_testdata_path(None) / "backtest-result_test.json"
|
||||
bt_data = load_backtest_data(filename)
|
||||
timerange = Arguments.parse_timerange("20180110-20180112")
|
||||
|
||||
df = history.load_pair_history(pair="POWR/BTC", ticker_interval='5m',
|
||||
datadir=None, timerange=timerange)
|
||||
fig = generage_empty_figure()
|
||||
|
||||
cum_profits = create_cum_profit(df.set_index('date'),
|
||||
bt_data[bt_data["pair"] == 'POWR/BTC'],
|
||||
"cum_profits")
|
||||
|
||||
fig1 = add_profit(fig, row=2, data=cum_profits, column='cum_profits', name='Profits')
|
||||
figure = fig1.layout.figure
|
||||
profits = find_trace_in_fig_data(figure.data, "Profits")
|
||||
assert isinstance(profits, go.Scattergl)
|
||||
assert profits.yaxis == "y2"
|
||||
|
||||
|
||||
def test_generate_profit_graph():
|
||||
filename = history.make_testdata_path(None) / "backtest-result_test.json"
|
||||
trades = load_backtest_data(filename)
|
||||
timerange = Arguments.parse_timerange("20180110-20180112")
|
||||
pairs = ["POWR/BTC", "XLM/BTC"]
|
||||
|
||||
tickers = history.load_data(datadir=None,
|
||||
pairs=pairs,
|
||||
ticker_interval='5m',
|
||||
timerange=timerange
|
||||
)
|
||||
trades = trades[trades['pair'].isin(pairs)]
|
||||
|
||||
fig = generate_profit_graph(pairs, tickers, trades)
|
||||
assert isinstance(fig, go.Figure)
|
||||
|
||||
assert fig.layout.title.text == "Profit plot"
|
||||
figure = fig.layout.figure
|
||||
assert len(figure.data) == 4
|
||||
|
||||
avgclose = find_trace_in_fig_data(figure.data, "Avg close price")
|
||||
assert isinstance(avgclose, go.Scattergl)
|
||||
|
||||
profit = find_trace_in_fig_data(figure.data, "Profit")
|
||||
assert isinstance(profit, go.Scattergl)
|
||||
|
||||
for pair in pairs:
|
||||
profit_pair = find_trace_in_fig_data(figure.data, f"Profit {pair}")
|
||||
assert isinstance(profit_pair, go.Scattergl)
|
||||
|
||||
@ -2,19 +2,7 @@
|
||||
"""
|
||||
Script to display when the bot will buy on specific pair(s)
|
||||
|
||||
Mandatory Cli parameters:
|
||||
-p / --pairs: pair(s) to examine
|
||||
|
||||
Option but recommended
|
||||
-s / --strategy: strategy to use
|
||||
|
||||
|
||||
Optional Cli parameters
|
||||
-d / --datadir: path to pair(s) backtest data
|
||||
--timerange: specify what timerange of data to use.
|
||||
-l / --live: Live, to download the latest ticker for the pair(s)
|
||||
-db / --db-url: Show trades stored in database
|
||||
|
||||
Use `python plot_dataframe.py --help` to display the command line arguments
|
||||
|
||||
Indicators recommended
|
||||
Row 1: sma, ema3, ema5, ema10, ema50
|
||||
@ -26,18 +14,16 @@ Example of usage:
|
||||
"""
|
||||
import logging
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from freqtrade.arguments import ARGS_PLOT_DATAFRAME, Arguments
|
||||
from freqtrade.data import history
|
||||
from freqtrade.data.btanalysis import (extract_trades_of_period,
|
||||
load_backtest_data, load_trades_from_db)
|
||||
from freqtrade.data.btanalysis import extract_trades_of_period
|
||||
from freqtrade.optimize import setup_configuration
|
||||
from freqtrade.plot.plotting import generate_graph, generate_plot_file
|
||||
from freqtrade.resolvers import ExchangeResolver, StrategyResolver
|
||||
from freqtrade.plot.plotting import (init_plotscript, generate_candlestick_graph,
|
||||
store_plot_file,
|
||||
generate_plot_filename)
|
||||
from freqtrade.state import RunMode
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@ -68,52 +54,29 @@ def analyse_and_plot_pairs(config: Dict[str, Any]):
|
||||
-Generate plot files
|
||||
:return: None
|
||||
"""
|
||||
exchange = ExchangeResolver(config.get('exchange', {}).get('name'), config).exchange
|
||||
|
||||
strategy = StrategyResolver(config).strategy
|
||||
if "pairs" in config:
|
||||
pairs = config["pairs"].split(',')
|
||||
else:
|
||||
pairs = config["exchange"]["pair_whitelist"]
|
||||
|
||||
# Set timerange to use
|
||||
timerange = Arguments.parse_timerange(config["timerange"])
|
||||
ticker_interval = strategy.ticker_interval
|
||||
|
||||
tickers = history.load_data(
|
||||
datadir=Path(str(config.get("datadir"))),
|
||||
pairs=pairs,
|
||||
ticker_interval=config['ticker_interval'],
|
||||
refresh_pairs=config.get('refresh_pairs', False),
|
||||
timerange=timerange,
|
||||
exchange=exchange,
|
||||
live=config.get("live", False),
|
||||
)
|
||||
plot_elements = init_plotscript(config)
|
||||
trades = plot_elements['trades']
|
||||
|
||||
pair_counter = 0
|
||||
for pair, data in tickers.items():
|
||||
for pair, data in plot_elements["tickers"].items():
|
||||
pair_counter += 1
|
||||
logger.info("analyse pair %s", pair)
|
||||
tickers = {}
|
||||
tickers[pair] = data
|
||||
dataframe = generate_dataframe(strategy, tickers, pair)
|
||||
if config["trade_source"] == "DB":
|
||||
trades = load_trades_from_db(config["db_url"])
|
||||
elif config["trade_source"] == "file":
|
||||
trades = load_backtest_data(Path(config["exportfilename"]))
|
||||
dataframe = generate_dataframe(plot_elements["strategy"], tickers, pair)
|
||||
|
||||
trades = trades.loc[trades['pair'] == pair]
|
||||
trades = extract_trades_of_period(dataframe, trades)
|
||||
trades_pair = trades.loc[trades['pair'] == pair]
|
||||
trades_pair = extract_trades_of_period(dataframe, trades_pair)
|
||||
|
||||
fig = generate_graph(
|
||||
fig = generate_candlestick_graph(
|
||||
pair=pair,
|
||||
data=dataframe,
|
||||
trades=trades,
|
||||
trades=trades_pair,
|
||||
indicators1=config["indicators1"].split(","),
|
||||
indicators2=config["indicators2"].split(",")
|
||||
)
|
||||
|
||||
generate_plot_file(fig, pair, ticker_interval)
|
||||
store_plot_file(fig, generate_plot_filename(pair, config['ticker_interval']))
|
||||
|
||||
logger.info('End of ploting process %s plots generated', pair_counter)
|
||||
|
||||
@ -130,7 +93,7 @@ def plot_parse_args(args: List[str]) -> Dict[str, Any]:
|
||||
parsed_args = arguments.parse_args()
|
||||
|
||||
# Load the configuration
|
||||
config = setup_configuration(parsed_args, RunMode.BACKTEST)
|
||||
config = setup_configuration(parsed_args, RunMode.OTHER)
|
||||
return config
|
||||
|
||||
|
||||
|
||||
@ -2,204 +2,39 @@
|
||||
"""
|
||||
Script to display profits
|
||||
|
||||
Mandatory Cli parameters:
|
||||
-p / --pair: pair to examine
|
||||
|
||||
Optional Cli parameters
|
||||
-c / --config: specify configuration file
|
||||
-s / --strategy: strategy to use
|
||||
-d / --datadir: path to pair backtest data
|
||||
--timerange: specify what timerange of data to use
|
||||
--export-filename: Specify where the backtest export is located.
|
||||
Use `python plot_profit.py --help` to display the command line arguments
|
||||
"""
|
||||
import json
|
||||
import logging
|
||||
import sys
|
||||
from argparse import Namespace
|
||||
from pathlib import Path
|
||||
from typing import List, Optional
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import numpy as np
|
||||
import plotly.graph_objs as go
|
||||
from plotly import tools
|
||||
from plotly.offline import plot
|
||||
|
||||
from freqtrade.arguments import Arguments, ARGS_PLOT_PROFIT
|
||||
from freqtrade.configuration import Configuration
|
||||
from freqtrade.data import history
|
||||
from freqtrade.exchange import timeframe_to_seconds
|
||||
from freqtrade.misc import common_datearray
|
||||
from freqtrade.resolvers import StrategyResolver
|
||||
from freqtrade.arguments import ARGS_PLOT_PROFIT, Arguments
|
||||
from freqtrade.optimize import setup_configuration
|
||||
from freqtrade.plot.plotting import init_plotscript, generate_profit_graph, store_plot_file
|
||||
from freqtrade.state import RunMode
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# data:: [ pair, profit-%, enter, exit, time, duration]
|
||||
# data:: ["ETH/BTC", 0.0023975, "1515598200", "1515602100", "2018-01-10 07:30:00+00:00", 65]
|
||||
def make_profit_array(data: List, px: int, min_date: int,
|
||||
interval: str,
|
||||
filter_pairs: Optional[List] = None) -> np.ndarray:
|
||||
pg = np.zeros(px)
|
||||
filter_pairs = filter_pairs or []
|
||||
# Go through the trades
|
||||
# and make an total profit
|
||||
# array
|
||||
for trade in data:
|
||||
pair = trade[0]
|
||||
if filter_pairs and pair not in filter_pairs:
|
||||
continue
|
||||
profit = trade[1]
|
||||
trade_sell_time = int(trade[3])
|
||||
|
||||
ix = define_index(min_date, trade_sell_time, interval)
|
||||
if ix < px:
|
||||
logger.debug('[%s]: Add profit %s on %s', pair, profit, trade[4])
|
||||
pg[ix] += profit
|
||||
|
||||
# rewrite the pg array to go from
|
||||
# total profits at each timeframe
|
||||
# to accumulated profits
|
||||
pa = 0
|
||||
for x in range(0, len(pg)):
|
||||
p = pg[x] # Get current total percent
|
||||
pa += p # Add to the accumulated percent
|
||||
pg[x] = pa # write back to save memory
|
||||
|
||||
return pg
|
||||
|
||||
|
||||
def plot_profit(args: Namespace) -> None:
|
||||
def plot_profit(config: Dict[str, Any]) -> None:
|
||||
"""
|
||||
Plots the total profit for all pairs.
|
||||
Note, the profit calculation isn't realistic.
|
||||
But should be somewhat proportional, and therefor useful
|
||||
in helping out to find a good algorithm.
|
||||
"""
|
||||
plot_elements = init_plotscript(config)
|
||||
trades = plot_elements['trades']
|
||||
# Filter trades to relevant pairs
|
||||
trades = trades[trades['pair'].isin(plot_elements["pairs"])]
|
||||
|
||||
# We need to use the same pairs, same ticker_interval
|
||||
# and same timeperiod as used in backtesting
|
||||
# to match the tickerdata against the profits-results
|
||||
timerange = Arguments.parse_timerange(args.timerange)
|
||||
|
||||
config = Configuration(args, RunMode.OTHER).get_config()
|
||||
|
||||
# Init strategy
|
||||
try:
|
||||
strategy = StrategyResolver({'strategy': config.get('strategy')}).strategy
|
||||
|
||||
except AttributeError:
|
||||
logger.critical(
|
||||
'Impossible to load the strategy. Please check the file "user_data/strategies/%s.py"',
|
||||
config.get('strategy')
|
||||
)
|
||||
exit(1)
|
||||
|
||||
# Load the profits results
|
||||
try:
|
||||
filename = args.exportfilename
|
||||
with open(filename) as file:
|
||||
data = json.load(file)
|
||||
except FileNotFoundError:
|
||||
logger.critical(
|
||||
'File "backtest-result.json" not found. This script require backtesting '
|
||||
'results to run.\nPlease run a backtesting with the parameter --export.')
|
||||
exit(1)
|
||||
|
||||
# Take pairs from the cli otherwise switch to the pair in the config file
|
||||
if args.pairs:
|
||||
filter_pairs = args.pairs
|
||||
filter_pairs = filter_pairs.split(',')
|
||||
else:
|
||||
filter_pairs = config['exchange']['pair_whitelist']
|
||||
|
||||
ticker_interval = strategy.ticker_interval
|
||||
pairs = config['exchange']['pair_whitelist']
|
||||
|
||||
if filter_pairs:
|
||||
pairs = list(set(pairs) & set(filter_pairs))
|
||||
logger.info('Filter, keep pairs %s' % pairs)
|
||||
|
||||
tickers = history.load_data(
|
||||
datadir=Path(str(config.get('datadir'))),
|
||||
pairs=pairs,
|
||||
ticker_interval=ticker_interval,
|
||||
refresh_pairs=False,
|
||||
timerange=timerange
|
||||
)
|
||||
dataframes = strategy.tickerdata_to_dataframe(tickers)
|
||||
|
||||
# NOTE: the dataframes are of unequal length,
|
||||
# 'dates' is an merged date array of them all.
|
||||
|
||||
dates = common_datearray(dataframes)
|
||||
min_date = int(min(dates).timestamp())
|
||||
max_date = int(max(dates).timestamp())
|
||||
num_iterations = define_index(min_date, max_date, ticker_interval) + 1
|
||||
|
||||
# Make an average close price of all the pairs that was involved.
|
||||
# Create an average close price of all the pairs that were involved.
|
||||
# this could be useful to gauge the overall market trend
|
||||
# We are essentially saying:
|
||||
# array <- sum dataframes[*]['close'] / num_items dataframes
|
||||
# FIX: there should be some onliner numpy/panda for this
|
||||
avgclose = np.zeros(num_iterations)
|
||||
num = 0
|
||||
for pair, pair_data in dataframes.items():
|
||||
close = pair_data['close']
|
||||
maxprice = max(close) # Normalize price to [0,1]
|
||||
logger.info('Pair %s has length %s' % (pair, len(close)))
|
||||
for x in range(0, len(close)):
|
||||
avgclose[x] += close[x] / maxprice
|
||||
# avgclose += close
|
||||
num += 1
|
||||
avgclose /= num
|
||||
|
||||
# make an profits-growth array
|
||||
pg = make_profit_array(data, num_iterations, min_date, ticker_interval, filter_pairs)
|
||||
|
||||
#
|
||||
# Plot the pairs average close prices, and total profit growth
|
||||
#
|
||||
|
||||
avgclose = go.Scattergl(
|
||||
x=dates,
|
||||
y=avgclose,
|
||||
name='Avg close price',
|
||||
)
|
||||
|
||||
profit = go.Scattergl(
|
||||
x=dates,
|
||||
y=pg,
|
||||
name='Profit',
|
||||
)
|
||||
|
||||
fig = tools.make_subplots(rows=3, cols=1, shared_xaxes=True, row_width=[1, 1, 1])
|
||||
|
||||
fig.append_trace(avgclose, 1, 1)
|
||||
fig.append_trace(profit, 2, 1)
|
||||
|
||||
for pair in pairs:
|
||||
pg = make_profit_array(data, num_iterations, min_date, ticker_interval, [pair])
|
||||
pair_profit = go.Scattergl(
|
||||
x=dates,
|
||||
y=pg,
|
||||
name=pair,
|
||||
)
|
||||
fig.append_trace(pair_profit, 3, 1)
|
||||
|
||||
plot(fig, filename=str(Path('user_data').joinpath('freqtrade-profit-plot.html')))
|
||||
fig = generate_profit_graph(plot_elements["pairs"], plot_elements["tickers"], trades)
|
||||
store_plot_file(fig, filename='freqtrade-profit-plot.html', auto_open=True)
|
||||
|
||||
|
||||
def define_index(min_date: int, max_date: int, ticker_interval: str) -> int:
|
||||
"""
|
||||
Return the index of a specific date
|
||||
"""
|
||||
interval_seconds = timeframe_to_seconds(ticker_interval)
|
||||
return int((max_date - min_date) / interval_seconds)
|
||||
|
||||
|
||||
def plot_parse_args(args: List[str]) -> Namespace:
|
||||
def plot_parse_args(args: List[str]) -> Dict[str, Any]:
|
||||
"""
|
||||
Parse args passed to the script
|
||||
:param args: Cli arguments
|
||||
@ -208,7 +43,11 @@ def plot_parse_args(args: List[str]) -> Namespace:
|
||||
arguments = Arguments(args, 'Graph profits')
|
||||
arguments.build_args(optionlist=ARGS_PLOT_PROFIT)
|
||||
|
||||
return arguments.parse_args()
|
||||
parsed_args = arguments.parse_args()
|
||||
|
||||
# Load the configuration
|
||||
config = setup_configuration(parsed_args, RunMode.OTHER)
|
||||
return config
|
||||
|
||||
|
||||
def main(sysargv: List[str]) -> None:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user