382 lines
12 KiB
Python
382 lines
12 KiB
Python
import logging
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from pathlib import Path
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from typing import Any, Dict, List
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import pandas as pd
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from freqtrade.configuration import TimeRange
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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,
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extract_trades_of_period, load_trades)
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from freqtrade.resolvers import StrategyResolver
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logger = logging.getLogger(__name__)
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try:
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from plotly.subplots import make_subplots
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from plotly.offline import plot
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import plotly.graph_objects as go
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except ImportError:
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logger.exception("Module plotly not found \n Please install using `pip3 install plotly`")
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exit(1)
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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 and pairs
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"""
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if "pairs" in config:
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pairs = config["pairs"]
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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 = TimeRange.parse_timerange(config.get("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.get('ticker_interval', '5m'),
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timerange=timerange,
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)
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trades = load_trades(config['trade_source'],
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db_url=config.get('db_url'),
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exportfilename=config.get('exportfilename'),
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)
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return {"tickers": tickers,
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"trades": trades,
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"pairs": pairs,
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}
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def add_indicators(fig, row, indicators: List[str], data: pd.DataFrame) -> 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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:param row: row number for this plot
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:param indicators: List of indicators present in the dataframe
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:param data: candlestick DataFrame
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"""
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for indicator in indicators:
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if indicator in data:
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# TODO: Figure out why scattergl causes problems
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scattergl = go.Scatter(
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x=data['date'],
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y=data[indicator].values,
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mode='lines',
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name=indicator
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)
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fig.add_trace(scattergl, row, 1)
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else:
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logger.info(
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'Indicator "%s" ignored. Reason: This indicator is not found '
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'in your strategy.',
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indicator
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)
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return fig
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def add_profit(fig, row, data: pd.DataFrame, column: str, name: str) -> make_subplots:
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"""
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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.add_trace(profit, row, 1)
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return fig
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def plot_trades(fig, trades: pd.DataFrame) -> 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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trade_buys = go.Scatter(
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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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marker=dict(
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symbol='square-open',
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size=11,
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line=dict(width=2),
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color='green'
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)
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)
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# Create description for sell summarizing the trade
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desc = trades.apply(lambda row: f"{round(row['profitperc'], 3)}%, {row['sell_reason']}, "
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f"{row['duration']}min",
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axis=1)
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trade_sells = go.Scatter(
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x=trades["close_time"],
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y=trades["close_rate"],
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text=desc,
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mode='markers',
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name='trade_sell',
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marker=dict(
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symbol='square-open',
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size=11,
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line=dict(width=2),
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color='red'
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)
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)
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fig.add_trace(trade_buys, 1, 1)
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fig.add_trace(trade_sells, 1, 1)
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else:
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logger.warning("No trades found.")
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return fig
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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] = [],) -> 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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:param pair: Pair to Display on the graph
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:param data: OHLCV DataFrame containing indicators and buy/sell signals
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:param trades: All trades created
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:param indicators1: List containing Main plot indicators
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:param indicators2: List containing Sub plot indicators
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:return: None
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"""
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# Define the graph
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fig = make_subplots(
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rows=3,
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cols=1,
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shared_xaxes=True,
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row_width=[1, 1, 4],
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vertical_spacing=0.0001,
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)
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fig['layout'].update(title=pair)
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fig['layout']['yaxis1'].update(title='Price')
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fig['layout']['yaxis2'].update(title='Volume')
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fig['layout']['yaxis3'].update(title='Other')
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fig['layout']['xaxis']['rangeslider'].update(visible=False)
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# Common information
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candles = go.Candlestick(
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x=data.date,
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open=data.open,
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high=data.high,
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low=data.low,
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close=data.close,
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name='Price'
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)
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fig.add_trace(candles, 1, 1)
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if 'buy' in data.columns:
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df_buy = data[data['buy'] == 1]
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if len(df_buy) > 0:
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buys = go.Scatter(
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x=df_buy.date,
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y=df_buy.close,
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mode='markers',
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name='buy',
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marker=dict(
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symbol='triangle-up-dot',
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size=9,
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line=dict(width=1),
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color='green',
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)
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)
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fig.add_trace(buys, 1, 1)
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else:
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logger.warning("No buy-signals found.")
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if 'sell' in data.columns:
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df_sell = data[data['sell'] == 1]
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if len(df_sell) > 0:
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sells = go.Scatter(
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x=df_sell.date,
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y=df_sell.close,
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mode='markers',
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name='sell',
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marker=dict(
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symbol='triangle-down-dot',
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size=9,
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line=dict(width=1),
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color='red',
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)
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)
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fig.add_trace(sells, 1, 1)
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else:
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logger.warning("No sell-signals found.")
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if 'bb_lowerband' in data and 'bb_upperband' in data:
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bb_lower = go.Scattergl(
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x=data.date,
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y=data.bb_lowerband,
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name='BB lower',
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line={'color': 'rgba(255,255,255,0)'},
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)
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bb_upper = go.Scattergl(
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x=data.date,
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y=data.bb_upperband,
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name='BB upper',
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fill="tonexty",
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fillcolor="rgba(0,176,246,0.2)",
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line={'color': 'rgba(255,255,255,0)'},
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)
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fig.add_trace(bb_lower, 1, 1)
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fig.add_trace(bb_upper, 1, 1)
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# Add indicators to main plot
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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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# Volume goes to row 2
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volume = go.Bar(
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x=data['date'],
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y=data['volume'],
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name='Volume'
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)
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fig.add_trace(volume, 2, 1)
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# Add indicators to seperate row
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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_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 = 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.add_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, directory: Path, 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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:param pair: Pair to plot (used as filename and Plot title)
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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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directory.mkdir(parents=True, exist_ok=True)
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_filename = directory.joinpath(filename)
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plot(fig, filename=str(_filename),
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auto_open=auto_open)
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logger.info(f"Stored plot as {_filename}")
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def analyse_and_plot_pairs(config: Dict[str, Any]):
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"""
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From configuration provided
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- Initializes plot-script
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- Get tickers data
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- Generate Dafaframes populated with indicators and signals based on configured strategy
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- Load trades excecuted during the selected period
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- Generate Plotly plot objects
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- Generate plot files
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:return: None
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"""
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strategy = StrategyResolver(config).strategy
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plot_elements = init_plotscript(config)
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trades = plot_elements['trades']
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pair_counter = 0
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for pair, data in plot_elements["tickers"].items():
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pair_counter += 1
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logger.info("analyse pair %s", pair)
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tickers = {}
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tickers[pair] = data
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dataframe = strategy.analyze_ticker(tickers[pair], {'pair': pair})
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trades_pair = trades.loc[trades['pair'] == pair]
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trades_pair = extract_trades_of_period(dataframe, trades_pair)
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fig = generate_candlestick_graph(
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pair=pair,
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data=dataframe,
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trades=trades_pair,
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indicators1=config["indicators1"],
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indicators2=config["indicators2"],
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)
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store_plot_file(fig, filename=generate_plot_filename(pair, config['ticker_interval']),
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directory=config['user_data_dir'] / "plot")
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logger.info('End of plotting process. %s plots generated', pair_counter)
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def plot_profit(config: Dict[str, Any]) -> None:
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"""
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Plots the total profit for all pairs.
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Note, the profit calculation isn't realistic.
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But should be somewhat proportional, and therefor useful
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in helping out to find a good algorithm.
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"""
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plot_elements = init_plotscript(config)
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trades = load_trades(config['trade_source'],
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db_url=config.get('db_url'),
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exportfilename=config.get('exportfilename'),
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)
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# Filter trades to relevant pairs
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trades = trades[trades['pair'].isin(plot_elements["pairs"])]
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# Create an average close price of all the pairs that were involved.
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# this could be useful to gauge the overall market trend
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fig = generate_profit_graph(plot_elements["pairs"], plot_elements["tickers"], trades)
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store_plot_file(fig, filename='freqtrade-profit-plot.html',
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directory=config['user_data_dir'] / "plot", auto_open=True)
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