mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-14 04:03:55 +00:00
394 lines
13 KiB
Python
394 lines
13 KiB
Python
import logging
|
|
from pathlib import Path
|
|
from typing import Any, Dict, List
|
|
|
|
import pandas as pd
|
|
from freqtrade.configuration import TimeRange
|
|
from freqtrade.data import history
|
|
from freqtrade.data.btanalysis import (combine_tickers_with_mean,
|
|
create_cum_profit,
|
|
extract_trades_of_period, load_trades)
|
|
from freqtrade.resolvers import StrategyResolver
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
try:
|
|
from plotly.subplots import make_subplots
|
|
from plotly.offline import plot
|
|
import plotly.graph_objects as go
|
|
except ImportError:
|
|
logger.exception("Module plotly not found \n Please install using `pip3 install plotly`")
|
|
exit(1)
|
|
|
|
|
|
def init_plotscript(config):
|
|
"""
|
|
Initialize objects needed for plotting
|
|
:return: Dict with tickers, trades and pairs
|
|
"""
|
|
|
|
if "pairs" in config:
|
|
pairs = config["pairs"]
|
|
else:
|
|
pairs = config["exchange"]["pair_whitelist"]
|
|
|
|
# Set timerange to use
|
|
timerange = TimeRange.parse_timerange(config.get("timerange"))
|
|
|
|
tickers = history.load_data(
|
|
datadir=config.get("datadir"),
|
|
pairs=pairs,
|
|
timeframe=config.get('ticker_interval', '5m'),
|
|
timerange=timerange,
|
|
)
|
|
|
|
trades = load_trades(config['trade_source'],
|
|
db_url=config.get('db_url'),
|
|
exportfilename=config.get('exportfilename'),
|
|
)
|
|
trades = history.trim_dataframe(trades, timerange, 'open_time')
|
|
return {"tickers": tickers,
|
|
"trades": trades,
|
|
"pairs": pairs,
|
|
}
|
|
|
|
|
|
def add_indicators(fig, row, indicators: List[str], data: pd.DataFrame) -> make_subplots:
|
|
"""
|
|
Generator all the indicator selected by the user for a specific row
|
|
:param fig: Plot figure to append to
|
|
:param row: row number for this plot
|
|
:param indicators: List of indicators present in the dataframe
|
|
:param data: candlestick DataFrame
|
|
"""
|
|
for indicator in indicators:
|
|
if indicator in data:
|
|
scatter = go.Scatter(
|
|
x=data['date'],
|
|
y=data[indicator].values,
|
|
mode='lines',
|
|
name=indicator
|
|
)
|
|
fig.add_trace(scatter, row, 1)
|
|
else:
|
|
logger.info(
|
|
'Indicator "%s" ignored. Reason: This indicator is not found '
|
|
'in your strategy.',
|
|
indicator
|
|
)
|
|
|
|
return fig
|
|
|
|
|
|
def add_profit(fig, row, data: pd.DataFrame, column: str, name: str) -> make_subplots:
|
|
"""
|
|
Add profit-plot
|
|
:param fig: Plot figure to append to
|
|
:param row: row number for this plot
|
|
:param data: candlestick DataFrame
|
|
:param column: Column to use for plot
|
|
:param name: Name to use
|
|
:return: fig with added profit plot
|
|
"""
|
|
profit = go.Scatter(
|
|
x=data.index,
|
|
y=data[column],
|
|
name=name,
|
|
)
|
|
fig.add_trace(profit, row, 1)
|
|
|
|
return fig
|
|
|
|
|
|
def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
|
|
"""
|
|
Add trades to "fig"
|
|
"""
|
|
# Trades can be empty
|
|
if trades is not None and len(trades) > 0:
|
|
trade_buys = go.Scatter(
|
|
x=trades["open_time"],
|
|
y=trades["open_rate"],
|
|
mode='markers',
|
|
name='trade_buy',
|
|
marker=dict(
|
|
symbol='square-open',
|
|
size=11,
|
|
line=dict(width=2),
|
|
color='green'
|
|
)
|
|
)
|
|
# Create description for sell summarizing the trade
|
|
desc = trades.apply(lambda row: f"{round(row['profitperc'], 3)}%, {row['sell_reason']}, "
|
|
f"{row['duration']} min",
|
|
axis=1)
|
|
trade_sells = go.Scatter(
|
|
x=trades["close_time"],
|
|
y=trades["close_rate"],
|
|
text=desc,
|
|
mode='markers',
|
|
name='trade_sell',
|
|
marker=dict(
|
|
symbol='square-open',
|
|
size=11,
|
|
line=dict(width=2),
|
|
color='red'
|
|
)
|
|
)
|
|
fig.add_trace(trade_buys, 1, 1)
|
|
fig.add_trace(trade_sells, 1, 1)
|
|
else:
|
|
logger.warning("No trades found.")
|
|
return fig
|
|
|
|
|
|
def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFrame = None,
|
|
indicators1: List[str] = [],
|
|
indicators2: List[str] = [],) -> go.Figure:
|
|
"""
|
|
Generate the graph from the data generated by Backtesting or from DB
|
|
Volume will always be ploted in row2, so Row 1 and 3 are to our disposal for custom indicators
|
|
:param pair: Pair to Display on the graph
|
|
:param data: OHLCV DataFrame containing indicators and buy/sell signals
|
|
:param trades: All trades created
|
|
:param indicators1: List containing Main plot indicators
|
|
:param indicators2: List containing Sub plot indicators
|
|
:return: None
|
|
"""
|
|
|
|
# Define the graph
|
|
fig = make_subplots(
|
|
rows=3,
|
|
cols=1,
|
|
shared_xaxes=True,
|
|
row_width=[1, 1, 4],
|
|
vertical_spacing=0.0001,
|
|
)
|
|
fig['layout'].update(title=pair)
|
|
fig['layout']['yaxis1'].update(title='Price')
|
|
fig['layout']['yaxis2'].update(title='Volume')
|
|
fig['layout']['yaxis3'].update(title='Other')
|
|
fig['layout']['xaxis']['rangeslider'].update(visible=False)
|
|
|
|
# Common information
|
|
candles = go.Candlestick(
|
|
x=data.date,
|
|
open=data.open,
|
|
high=data.high,
|
|
low=data.low,
|
|
close=data.close,
|
|
name='Price'
|
|
)
|
|
fig.add_trace(candles, 1, 1)
|
|
|
|
if 'buy' in data.columns:
|
|
df_buy = data[data['buy'] == 1]
|
|
if len(df_buy) > 0:
|
|
buys = go.Scatter(
|
|
x=df_buy.date,
|
|
y=df_buy.close,
|
|
mode='markers',
|
|
name='buy',
|
|
marker=dict(
|
|
symbol='triangle-up-dot',
|
|
size=9,
|
|
line=dict(width=1),
|
|
color='green',
|
|
)
|
|
)
|
|
fig.add_trace(buys, 1, 1)
|
|
else:
|
|
logger.warning("No buy-signals found.")
|
|
|
|
if 'sell' in data.columns:
|
|
df_sell = data[data['sell'] == 1]
|
|
if len(df_sell) > 0:
|
|
sells = go.Scatter(
|
|
x=df_sell.date,
|
|
y=df_sell.close,
|
|
mode='markers',
|
|
name='sell',
|
|
marker=dict(
|
|
symbol='triangle-down-dot',
|
|
size=9,
|
|
line=dict(width=1),
|
|
color='red',
|
|
)
|
|
)
|
|
fig.add_trace(sells, 1, 1)
|
|
else:
|
|
logger.warning("No sell-signals found.")
|
|
|
|
# TODO: Figure out why scattergl causes problems plotly/plotly.js#2284
|
|
if 'bb_lowerband' in data and 'bb_upperband' in data:
|
|
bb_lower = go.Scatter(
|
|
x=data.date,
|
|
y=data.bb_lowerband,
|
|
showlegend=False,
|
|
line={'color': 'rgba(255,255,255,0)'},
|
|
)
|
|
bb_upper = go.Scatter(
|
|
x=data.date,
|
|
y=data.bb_upperband,
|
|
name='Bollinger Band',
|
|
fill="tonexty",
|
|
fillcolor="rgba(0,176,246,0.2)",
|
|
line={'color': 'rgba(255,255,255,0)'},
|
|
)
|
|
fig.add_trace(bb_lower, 1, 1)
|
|
fig.add_trace(bb_upper, 1, 1)
|
|
if 'bb_upperband' in indicators1 and 'bb_lowerband' in indicators1:
|
|
indicators1.remove('bb_upperband')
|
|
indicators1.remove('bb_lowerband')
|
|
|
|
# Add indicators to main plot
|
|
fig = add_indicators(fig=fig, row=1, indicators=indicators1, data=data)
|
|
|
|
fig = plot_trades(fig, trades)
|
|
|
|
# Volume goes to row 2
|
|
volume = go.Bar(
|
|
x=data['date'],
|
|
y=data['volume'],
|
|
name='Volume',
|
|
marker_color='DarkSlateGrey',
|
|
marker_line_color='DarkSlateGrey'
|
|
)
|
|
fig.add_trace(volume, 2, 1)
|
|
|
|
# Add indicators to separate row
|
|
fig = add_indicators(fig=fig, row=3, indicators=indicators2, data=data)
|
|
|
|
return fig
|
|
|
|
|
|
def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
|
|
trades: pd.DataFrame, timeframe: str) -> go.Figure:
|
|
# Combine close-values for all pairs, rename columns to "pair"
|
|
df_comb = combine_tickers_with_mean(tickers, "close")
|
|
|
|
# Add combined cumulative profit
|
|
df_comb = create_cum_profit(df_comb, trades, 'cum_profit', timeframe)
|
|
|
|
# Plot the pairs average close prices, and total profit growth
|
|
avgclose = go.Scatter(
|
|
x=df_comb.index,
|
|
y=df_comb['mean'],
|
|
name='Avg close price',
|
|
)
|
|
|
|
fig = make_subplots(rows=3, cols=1, shared_xaxes=True,
|
|
row_width=[1, 1, 1],
|
|
vertical_spacing=0.05,
|
|
subplot_titles=["AVG Close Price", "Combined Profit", "Profit per pair"])
|
|
fig['layout'].update(title="Freqtrade Profit plot")
|
|
fig['layout']['yaxis1'].update(title='Price')
|
|
fig['layout']['yaxis2'].update(title='Profit')
|
|
fig['layout']['yaxis3'].update(title='Profit')
|
|
fig['layout']['xaxis']['rangeslider'].update(visible=False)
|
|
|
|
fig.add_trace(avgclose, 1, 1)
|
|
fig = add_profit(fig, 2, df_comb, 'cum_profit', 'Profit')
|
|
|
|
for pair in pairs:
|
|
profit_col = f'cum_profit_{pair}'
|
|
df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col, timeframe)
|
|
|
|
fig = add_profit(fig, 3, df_comb, profit_col, f"Profit {pair}")
|
|
|
|
return fig
|
|
|
|
|
|
def generate_plot_filename(pair, timeframe) -> str:
|
|
"""
|
|
Generate filenames per pair/timeframe to be used for storing plots
|
|
"""
|
|
pair_name = pair.replace("/", "_")
|
|
file_name = 'freqtrade-plot-' + pair_name + '-' + timeframe + '.html'
|
|
|
|
logger.info('Generate plot file for %s', pair)
|
|
|
|
return file_name
|
|
|
|
|
|
def store_plot_file(fig, filename: str, directory: Path, auto_open: bool = False) -> None:
|
|
"""
|
|
Generate a plot html file from pre populated fig plotly object
|
|
:param fig: Plotly Figure to plot
|
|
:param filename: Name to store the file as
|
|
:param directory: Directory to store the file in
|
|
:param auto_open: Automatically open files saved
|
|
:return: None
|
|
"""
|
|
directory.mkdir(parents=True, exist_ok=True)
|
|
|
|
_filename = directory.joinpath(filename)
|
|
plot(fig, filename=str(_filename),
|
|
auto_open=auto_open)
|
|
logger.info(f"Stored plot as {_filename}")
|
|
|
|
|
|
def load_and_plot_trades(config: Dict[str, Any]):
|
|
"""
|
|
From configuration provided
|
|
- Initializes plot-script
|
|
- Get tickers data
|
|
- Generate Dafaframes populated with indicators and signals based on configured strategy
|
|
- Load trades excecuted during the selected period
|
|
- Generate Plotly plot objects
|
|
- Generate plot files
|
|
:return: None
|
|
"""
|
|
strategy = StrategyResolver.load_strategy(config)
|
|
|
|
plot_elements = init_plotscript(config)
|
|
trades = plot_elements['trades']
|
|
pair_counter = 0
|
|
for pair, data in plot_elements["tickers"].items():
|
|
pair_counter += 1
|
|
logger.info("analyse pair %s", pair)
|
|
tickers = {}
|
|
tickers[pair] = data
|
|
|
|
dataframe = strategy.analyze_ticker(tickers[pair], {'pair': pair})
|
|
trades_pair = trades.loc[trades['pair'] == pair]
|
|
trades_pair = extract_trades_of_period(dataframe, trades_pair)
|
|
|
|
fig = generate_candlestick_graph(
|
|
pair=pair,
|
|
data=dataframe,
|
|
trades=trades_pair,
|
|
indicators1=config["indicators1"],
|
|
indicators2=config["indicators2"],
|
|
)
|
|
|
|
store_plot_file(fig, filename=generate_plot_filename(pair, config['ticker_interval']),
|
|
directory=config['user_data_dir'] / "plot")
|
|
|
|
logger.info('End of plotting process. %s plots generated', pair_counter)
|
|
|
|
|
|
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
|
|
# Remove open pairs - we don't know the profit yet so can't calculate profit for these.
|
|
# Also, If only one open pair is left, then the profit-generation would fail.
|
|
trades = trades[(trades['pair'].isin(plot_elements["pairs"]))
|
|
& (~trades['close_time'].isnull())
|
|
]
|
|
|
|
# Create an average close price of all the pairs that were involved.
|
|
# this could be useful to gauge the overall market trend
|
|
fig = generate_profit_graph(plot_elements["pairs"], plot_elements["tickers"],
|
|
trades, config.get('ticker_interval', '5m'))
|
|
store_plot_file(fig, filename='freqtrade-profit-plot.html',
|
|
directory=config['user_data_dir'] / "plot", auto_open=True)
|