stable/scripts/plot_dataframe.py

446 lines
13 KiB
Python
Raw Normal View History

#!/usr/bin/env python3
"""
2019-01-25 05:42:29 +00:00
Script to display when the bot will buy on specific pair(s)
Mandatory Cli parameters:
2019-01-25 05:42:29 +00:00
-p / --pairs: pair(s) to examine
Option but recommended
-s / --strategy: strategy to use
Optional Cli parameters
2019-01-25 05:42:29 +00:00
-d / --datadir: path to pair(s) backtest data
--timerange: specify what timerange of data to use.
2019-01-25 05:42:29 +00:00
-l / --live: Live, to download the latest ticker for the pair(s)
-db / --db-url: Show trades stored in database
Indicators recommended
Row 1: sma, ema3, ema5, ema10, ema50
Row 3: macd, rsi, fisher_rsi, mfi, slowd, slowk, fastd, fastk
Example of usage:
2019-01-26 09:56:29 +00:00
> python3 scripts/plot_dataframe.py --pairs BTC/EUR,XRP/BTC -d user_data/data/
--indicators1 sma,ema3 --indicators2 fastk,fastd
"""
import json
import logging
import sys
from argparse import Namespace
from pathlib import Path
from typing import Dict, List, Any
import pandas as pd
import plotly.graph_objs as go
import pytz
from plotly import tools
from plotly.offline import plot
from freqtrade import persistence
from freqtrade.arguments import Arguments, TimeRange
2018-12-13 05:34:37 +00:00
from freqtrade.data import history
from freqtrade.data.btanalysis import load_backtest_data, BT_DATA_COLUMNS
from freqtrade.exchange import Exchange
from freqtrade.optimize.backtesting import setup_configuration
from freqtrade.persistence import Trade
from freqtrade.resolvers import StrategyResolver
logger = logging.getLogger(__name__)
_CONF: Dict[str, Any] = {}
timeZone = pytz.UTC
def load_trades(args: Namespace, pair: str, timerange: TimeRange) -> pd.DataFrame:
trades: pd.DataFrame = pd.DataFrame()
if args.db_url:
persistence.init(_CONF)
columns = ["pair", "profit", "open_time", "close_time",
"open_rate", "close_rate", "duration"]
for x in Trade.query.all():
print("date: {}".format(x.open_date))
trades = pd.DataFrame([(t.pair, t.calc_profit(),
t.open_date.replace(tzinfo=timeZone),
t.close_date.replace(tzinfo=timeZone) if t.close_date else None,
t.open_rate, t.close_rate,
2019-01-25 05:42:29 +00:00
t.close_date.timestamp() - t.open_date.timestamp()
if t.close_date else None)
for t in Trade.query.filter(Trade.pair.is_(pair)).all()],
columns=columns)
elif args.exportfilename:
file = Path(args.exportfilename)
if file.exists():
load_backtest_data(file)
2019-01-25 05:42:29 +00:00
else:
trades = pd.DataFrame([], columns=BT_DATA_COLUMNS)
2019-01-25 05:42:29 +00:00
return trades
2019-01-25 05:42:29 +00:00
def generate_plot_file(fig, pair, tick_interval, is_last) -> None:
"""
2019-01-25 05:42:29 +00:00
Generate a plot html file from pre populated fig plotly object
:return: None
"""
2019-01-25 05:42:29 +00:00
logger.info('Generate plot file for %s', pair)
pair_name = pair.replace("/", "_")
file_name = 'freqtrade-plot-' + pair_name + '-' + tick_interval + '.html'
Path("user_data/plots").mkdir(parents=True, exist_ok=True)
2019-01-25 05:42:29 +00:00
plot(fig, filename=str(Path('user_data/plots').joinpath(file_name)), auto_open=False)
if is_last:
plot(fig, filename=str(Path('user_data').joinpath('freqtrade-plot.html')), auto_open=False)
def get_trading_env(args: Namespace):
"""
Initalize freqtrade Exchange and Strategy, split pairs recieved in parameter
:return: Strategy
"""
global _CONF
# Load the configuration
_CONF.update(setup_configuration(args))
print(_CONF)
2019-01-25 05:42:29 +00:00
pairs = args.pairs.split(',')
if pairs is None:
logger.critical('Parameter --pairs mandatory;. E.g --pairs ETH/BTC,XRP/BTC')
exit()
# Load the strategy
try:
strategy = StrategyResolver(_CONF).strategy
exchange = Exchange(_CONF)
except AttributeError:
logger.critical(
'Impossible to load the strategy. Please check the file "user_data/strategies/%s.py"',
args.strategy
)
exit()
2019-01-25 05:42:29 +00:00
return [strategy, exchange, pairs]
def get_tickers_data(strategy, exchange, pairs: List[str], args):
"""
Get tickers data for each pairs on live or local, option defined in args
:return: dictinnary of tickers. output format: {'pair': tickersdata}
"""
tick_interval = strategy.ticker_interval
2019-01-25 05:42:29 +00:00
timerange = Arguments.parse_timerange(args.timerange)
tickers = {}
if args.live:
2019-01-25 05:42:29 +00:00
logger.info('Downloading pairs.')
exchange.refresh_latest_ohlcv([(pair, tick_interval) for pair in pairs])
2019-01-25 05:42:29 +00:00
for pair in pairs:
tickers[pair] = exchange.klines((pair, tick_interval))
else:
2018-12-13 05:34:37 +00:00
tickers = history.load_data(
2018-12-15 12:54:35 +00:00
datadir=Path(_CONF.get("datadir")),
2019-01-25 05:42:29 +00:00
pairs=pairs,
ticker_interval=tick_interval,
refresh_pairs=_CONF.get('refresh_pairs', False),
timerange=timerange,
exchange=Exchange(_CONF)
)
2019-01-25 05:42:29 +00:00
# No ticker found, impossible to download, len mismatch
for pair, data in tickers.copy().items():
logger.debug("checking tickers data of pair: %s", pair)
logger.debug("data.empty: %s", data.empty)
logger.debug("len(data): %s", len(data))
if data.empty:
del tickers[pair]
logger.info(
'An issue occured while retreiving datas of %s pair, please retry '
'using -l option for live or --refresh-pairs-cached', pair)
return tickers
2019-01-25 05:42:29 +00:00
def generate_dataframe(strategy, tickers, pair) -> pd.DataFrame:
"""
Get tickers then Populate strategy indicators and signals, then return the full dataframe
:return: the DataFrame of a pair
"""
dataframes = strategy.tickerdata_to_dataframe(tickers)
dataframe = dataframes[pair]
dataframe = strategy.advise_buy(dataframe, {'pair': pair})
dataframe = strategy.advise_sell(dataframe, {'pair': pair})
2019-01-25 05:42:29 +00:00
return dataframe
2019-01-25 05:42:29 +00:00
def extract_trades_of_period(dataframe, trades) -> pd.DataFrame:
"""
Compare trades and backtested pair DataFrames to get trades performed on backtested period
:return: the DataFrame of a trades of period
"""
trades = trades.loc[trades['open_time'] >= dataframe.iloc[0]['date']]
2019-01-25 05:42:29 +00:00
return trades
def generate_graph(
pair: str,
trades: pd.DataFrame,
data: pd.DataFrame,
indicators1: str,
indicators2: str
) -> tools.make_subplots:
"""
Generate the graph from the data generated by Backtesting or from DB
:param pair: Pair to Display on the graph
:param trades: All trades created
:param data: Dataframe
:indicators1: String Main plot indicators
:indicators2: String Sub plot indicators
:return: None
"""
# Define the graph
fig = tools.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')
2019-01-25 05:42:29 +00:00
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'
)
df_buy = data[data['buy'] == 1]
buys = go.Scattergl(
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',
)
)
df_sell = data[data['sell'] == 1]
sells = go.Scattergl(
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',
)
)
trade_buys = go.Scattergl(
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'
)
)
trade_sells = go.Scattergl(
x=trades["close_time"],
y=trades["close_rate"],
mode='markers',
name='trade_sell',
marker=dict(
symbol='square-open',
size=11,
line=dict(width=2),
color='red'
)
)
# Row 1
fig.append_trace(candles, 1, 1)
if 'bb_lowerband' in data and 'bb_upperband' in data:
bb_lower = go.Scatter(
x=data.date,
y=data.bb_lowerband,
name='BB lower',
line={'color': 'rgba(255,255,255,0)'},
)
bb_upper = go.Scatter(
x=data.date,
y=data.bb_upperband,
name='BB upper',
fill="tonexty",
fillcolor="rgba(0,176,246,0.2)",
line={'color': 'rgba(255,255,255,0)'},
)
fig.append_trace(bb_lower, 1, 1)
fig.append_trace(bb_upper, 1, 1)
fig = generate_row(fig=fig, row=1, raw_indicators=indicators1, data=data)
fig.append_trace(buys, 1, 1)
fig.append_trace(sells, 1, 1)
fig.append_trace(trade_buys, 1, 1)
fig.append_trace(trade_sells, 1, 1)
# Row 2
volume = go.Bar(
x=data['date'],
y=data['volume'],
name='Volume'
)
fig.append_trace(volume, 2, 1)
# Row 3
fig = generate_row(fig=fig, row=3, raw_indicators=indicators2, data=data)
return fig
def generate_row(fig, row, raw_indicators, data) -> tools.make_subplots:
"""
Generator all the indicator selected by the user for a specific row
"""
for indicator in raw_indicators.split(','):
if indicator in data:
scattergl = go.Scattergl(
x=data['date'],
y=data[indicator],
name=indicator
)
fig.append_trace(scattergl, row, 1)
else:
logger.info(
'Indicator "%s" ignored. Reason: This indicator is not found '
'in your strategy.',
indicator
)
return fig
def plot_parse_args(args: List[str]) -> Namespace:
"""
Parse args passed to the script
:param args: Cli arguments
:return: args: Array with all arguments
"""
arguments = Arguments(args, 'Graph dataframe')
arguments.scripts_options()
arguments.parser.add_argument(
'--indicators1',
help='Set indicators from your strategy you want in the first row of the graph. Separate '
'them with a coma. E.g: ema3,ema5 (default: %(default)s)',
type=str,
default='sma,ema3,ema5',
dest='indicators1',
)
arguments.parser.add_argument(
'--indicators2',
help='Set indicators from your strategy you want in the third row of the graph. Separate '
'them with a coma. E.g: fastd,fastk (default: %(default)s)',
type=str,
2019-01-25 18:04:39 +00:00
default='macd,macdsignal',
dest='indicators2',
)
arguments.parser.add_argument(
'--plot-limit',
help='Specify tick limit for plotting - too high values cause huge files - '
'Default: %(default)s',
dest='plot_limit',
default=750,
type=int,
)
arguments.common_args_parser()
arguments.optimizer_shared_options(arguments.parser)
arguments.backtesting_options(arguments.parser)
return arguments.parse_args()
2019-01-25 05:42:29 +00:00
def analyse_and_plot_pairs(args: Namespace):
"""
From arguments provided in cli:
-Initialise backtest env
-Get tickers data
-Generate Dafaframes populated with indicators and signals
-Load trades excecuted on same periods
-Generate Plotly plot objects
-Generate plot files
:return: None
"""
strategy, exchange, pairs = get_trading_env(args)
# Set timerange to use
timerange = Arguments.parse_timerange(args.timerange)
tick_interval = strategy.ticker_interval
tickers = get_tickers_data(strategy, exchange, pairs, args)
pair_counter = 0
for pair, data in tickers.items():
pair_counter += 1
logger.info("analyse pair %s", pair)
tickers = {}
tickers[pair] = data
dataframe = generate_dataframe(strategy, tickers, pair)
trades = load_trades(args, pair, timerange)
trades = extract_trades_of_period(dataframe, trades)
fig = generate_graph(
pair=pair,
trades=trades,
data=dataframe,
indicators1=args.indicators1,
indicators2=args.indicators2
2019-01-25 05:42:29 +00:00
)
is_last = (False, True)[pair_counter == len(tickers)]
generate_plot_file(fig, pair, tick_interval, is_last)
logger.info('End of ploting process %s plots generated', pair_counter)
def main(sysargv: List[str]) -> None:
"""
This function will initiate the bot and start the trading loop.
:return: None
"""
logger.info('Starting Plot Dataframe')
2019-01-25 05:42:29 +00:00
analyse_and_plot_pairs(
plot_parse_args(sysargv)
)
2019-01-25 05:42:29 +00:00
exit()
if __name__ == '__main__':
main(sys.argv[1:])