freqtrade_origin/scripts/plot_dataframe.py
peterkorodi 0c051b1b7a Make plot_dataframe able to show trades stored in database. (#692)
* Show trades stored in db on the graph
2018-05-19 09:14:42 +03:00

221 lines
5.9 KiB
Python
Executable File

#!/usr/bin/env python3
"""
Script to display when the bot will buy a specific pair
Mandatory Cli parameters:
-p / --pair: pair to examine
Optional Cli parameters
-s / --strategy: strategy to use
-d / --datadir: path to pair backtest data
--timerange: specify what timerange of data to use.
-l / --live: Live, to download the latest ticker for the pair
-db / --db-url: Show trades stored in database
"""
import logging
import sys
from argparse import Namespace
from typing import List
from plotly import tools
from plotly.offline import plot
import plotly.graph_objs as go
from typing import Dict, List, Any
from sqlalchemy import create_engine
from freqtrade.arguments import Arguments
from freqtrade.analyze import Analyze
from freqtrade import exchange
import freqtrade.optimize as optimize
from freqtrade import persistence
from freqtrade.persistence import Trade
logger = logging.getLogger(__name__)
_CONF: Dict[str, Any] = {}
def plot_analyzed_dataframe(args: Namespace) -> None:
"""
Calls analyze() and plots the returned dataframe
:return: None
"""
pair = args.pair.replace('-', '_')
timerange = Arguments.parse_timerange(args.timerange)
# Init strategy
try:
analyze = Analyze({'strategy': args.strategy})
except AttributeError:
logger.critical(
'Impossible to load the strategy. Please check the file "user_data/strategies/%s.py"',
args.strategy
)
exit()
tick_interval = analyze.strategy.ticker_interval
tickers = {}
if args.live:
logger.info('Downloading pair.')
# Init Bittrex to use public API
exchange.init({'key': '', 'secret': ''})
tickers[pair] = exchange.get_ticker_history(pair, tick_interval)
else:
tickers = optimize.load_data(
datadir=args.datadir,
pairs=[pair],
ticker_interval=tick_interval,
refresh_pairs=False,
timerange=timerange
)
dataframes = analyze.tickerdata_to_dataframe(tickers)
dataframe = dataframes[pair]
dataframe = analyze.populate_buy_trend(dataframe)
dataframe = analyze.populate_sell_trend(dataframe)
trades = []
if args.db_url:
engine = create_engine('sqlite:///' + args.db_url)
persistence.init(_CONF, engine)
trades = Trade.query.filter(Trade.pair.is_(pair)).all()
if len(dataframe.index) > 750:
logger.warning('Ticker contained more than 750 candles, clipping.')
data = dataframe.tail(750)
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=[t.open_date.isoformat() for t in trades],
y=[t.open_rate for t in trades],
mode='markers',
name='trade_buy',
marker=dict(
symbol='square-open',
size=11,
line=dict(width=2),
color='green'
)
)
trade_sells = go.Scattergl(
x=[t.close_date.isoformat() for t in trades],
y=[t.close_rate for t in trades],
mode='markers',
name='trade_sell',
marker=dict(
symbol='square-open',
size=11,
line=dict(width=2),
color='red'
)
)
bb_lower = go.Scatter(
x=data.date,
y=data.bb_lowerband,
name='BB lower',
line={'color': "transparent"},
)
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': "transparent"},
)
macd = go.Scattergl(x=data['date'], y=data['macd'], name='MACD')
macdsignal = go.Scattergl(x=data['date'], y=data['macdsignal'], name='MACD signal')
volume = go.Bar(x=data['date'], y=data['volume'], name='Volume')
fig = tools.make_subplots(
rows=3,
cols=1,
shared_xaxes=True,
row_width=[1, 1, 4],
vertical_spacing=0.0001,
)
fig.append_trace(candles, 1, 1)
fig.append_trace(bb_lower, 1, 1)
fig.append_trace(bb_upper, 1, 1)
fig.append_trace(buys, 1, 1)
fig.append_trace(sells, 1, 1)
fig.append_trace(volume, 2, 1)
fig.append_trace(macd, 3, 1)
fig.append_trace(macdsignal, 3, 1)
fig.append_trace(trade_buys, 1, 1)
fig.append_trace(trade_sells, 1, 1)
fig['layout'].update(title=args.pair)
fig['layout']['yaxis1'].update(title='Price')
fig['layout']['yaxis2'].update(title='Volume')
fig['layout']['yaxis3'].update(title='MACD')
plot(fig, filename='freqtrade-plot.html')
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.common_args_parser()
arguments.optimizer_shared_options(arguments.parser)
arguments.backtesting_options(arguments.parser)
return arguments.parse_args()
def main(sysargv: List[str]) -> None:
"""
This function will initiate the bot and start the trading loop.
:return: None
"""
logger.info('Starting Plot Dataframe')
plot_analyzed_dataframe(
plot_parse_args(sysargv)
)
if __name__ == '__main__':
main(sys.argv[1:])