297 lines
8.1 KiB
Python
Executable File
297 lines
8.1 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
|
|
"""
|
|
import datetime
|
|
import logging
|
|
import sys
|
|
from argparse import Namespace
|
|
from typing import List
|
|
|
|
import plotly.graph_objs as go
|
|
from plotly import tools
|
|
from plotly.offline import plot
|
|
|
|
import freqtrade.optimize as optimize
|
|
from freqtrade import exchange
|
|
from freqtrade.analyze import Analyze
|
|
from freqtrade.arguments import Arguments
|
|
from freqtrade.configuration import Configuration
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
def plot_stop_loss_trade(df_sell, fig, analyze, args):
|
|
"""
|
|
plots the stop loss for the associated trades and buys
|
|
as well as the estimated profit ranges.
|
|
|
|
will be enabled if --stop-loss is provided
|
|
as argument
|
|
|
|
:param data:
|
|
:param trades:
|
|
:return:
|
|
"""
|
|
|
|
if args.stoplossdisplay is False:
|
|
return
|
|
|
|
stoploss = analyze.strategy.stoploss
|
|
|
|
for index, x in df_sell.iterrows():
|
|
if x['associated_buy_price'] > 0:
|
|
# draw stop loss
|
|
fig['layout']['shapes'].append(
|
|
{
|
|
'fillcolor': 'red',
|
|
'opacity': 0.1,
|
|
'type': 'rect',
|
|
'x0': x['associated_buy_date'],
|
|
'x1': x['date'],
|
|
'y0': x['associated_buy_price'],
|
|
'y1': (x['associated_buy_price'] - abs(stoploss) * x['associated_buy_price']),
|
|
'line': {'color': 'red'}
|
|
}
|
|
)
|
|
|
|
totalTime = 0
|
|
for time in analyze.strategy.minimal_roi:
|
|
t = int(time)
|
|
totalTime = t + totalTime
|
|
|
|
enddate = x['date']
|
|
|
|
date = x['associated_buy_date'] + datetime.timedelta(minutes=totalTime)
|
|
|
|
# draw profit range
|
|
fig['layout']['shapes'].append(
|
|
{
|
|
'fillcolor': 'green',
|
|
'opacity': 0.1,
|
|
'type': 'rect',
|
|
'x0': date,
|
|
'x1': enddate,
|
|
'y0': x['associated_buy_price'],
|
|
'y1': x['associated_buy_price'] + x['associated_buy_price'] * analyze.strategy.minimal_roi[
|
|
time],
|
|
'line': {'color': 'green'}
|
|
}
|
|
)
|
|
|
|
|
|
def find_profits(data):
|
|
"""
|
|
finds the profits between sells and the associated buys. This does not take in account
|
|
ROI!
|
|
:param data:
|
|
:return:
|
|
"""
|
|
|
|
# go over all the sells
|
|
# find all previous buys
|
|
|
|
df_sell = data[data['sell'] == 1]
|
|
df_buys = data[data['buy'] == 1]
|
|
lastDate = data['date'].iloc[0]
|
|
|
|
for index, row in df_sell.iterrows():
|
|
|
|
buys = df_buys[(df_buys['date'] < row['date']) & (df_buys['date'] > lastDate)]
|
|
|
|
profit = None
|
|
if buys['date'].count() > 0:
|
|
buys = buys.tail()
|
|
profit = round(row['close'] / buys['close'].values[0] * 100 - 100, 2)
|
|
lastDate = row['date']
|
|
|
|
df_sell.loc[index, 'associated_buy_date'] = buys['date'].values[0]
|
|
df_sell.loc[index, 'associated_buy_price'] = buys['close'].values[0]
|
|
|
|
df_sell.loc[index, 'profit'] = profit
|
|
|
|
return df_sell
|
|
|
|
|
|
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:
|
|
config = Configuration(args)
|
|
|
|
analyze = Analyze(config.get_config())
|
|
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._API = exchange.Bittrex({'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)
|
|
|
|
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 * 0.995,
|
|
mode='markers',
|
|
name='buy',
|
|
marker=dict(
|
|
symbol='triangle-up-dot',
|
|
size=15,
|
|
line=dict(width=1),
|
|
color='green',
|
|
)
|
|
)
|
|
df_sell = find_profits(data)
|
|
|
|
sells = go.Scatter(
|
|
x=df_sell.date,
|
|
y=df_sell.close * 1.01,
|
|
mode='markers+text',
|
|
name='sell',
|
|
text=df_sell.profit,
|
|
textposition='top right',
|
|
marker=dict(
|
|
symbol='triangle-down-dot',
|
|
size=15,
|
|
line=dict(width=1),
|
|
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"},
|
|
)
|
|
bb_middle = go.Scatter(
|
|
x=data.date,
|
|
y=data.bb_middleband,
|
|
name='BB middle',
|
|
fill="tonexty",
|
|
fillcolor="rgba(0,176,246,0.2)",
|
|
line={'color': "red"},
|
|
)
|
|
|
|
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_middle, 1, 1)
|
|
fig.append_trace(bb_upper, 1, 1)
|
|
|
|
fig.append_trace(buys, 1, 1)
|
|
fig.append_trace(sells, 1, 1)
|
|
|
|
# append stop loss/profit
|
|
plot_stop_loss_trade(df_sell, fig, analyze,args)
|
|
|
|
fig.append_trace(volume, 2, 1)
|
|
fig.append_trace(macd, 3, 1)
|
|
fig.append_trace(macdsignal, 3, 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:])
|