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Plot_profit.py: fix it and make it works with the new object model

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This commit is contained in:
Gerald Lonlas 2018-03-04 20:21:49 -08:00
parent 9ae2491b1e
commit 45341bb246
2 changed files with 104 additions and 82 deletions
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44
config.json.bak
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@ -1,44 +0,0 @@
{
"max_open_trades": 3,
"stake_currency": "BTC",
"stake_amount": 0.005,
"fiat_display_currency": "USD",
"dry_run": true,
"unfilledtimeout": 600,
"bid_strategy": {
"ask_last_balance": 0.0
},
"exchange": {
"name": "bittrex",
"key": "",
"secret": "",
"pair_whitelist": [
"BTC_ETH",
"BTC_LTC",
"BTC_ETC",
"BTC_DASH",
"BTC_ZEC",
"BTC_XLM",
"BTC_NXT",
"BTC_POWR",
"BTC_ADA",
"BTC_XMR"
],
"pair_blacklist": [
"BTC_DOGE"
]
},
"experimental": {
"use_sell_signal": false,
"sell_profit_only": false
},
"telegram": {
"enabled": true,
"token": "387056091:AAEVz29u5KwphICqGB6c63RwZjqCd7Kh6T4",
"chat_id": "391939601"
},
"initial_state": "running",
"internals": {
"process_throttle_secs": 5
}
}

142
scripts/plot_profit.py
View File

@ -1,31 +1,43 @@
#!/usr/bin/env python3
"""
Script to display profits
Mandatory Cli parameters:
-p / --pair: pair to examine
Optional Cli parameters
-c / --config: specify configuration file
-s / --strategy: strategy to use
--timerange: specify what timerange of data to use.
"""
import sys
import json
from typing import Dict
import numpy as np
from datetime import datetime
from plotly import tools
from plotly.offline import plot
import plotly.graph_objs as go
from freqtrade.arguments import Arguments
from freqtrade.configuration import Configuration
from freqtrade.analyze import Analyze
from freqtrade.logger import Logger
import freqtrade.optimize as optimize
import freqtrade.misc as misc
from freqtrade.strategy.strategy import Strategy
import pprint
def plot_parse_args(args):
parser = misc.common_args_parser('Graph profits')
# FIX: perhaps delete those backtesting options that are not feasible (shows up in -h)
misc.backtesting_options(parser)
misc.scripts_options(parser)
return parser.parse_args(args)
logger = Logger(name="Graph profits").get_logger()
# data:: [ pair, profit-%, enter, exit, time, duration]
# data:: ['BTC_XMR', 0.00537847, '1511176800', '1511178000', 5057, 1]
# FIX: make use of the enter/exit dates to insert the
# profit more precisely into the pg array
def make_profit_array(data, px, filter_pairs=[]):
# data:: ["BTC_ETH", 0.0023975, "1515598200", "1515602100", "2018-01-10 07:30:00+00:00", 65]
def make_profit_array(data, px, min_date, interval, filter_pairs=[]):
pg = np.zeros(px)
# Go through the trades
# and make an total profit
@ -35,10 +47,11 @@ def make_profit_array(data, px, filter_pairs=[]):
if filter_pairs and pair not in filter_pairs:
continue
profit = trade[1]
tim = trade[4]
dur = trade[5]
ix = tim + dur - 1
trade_sell_time = int(trade[3])
ix = define_index(min_date, trade_sell_time, interval)
if ix < px:
logger.debug('[%s]: Add profit %s on %s', pair, profit, trade[4])
pg[ix] += profit
# rewrite the pg array to go from
@ -64,47 +77,62 @@ def plot_profit(args) -> None:
# We need to use the same pairs, same tick_interval
# and same timeperiod as used in backtesting
# to match the tickerdata against the profits-results
timerange = Arguments.parse_timerange(args.timerange)
filter_pairs = args.pair
config = misc.load_config(args.config)
config.update({'strategy': args.strategy})
config = Configuration(args).get_config()
# Init strategy
strategy = Strategy()
strategy.init(config)
try:
analyze = Analyze({'strategy': config.get('strategy')})
except AttributeError:
logger.critical(
'Impossible to load the strategy. Please check the file "user_data/strategies/%s.py"',
config.get('strategy')
)
exit()
# Take pairs from the cli otherwise switch to the pair in the config file
if args.pair:
filter_pairs = args.pair
filter_pairs = filter_pairs.split(',')
else:
filter_pairs = config['exchange']['pair_whitelist']
tick_interval = analyze.strategy.ticker_interval
pairs = config['exchange']['pair_whitelist']
if filter_pairs:
filter_pairs = filter_pairs.split(',')
pairs = list(set(pairs) & set(filter_pairs))
print('Filter, keep pairs %s' % pairs)
logger.info('Filter, keep pairs %s' % pairs)
timerange = misc.parse_timerange(args.timerange)
tickers = optimize.load_data(args.datadir, pairs=pairs,
ticker_interval=strategy.ticker_interval,
refresh_pairs=False,
timerange=timerange)
dataframes = optimize.preprocess(tickers)
tickers = optimize.load_data(
datadir=args.datadir,
pairs=pairs,
ticker_interval=tick_interval,
refresh_pairs=False,
timerange=timerange
)
dataframes = analyze.tickerdata_to_dataframe(tickers)
# NOTE: the dataframes are of unequal length,
# 'dates' is an merged date array of them all.
dates = misc.common_datearray(dataframes)
max_x = dates.size
min_date = int(min(dates).timestamp())
max_date = int(max(dates).timestamp())
num_iterations = define_index(min_date, max_date, tick_interval) + 1
# Make an average close price of all the pairs that was involved.
# this could be useful to gauge the overall market trend
# We are essentially saying:
# array <- sum dataframes[*]['close'] / num_items dataframes
# FIX: there should be some onliner numpy/panda for this
avgclose = np.zeros(max_x)
avgclose = np.zeros(num_iterations)
num = 0
for pair, pair_data in dataframes.items():
close = pair_data['close']
maxprice = max(close) # Normalize price to [0,1]
print('Pair %s has length %s' % (pair, len(close)))
logger.info('Pair %s has length %s' % (pair, len(close)))
for x in range(0, len(close)):
avgclose[x] += close[x] / maxprice
# avgclose += close
@ -114,10 +142,16 @@ def plot_profit(args) -> None:
# Load the profits results
# And make an profits-growth array
filename = 'backtest-result.json'
with open(filename) as file:
data = json.load(file)
pg = make_profit_array(data, max_x, filter_pairs)
try:
filename = 'backtest-result.json'
with open(filename) as file:
data = json.load(file)
except FileNotFoundError:
logger.critical('File "backtest-result.json" not found. This script require backtesting '
'results to run.\nPlease run a backtesting with the parameter --export.')
exit(0)
pg = make_profit_array(data, num_iterations, min_date, tick_interval, filter_pairs)
#
# Plot the pairs average close prices, and total profit growth
@ -128,6 +162,7 @@ def plot_profit(args) -> None:
y=avgclose,
name='Avg close price',
)
profit = go.Scattergl(
x=dates,
y=pg,
@ -140,7 +175,7 @@ def plot_profit(args) -> None:
fig.append_trace(profit, 2, 1)
for pair in pairs:
pg = make_profit_array(data, max_x, pair)
pg = make_profit_array(data, num_iterations, min_date, tick_interval, pair)
pair_profit = go.Scattergl(
x=dates,
y=pg,
@ -151,6 +186,37 @@ def plot_profit(args) -> None:
plot(fig, filename='freqtrade-profit-plot.html')
def define_index(min_date, max_date, interval):
"""
Return the index of a specific date
"""
return int((max_date - min_date) / (interval * 60))
def plot_parse_args(args):
"""
Parse args passed to the script
:param args: Cli arguments
:return: args: Array with all arguments
"""
arguments = Arguments(args, 'Graph profits')
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: Dict) -> None:
"""
This function will initiate the bot and start the trading loop.
:return: None
"""
logger.info('Starting Plot Dataframe')
plot_profit(
plot_parse_args(sysargv)
)
if __name__ == '__main__':
args = plot_parse_args(sys.argv[1:])
plot_profit(args)
main(sys.argv[1:])