Revise plot_profit to use pandas functions where possible
This commit is contained in:
Matthias
parent
8aa327cb8a
commit
c3db4ebbc3
@ -4,64 +4,28 @@ Script to display profits
|
||||
|
||||
Use `python plot_profit.py --help` to display the command line arguments
|
||||
"""
|
||||
import json
|
||||
import logging
|
||||
import sys
|
||||
from argparse import Namespace
|
||||
from pathlib import Path
|
||||
from typing import List, Optional
|
||||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import plotly.graph_objs as go
|
||||
from plotly import tools
|
||||
from plotly.offline import plot
|
||||
|
||||
from freqtrade.arguments import Arguments, ARGS_PLOT_PROFIT
|
||||
from freqtrade.arguments import ARGS_PLOT_PROFIT, Arguments
|
||||
from freqtrade.configuration import Configuration
|
||||
from freqtrade.data import history
|
||||
from freqtrade.exchange import timeframe_to_seconds
|
||||
from freqtrade.misc import common_datearray
|
||||
from freqtrade.data.btanalysis import create_cum_profit, load_trades
|
||||
from freqtrade.plot.plotting import generate_plot_file
|
||||
from freqtrade.resolvers import StrategyResolver
|
||||
from freqtrade.state import RunMode
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# data:: [ pair, profit-%, enter, exit, time, duration]
|
||||
# data:: ["ETH/BTC", 0.0023975, "1515598200", "1515602100", "2018-01-10 07:30:00+00:00", 65]
|
||||
def make_profit_array(data: List, px: int, min_date: int,
|
||||
interval: str,
|
||||
filter_pairs: Optional[List] = None) -> np.ndarray:
|
||||
pg = np.zeros(px)
|
||||
filter_pairs = filter_pairs or []
|
||||
# Go through the trades
|
||||
# and make an total profit
|
||||
# array
|
||||
for trade in data:
|
||||
pair = trade[0]
|
||||
if filter_pairs and pair not in filter_pairs:
|
||||
continue
|
||||
profit = trade[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
|
||||
# total profits at each timeframe
|
||||
# to accumulated profits
|
||||
pa = 0
|
||||
for x in range(0, len(pg)):
|
||||
p = pg[x] # Get current total percent
|
||||
pa += p # Add to the accumulated percent
|
||||
pg[x] = pa # write back to save memory
|
||||
|
||||
return pg
|
||||
|
||||
|
||||
def plot_profit(args: Namespace) -> None:
|
||||
"""
|
||||
Plots the total profit for all pairs.
|
||||
@ -70,34 +34,15 @@ def plot_profit(args: Namespace) -> None:
|
||||
in helping out to find a good algorithm.
|
||||
"""
|
||||
|
||||
# We need to use the same pairs, same ticker_interval
|
||||
# and same timeperiod as used in backtesting
|
||||
# to match the tickerdata against the profits-results
|
||||
# We need to use the same pairs and the same ticker_interval
|
||||
# as used in backtesting / trading
|
||||
# to match the tickerdata against the results
|
||||
timerange = Arguments.parse_timerange(args.timerange)
|
||||
|
||||
config = Configuration(args, RunMode.OTHER).get_config()
|
||||
|
||||
# Init strategy
|
||||
try:
|
||||
strategy = StrategyResolver({'strategy': config.get('strategy')}).strategy
|
||||
|
||||
except AttributeError:
|
||||
logger.critical(
|
||||
'Impossible to load the strategy. Please check the file "user_data/strategies/%s.py"',
|
||||
config.get('strategy')
|
||||
)
|
||||
exit(1)
|
||||
|
||||
# Load the profits results
|
||||
try:
|
||||
filename = args.exportfilename
|
||||
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(1)
|
||||
strategy = StrategyResolver(config).strategy
|
||||
|
||||
# Take pairs from the cli otherwise switch to the pair in the config file
|
||||
if args.pairs:
|
||||
@ -106,6 +51,11 @@ def plot_profit(args: Namespace) -> None:
|
||||
else:
|
||||
filter_pairs = config['exchange']['pair_whitelist']
|
||||
|
||||
# Load the profits results
|
||||
trades = load_trades(config)
|
||||
|
||||
trades = trades[trades['pair'].isin(filter_pairs)]
|
||||
|
||||
ticker_interval = strategy.ticker_interval
|
||||
pairs = config['exchange']['pair_whitelist']
|
||||
|
||||
@ -120,49 +70,28 @@ def plot_profit(args: Namespace) -> None:
|
||||
refresh_pairs=False,
|
||||
timerange=timerange
|
||||
)
|
||||
dataframes = strategy.tickerdata_to_dataframe(tickers)
|
||||
|
||||
# NOTE: the dataframes are of unequal length,
|
||||
# 'dates' is an merged date array of them all.
|
||||
|
||||
dates = common_datearray(dataframes)
|
||||
min_date = int(min(dates).timestamp())
|
||||
max_date = int(max(dates).timestamp())
|
||||
num_iterations = define_index(min_date, max_date, ticker_interval) + 1
|
||||
|
||||
# Make an average close price of all the pairs that was involved.
|
||||
# Create an average close price of all the pairs that were 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(num_iterations)
|
||||
num = 0
|
||||
for pair, pair_data in dataframes.items():
|
||||
close = pair_data['close']
|
||||
maxprice = max(close) # Normalize price to [0,1]
|
||||
logger.info('Pair %s has length %s' % (pair, len(close)))
|
||||
for x in range(0, len(close)):
|
||||
avgclose[x] += close[x] / maxprice
|
||||
# avgclose += close
|
||||
num += 1
|
||||
avgclose /= num
|
||||
|
||||
# make an profits-growth array
|
||||
pg = make_profit_array(data, num_iterations, min_date, ticker_interval, filter_pairs)
|
||||
# Combine close-values for all pairs, rename columns to "pair"
|
||||
df_comb = pd.concat([tickers[pair].set_index('date').rename(
|
||||
{'close': pair}, axis=1)[pair] for pair in tickers], axis=1)
|
||||
df_comb['mean'] = df_comb.mean(axis=1)
|
||||
|
||||
# Add combined cumulative profit
|
||||
df_comb = create_cum_profit(df_comb, trades, 'cum_profit')
|
||||
|
||||
#
|
||||
# Plot the pairs average close prices, and total profit growth
|
||||
#
|
||||
|
||||
avgclose = go.Scattergl(
|
||||
x=dates,
|
||||
y=avgclose,
|
||||
x=df_comb.index,
|
||||
y=df_comb['mean'],
|
||||
name='Avg close price',
|
||||
)
|
||||
|
||||
profit = go.Scattergl(
|
||||
x=dates,
|
||||
y=pg,
|
||||
x=df_comb.index,
|
||||
y=df_comb['cum_profit'],
|
||||
name='Profit',
|
||||
)
|
||||
|
||||
@ -172,23 +101,19 @@ def plot_profit(args: Namespace) -> None:
|
||||
fig.append_trace(profit, 2, 1)
|
||||
|
||||
for pair in pairs:
|
||||
pg = make_profit_array(data, num_iterations, min_date, ticker_interval, [pair])
|
||||
profit_col = f'cum_profit_{pair}'
|
||||
df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col)
|
||||
|
||||
pair_profit = go.Scattergl(
|
||||
x=dates,
|
||||
y=pg,
|
||||
name=pair,
|
||||
x=df_comb.index,
|
||||
y=df_comb[profit_col],
|
||||
name=f"Profit {pair}",
|
||||
)
|
||||
fig.append_trace(pair_profit, 3, 1)
|
||||
|
||||
plot(fig, filename=str(Path('user_data').joinpath('freqtrade-profit-plot.html')))
|
||||
|
||||
|
||||
def define_index(min_date: int, max_date: int, ticker_interval: str) -> int:
|
||||
"""
|
||||
Return the index of a specific date
|
||||
"""
|
||||
interval_seconds = timeframe_to_seconds(ticker_interval)
|
||||
return int((max_date - min_date) / interval_seconds)
|
||||
generate_plot_file(fig,
|
||||
filename='freqtrade-profit-plot.html',
|
||||
auto_open=True)
|
||||
|
||||
|
||||
def plot_parse_args(args: List[str]) -> Namespace:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user