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removing all codes from maindev branch in preparation for PR from freq/dev

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Nullart
2018-07-09 08:31:39 +08:00
parent e5cd756cca
commit 522c1a421d
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200
scripts/convert_backtestdata.py
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#!/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
-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 logging
import sys
from argparse import Namespace
from os import path
import glob
import json
import re
from typing import List, Dict
import gzip
from freqtrade.arguments import Arguments
from freqtrade import misc, constants
from pandas import DataFrame
import dateutil.parser
logger = logging.getLogger('freqtrade')
def load_old_file(filename) -> (List[Dict], bool):
if not path.isfile(filename):
logger.warning("filename %s does not exist", filename)
return (None, False)
logger.debug('Loading ticker data from file %s', filename)
pairdata = None
if filename.endswith('.gz'):
logger.debug('Loading ticker data from file %s', filename)
is_zip = True
with gzip.open(filename) as tickerdata:
pairdata = json.load(tickerdata)
else:
is_zip = False
with open(filename) as tickerdata:
pairdata = json.load(tickerdata)
return (pairdata, is_zip)
def parse_old_backtest_data(ticker) -> DataFrame:
"""
Reads old backtest data
Format: "O": 8.794e-05,
"H": 8.948e-05,
"L": 8.794e-05,
"C": 8.88e-05,
"V": 991.09056638,
"T": "2017-11-26T08:50:00",
"BV": 0.0877869
"""
columns = {'C': 'close', 'V': 'volume', 'O': 'open',
'H': 'high', 'L': 'low', 'T': 'date'}
frame = DataFrame(ticker) \
.rename(columns=columns)
if 'BV' in frame:
frame.drop('BV', 1, inplace=True)
if 'date' not in frame:
logger.warning("Date not in frame - probably not a Ticker file")
return None
frame.sort_values('date', inplace=True)
return frame
def convert_dataframe(frame: DataFrame):
"""Convert dataframe to new format"""
# reorder columns:
cols = ['date', 'open', 'high', 'low', 'close', 'volume']
frame = frame[cols]
# Make sure parsing/printing data is assumed to be UTC
frame['date'] = frame['date'].apply(
lambda d: int(dateutil.parser.parse(d+'+00:00').timestamp()) * 1000)
frame['date'] = frame['date'].astype('int64')
# Convert columns one by one to preserve type.
by_column = [frame[x].values.tolist() for x in frame.columns]
return list(list(x) for x in zip(*by_column))
def convert_file(filename: str, filename_new: str) -> None:
"""Converts a file from old format to ccxt format"""
(pairdata, is_zip) = load_old_file(filename)
if pairdata and type(pairdata) is list:
if type(pairdata[0]) is list:
logger.error("pairdata for %s already in new format", filename)
return
frame = parse_old_backtest_data(pairdata)
# Convert frame to new format
if frame is not None:
frame1 = convert_dataframe(frame)
misc.file_dump_json(filename_new, frame1, is_zip)
def convert_main(args: Namespace) -> None:
"""
converts a folder given in --datadir from old to new format to support ccxt
"""
workdir = path.join(args.datadir, "")
logger.info("Workdir: %s", workdir)
for filename in glob.glob(workdir + "*.json"):
# swap currency names
ret = re.search(r'[A-Z_]{7,}', path.basename(filename))
if args.norename:
filename_new = filename
else:
if not ret:
logger.warning("file %s could not be converted, could not extract currencies",
filename)
continue
pair = ret.group(0)
currencies = pair.split("_")
if len(currencies) != 2:
logger.warning("file %s could not be converted, could not extract currencies",
filename)
continue
ret_integer = re.search(r'\d+(?=\.json)', path.basename(filename))
ret_string = re.search(r'(\d+[mhdw])(?=\.json)', path.basename(filename))
if ret_integer:
minutes = int(ret_integer.group(0))
# default to adding 'm' to end of minutes for new interval name
interval = str(minutes) + 'm'
# but check if there is a mapping between int and string also
for str_interval, minutes_interval in constants.TICKER_INTERVAL_MINUTES.items():
if minutes_interval == minutes:
interval = str_interval
break
# change order on pairs if old ticker interval found
filename_new = path.join(path.dirname(filename),
f"{currencies[1]}_{currencies[0]}-{interval}.json")
elif ret_string:
interval = ret_string.group(0)
filename_new = path.join(path.dirname(filename),
f"{currencies[0]}_{currencies[1]}-{interval}.json")
else:
logger.warning("file %s could not be converted, interval not found", filename)
continue
logger.debug("Converting and renaming %s to %s", filename, filename_new)
convert_file(filename, filename_new)
def convert_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, 'Convert datafiles')
arguments.parser.add_argument(
'-d', '--datadir',
help='path to backtest data (default: %(default)s',
dest='datadir',
default=path.join('freqtrade', 'tests', 'testdata'),
type=str,
metavar='PATH',
)
arguments.parser.add_argument(
'-n', '--norename',
help='don''t rename files from BTC_<PAIR> to <PAIR>_BTC - '
'Note that not renaming will overwrite source files',
dest='norename',
default=False,
action='store_true'
)
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 Dataframe conversation')
convert_main(convert_parse_args(sysargv))
if __name__ == '__main__':
main(sys.argv[1:])

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scripts/download_backtest_data.py
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#!/usr/bin/env python3
"""This script generate json data from bittrex"""
import json
import sys
from pathlib import Path
import arrow
from freqtrade import arguments
from freqtrade.arguments import TimeRange
from freqtrade.exchange import Exchange
from freqtrade.optimize import download_backtesting_testdata
DEFAULT_DL_PATH = 'user_data/data'
arguments = arguments.Arguments(sys.argv[1:], 'download utility')
arguments.testdata_dl_options()
args = arguments.parse_args()
timeframes = args.timeframes
dl_path = Path(DEFAULT_DL_PATH).joinpath(args.exchange)
if args.export:
dl_path = Path(args.export)
if not dl_path.is_dir():
sys.exit(f'Directory {dl_path} does not exist.')
pairs_file = Path(args.pairs_file) if args.pairs_file else dl_path.joinpath('pairs.json')
if not pairs_file.exists():
sys.exit(f'No pairs file found with path {pairs_file}.')
with pairs_file.open() as file:
PAIRS = list(set(json.load(file)))
PAIRS.sort()
timerange = TimeRange()
if args.days:
time_since = arrow.utcnow().shift(days=-args.days).strftime("%Y%m%d")
timerange = arguments.parse_timerange(f'{time_since}-')
print(f'About to download pairs: {PAIRS} to {dl_path}')
# Init exchange
exchange = Exchange({'key': '',
'secret': '',
'stake_currency': '',
'dry_run': True,
'exchange': {
'name': args.exchange,
'pair_whitelist': []
}
})
pairs_not_available = []
for pair in PAIRS:
if pair not in exchange._api.markets:
pairs_not_available.append(pair)
print(f"skipping pair {pair}")
continue
for tick_interval in timeframes:
pair_print = pair.replace('/', '_')
filename = f'{pair_print}-{tick_interval}.json'
dl_file = dl_path.joinpath(filename)
if args.erase and dl_file.exists():
print(f'Deleting existing data for pair {pair}, interval {tick_interval}')
dl_file.unlink()
print(f'downloading pair {pair}, interval {tick_interval}')
download_backtesting_testdata(str(dl_path), exchange=exchange,
pair=pair,
tick_interval=tick_interval,
timerange=timerange)
if pairs_not_available:
print(f"Pairs [{','.join(pairs_not_available)}] not availble.")

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scripts/plot_dataframe.py
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#!/usr/bin/env python3
"""
Script to display when the bot will buy a specific pair
Mandatory Cli parameters:
-p / --pair: pair to examine
Option but recommended
-s / --strategy: strategy to use
Optional Cli parameters
-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
Indicators recommended
Row 1: sma, ema3, ema5, ema10, ema50
Row 3: macd, rsi, fisher_rsi, mfi, slowd, slowk, fastd, fastk
Example of usage:
> python3 scripts/plot_dataframe.py --pair BTC/EUR -d user_data/data/ --indicators1 sma,ema3
--indicators2 fastk,fastd
"""
import logging
import sys
import json
from pathlib import Path
from argparse import Namespace
from typing import Dict, List, Any
import pandas as pd
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import plot
import freqtrade.optimize as optimize
from freqtrade import persistence
from freqtrade.analyze import Analyze
from freqtrade.arguments import Arguments, TimeRange
from freqtrade.exchange import Exchange
from freqtrade.optimize.backtesting import setup_configuration
from freqtrade.persistence import Trade
logger = logging.getLogger(__name__)
_CONF: Dict[str, Any] = {}
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", "opents", "closets", "open_rate", "close_rate", "duration"]
trades = pd.DataFrame([(t.pair, t.calc_profit(),
t.open_date, t.close_date,
t.open_rate, t.close_rate,
t.close_date.timestamp() - t.open_date.timestamp())
for t in Trade.query.filter(Trade.pair.is_(pair)).all()],
columns=columns)
if args.exportfilename:
file = Path(args.exportfilename)
# must align with columns in backtest.py
columns = ["pair", "profit", "opents", "closets", "index", "duration",
"open_rate", "close_rate", "open_at_end"]
with file.open() as f:
data = json.load(f)
trades = pd.DataFrame(data, columns=columns)
trades = trades.loc[trades["pair"] == pair]
if timerange:
if timerange.starttype == 'date':
trades = trades.loc[trades["opents"] >= timerange.startts]
if timerange.stoptype == 'date':
trades = trades.loc[trades["opents"] <= timerange.stopts]
trades['opents'] = pd.to_datetime(trades['opents'],
unit='s',
utc=True,
infer_datetime_format=True)
trades['closets'] = pd.to_datetime(trades['closets'],
unit='s',
utc=True,
infer_datetime_format=True)
return trades
def plot_analyzed_dataframe(args: Namespace) -> None:
"""
Calls analyze() and plots the returned dataframe
:return: None
"""
global _CONF
# Load the configuration
_CONF.update(setup_configuration(args))
# Set the pair to audit
pair = args.pair
if pair is None:
logger.critical('Parameter --pair mandatory;. E.g --pair ETH/BTC')
exit()
if '/' not in pair:
logger.critical('--pair format must be XXX/YYY')
exit()
# Set timerange to use
timerange = Arguments.parse_timerange(args.timerange)
# Load the strategy
try:
analyze = Analyze(_CONF)
exchange = Exchange(_CONF)
except AttributeError:
logger.critical(
'Impossible to load the strategy. Please check the file "user_data/strategies/%s.py"',
args.strategy
)
exit()
# Set the ticker to use
tick_interval = analyze.get_ticker_interval()
# Load pair tickers
tickers = {}
if args.live:
logger.info('Downloading pair.')
tickers[pair] = exchange.get_ticker_history(pair, tick_interval)
else:
tickers = optimize.load_data(
datadir=_CONF.get("datadir"),
pairs=[pair],
ticker_interval=tick_interval,
refresh_pairs=_CONF.get('refresh_pairs', False),
timerange=timerange
)
# No ticker found, or impossible to download
if tickers == {}:
exit()
if args.db_url and args.exportfilename:
logger.critical("Can only specify --db-url or --export-filename")
# Get trades already made from the DB
trades = load_trades(args, pair, 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) > args.plot_limit:
logger.warning('Ticker contained more than %s candles as defined '
'with --plot-limit, clipping.', args.plot_limit)
dataframe = dataframe.tail(args.plot_limit)
trades = trades.loc[trades['opents'] >= dataframe.iloc[0]['date']]
fig = generate_graph(
pair=pair,
trades=trades,
data=dataframe,
args=args
)
plot(fig, filename=str(Path('user_data').joinpath('freqtrade-plot.html')))
def generate_graph(pair, trades: pd.DataFrame, data: pd.DataFrame, args) -> 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
:param args: sys.argv that contrains the two params indicators1, and indicators2
: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')
# 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["opents"],
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["closets"],
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': "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"},
)
fig.append_trace(bb_lower, 1, 1)
fig.append_trace(bb_upper, 1, 1)
fig = generate_row(fig=fig, row=1, raw_indicators=args.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=args.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,
default='macd',
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()
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:])

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scripts/plot_profit.py
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#!/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
-d / --datadir: path to pair backtest data
--timerange: specify what timerange of data to use
--export-filename: Specify where the backtest export is located.
"""
import logging
import os
import sys
import json
from argparse import Namespace
from typing import List, Optional
import numpy as np
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 import constants
import freqtrade.optimize as optimize
import freqtrade.misc as misc
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: int,
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.
Note, the profit calculation isn't realistic.
But should be somewhat proportional, and therefor useful
in helping out to find a good algorithm.
"""
# 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)
config = Configuration(args).get_config()
# Init strategy
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(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)
# 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:
pairs = list(set(pairs) & set(filter_pairs))
logger.info('Filter, keep pairs %s' % pairs)
tickers = optimize.load_data(
datadir=config.get('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)
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(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, tick_interval, filter_pairs)
#
# Plot the pairs average close prices, and total profit growth
#
avgclose = go.Scattergl(
x=dates,
y=avgclose,
name='Avg close price',
)
profit = go.Scattergl(
x=dates,
y=pg,
name='Profit',
)
fig = tools.make_subplots(rows=3, cols=1, shared_xaxes=True, row_width=[1, 1, 1])
fig.append_trace(avgclose, 1, 1)
fig.append_trace(profit, 2, 1)
for pair in pairs:
pg = make_profit_array(data, num_iterations, min_date, tick_interval, pair)
pair_profit = go.Scattergl(
x=dates,
y=pg,
name=pair,
)
fig.append_trace(pair_profit, 3, 1)
plot(fig, filename=os.path.join('user_data', 'freqtrade-profit-plot.html'))
def define_index(min_date: int, max_date: int, interval: str) -> int:
"""
Return the index of a specific date
"""
interval_minutes = constants.TICKER_INTERVAL_MINUTES[interval]
return int((max_date - min_date) / (interval_minutes * 60))
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 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: List[str]) -> 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__':
main(sys.argv[1:])