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Merge branch 'develop' into test_backtest

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This commit is contained in:
kryofly 2018-02-08 14:01:01 +01:00
commit f5de212b84
14 changed files with 85 additions and 93 deletions
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4
config.json.example
View File

@ -34,8 +34,8 @@
},
"telegram": {
"enabled": true,
"token": "your_instagram_token",
"chat_id": "your_instagram_chat_id"
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id"
},
"initial_state": "running",
"internals": {

4
config_full.json.example
View File

@ -42,8 +42,8 @@
},
"telegram": {
"enabled": true,
"token": "your_instagram_token",
"chat_id": "your_instagram_chat_id"
"token": "your_telegram_token",
"chat_id": "your_telegram_chat_id"
},
"initial_state": "running",
"internals": {

11
freqtrade/main.py
View File

@ -301,12 +301,13 @@ def min_roi_reached(trade: Trade, current_rate: float, current_time: datetime) -
return True
# Check if time matches and current rate is above threshold
time_diff = (current_time - trade.open_date).total_seconds() / 60
for duration, threshold in sorted(strategy.minimal_roi.items()):
if time_diff > float(duration) and current_profit > threshold:
time_diff = (current_time.timestamp() - trade.open_date.timestamp()) / 60
for duration_string, threshold in strategy.minimal_roi.items():
duration = float(duration_string)
if time_diff > duration and current_profit > threshold:
return True
logger.debug('Threshold not reached. (cur_profit: %1.2f%%)', float(current_profit) * 100.0)
if time_diff < duration:
return False
return False

10
freqtrade/misc.py
View File

@ -145,15 +145,15 @@ def common_args_parser(description: str):
)
parser.add_argument(
'-c', '--config',
help='specify configuration file (default: config.json)',
help='specify configuration file (default: %(default)s)',
dest='config',
default='config.json',
type=str,
metavar='PATH',
)
parser.add_argument(
'--datadir',
help='path to backtest data (default freqdata/tests/testdata)',
'-d', '--datadir',
help='path to backtest data (default: %(default)s',
dest='datadir',
default=os.path.join('freqtrade', 'tests', 'testdata'),
type=str,
@ -161,7 +161,7 @@ def common_args_parser(description: str):
)
parser.add_argument(
'-s', '--strategy',
help='specify strategy file (default: freqtrade/strategy/default_strategy.py)',
help='specify strategy file (default: %(default)s)',
dest='strategy',
default='default_strategy',
type=str,
@ -254,7 +254,7 @@ def backtesting_options(parser: argparse.ArgumentParser) -> None:
def hyperopt_options(parser: argparse.ArgumentParser) -> None:
parser.add_argument(
'-e', '--epochs',
help='specify number of epochs (default: 100)',
help='specify number of epochs (default: %(default)d)',
dest='epochs',
default=100,
type=int,

38
freqtrade/optimize/backtesting.py
View File

@ -33,7 +33,7 @@ def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]
def generate_text_table(
data: Dict[str, Dict], results: DataFrame, stake_currency, ticker_interval) -> str:
data: Dict[str, Dict], results: DataFrame, stake_currency) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:return: pretty printed table with tabulate as str
@ -49,7 +49,7 @@ def generate_text_table(
len(result.index),
result.profit_percent.mean() * 100.0,
result.profit_BTC.sum(),
result.duration.mean() * ticker_interval,
result.duration.mean(),
len(result[result.profit_BTC > 0]),
len(result[result.profit_BTC < 0])
])
@ -60,7 +60,7 @@ def generate_text_table(
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_BTC.sum(),
results.duration.mean() * ticker_interval,
results.duration.mean(),
len(results[results.profit_BTC > 0]),
len(results[results.profit_BTC < 0])
])
@ -71,28 +71,26 @@ def get_sell_trade_entry(pair, row, buy_subset, ticker, trade_count_lock, args):
stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
trade = Trade(open_rate=row.close,
open_date=row.date,
open_date=row.Index,
stake_amount=stake_amount,
amount=stake_amount / row.open,
fee=exchange.get_fee()
)
# calculate win/lose forwards from buy point
sell_subset = ticker[ticker.date > row.date][['close', 'date', 'sell']]
sell_subset = ticker[ticker.index > row.Index][['close', 'sell', 'buy']]
for row2 in sell_subset.itertuples(index=True):
if max_open_trades > 0:
# Increase trade_count_lock for every iteration
trade_count_lock[row2.date] = trade_count_lock.get(row2.date, 0) + 1
trade_count_lock[row2.Index] = trade_count_lock.get(row2.Index, 0) + 1
# Buy is on is in the buy_subset there is a row that matches the date
# of the sell event
buy_signal = not buy_subset[buy_subset.date == row2.date].empty
if(should_sell(trade, row2.close, row2.date, buy_signal, row2.sell)):
buy_signal = row2.buy
if(should_sell(trade, row2.close, row2.Index, buy_signal, row2.sell)):
return row2, (pair,
trade.calc_profit_percent(rate=row2.close),
trade.calc_profit(rate=row2.close),
row2.Index - row.Index
), row2.date
(row2.Index - row.Index).seconds // 60
), row2.Index
return None
@ -120,22 +118,24 @@ def backtest(args) -> DataFrame:
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0
ticker = populate_sell_trend(populate_buy_trend(pair_data))
if 'date' in ticker:
ticker.set_index('date', inplace=True)
# for each buy point
lock_pair_until = None
headers = ['buy', 'open', 'close', 'date', 'sell']
headers = ['buy', 'open', 'close', 'sell']
buy_subset = ticker[(ticker.buy == 1) & (ticker.sell == 0)][headers]
for row in buy_subset.itertuples(index=True):
if realistic:
if lock_pair_until is not None and row.date <= lock_pair_until:
if lock_pair_until is not None and row.Index <= lock_pair_until:
continue
if max_open_trades > 0:
# Check if max_open_trades has already been reached for the given date
if not trade_count_lock.get(row.date, 0) < max_open_trades:
if not trade_count_lock.get(row.Index, 0) < max_open_trades:
continue
if max_open_trades > 0:
# Increase lock
trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
trade_count_lock[row.Index] = trade_count_lock.get(row.Index, 0) + 1
ret = get_sell_trade_entry(pair, row, buy_subset, ticker,
trade_count_lock, args)
@ -148,8 +148,8 @@ def backtest(args) -> DataFrame:
# record a tuple of pair, current_profit_percent,
# entry-date, duration
records.append((pair, trade_entry[1],
row.date.strftime('%s'),
row2.date.strftime('%s'),
row.Index.strftime('%s'),
row2.Index.strftime('%s'),
row.Index, trade_entry[3]))
# For now export inside backtest(), maybe change so that backtest()
# returns a tuple like: (dataframe, records, logs, etc)
@ -232,5 +232,5 @@ def start(args):
})
logger.info(
'\n==================================== BACKTESTING REPORT ====================================\n%s', # noqa
generate_text_table(data, results, config['stake_currency'], strategy.ticker_interval)
generate_text_table(data, results, config['stake_currency'])
)

2
freqtrade/optimize/hyperopt.py
View File

@ -406,7 +406,7 @@ def optimizer(params):
total_profit = results.profit_percent.sum()
trade_count = len(results.index)
trade_duration = results.duration.mean() * 5
trade_duration = results.duration.mean()
if trade_count == 0 or trade_duration > MAX_ACCEPTED_TRADE_DURATION:
print('.', end='')

8
freqtrade/persistence.py
View File

@ -187,8 +187,8 @@ class Trade(_DECL_BASE):
"""
open_trade_price = self.calc_open_trade_price()
close_trade_price = self.calc_close_trade_price(
rate=Decimal(rate or self.close_rate),
fee=Decimal(fee or self.fee)
rate=(rate or self.close_rate),
fee=(fee or self.fee)
)
return float("{0:.8f}".format(close_trade_price - open_trade_price))
@ -206,8 +206,8 @@ class Trade(_DECL_BASE):
open_trade_price = self.calc_open_trade_price()
close_trade_price = self.calc_close_trade_price(
rate=Decimal(rate or self.close_rate),
fee=Decimal(fee or self.fee)
rate=(rate or self.close_rate),
fee=(fee or self.fee)
)
return float("{0:.8f}".format((close_trade_price / open_trade_price) - 1))

5
freqtrade/strategy/strategy.py
View File

@ -7,6 +7,7 @@ import os
import sys
import logging
import importlib
from collections import OrderedDict
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
@ -69,7 +70,9 @@ class Strategy(object):
)
# Minimal ROI designed for the strategy
self.minimal_roi = self.custom_strategy.minimal_roi
self.minimal_roi = OrderedDict(sorted(
self.custom_strategy.minimal_roi.items(),
key=lambda tuple: float(tuple[0]))) # sort after converting to number
# Optimal stoploss designed for the strategy
self.stoploss = self.custom_strategy.stoploss

12
freqtrade/tests/optimize/test_backtesting.py
View File

@ -94,12 +94,12 @@ def test_generate_text_table():
'loss': [0, 0]
}
)
print(generate_text_table({'BTC_ETH': {}}, results, 'BTC', 5))
assert generate_text_table({'BTC_ETH': {}}, results, 'BTC', 5) == (
print(generate_text_table({'BTC_ETH': {}}, results, 'BTC'))
assert generate_text_table({'BTC_ETH': {}}, results, 'BTC') == (
'pair buy count avg profit % total profit BTC avg duration profit loss\n' # noqa
'------- ----------- -------------- ------------------ -------------- -------- ------\n' # noqa
'BTC_ETH 2 15.00 0.60000000 100.0 2 0\n' # noqa
'TOTAL 2 15.00 0.60000000 100.0 2 0') # noqa
'BTC_ETH 2 15.00 0.60000000 20.0 2 0\n' # noqa
'TOTAL 2 15.00 0.60000000 20.0 2 0') # noqa
def test_get_timeframe(default_strategy):
@ -262,13 +262,15 @@ def test_backtest_record(default_conf, mocker, default_strategy):
assert len(records) == 3
# ('BTC_UNITEST', 0.00331158, '1510684320', '1510691700', 0, 117)
# Below follows just a typecheck of the schema/type of trade-records
oix = -1
oix = None
for (pair, profit, date_buy, date_sell, buy_index, dur) in records:
assert pair == 'BTC_UNITEST'
isinstance(profit, float)
# FIX: buy/sell should be converted to ints
isinstance(date_buy, str)
isinstance(date_sell, str)
isinstance(buy_index, pd._libs.tslib.Timestamp)
if oix:
assert buy_index > oix
oix = buy_index
assert dur > 0

8
freqtrade/tests/test_persistence.py
View File

@ -271,10 +271,6 @@ def test_calc_profit(limit_buy_order, limit_sell_order):
# Lower than open rate
assert trade.calc_profit(rate=0.00000123, fee=0.003) == -0.00089092
# Only custom fee without sell order applied
with pytest.raises(TypeError):
trade.calc_profit(fee=0.003)
# Test when we apply a Sell order. Sell higher than open rate @ 0.00001173
trade.update(limit_sell_order)
assert trade.calc_profit() == 0.00006217
@ -299,10 +295,6 @@ def test_calc_profit_percent(limit_buy_order, limit_sell_order):
# Get percent of profit with a custom rate (Lower than open rate)
assert trade.calc_profit_percent(rate=0.00000123) == -0.88863827
# Only custom fee without sell order applied
with pytest.raises(TypeError):
trade.calc_profit_percent(fee=0.003)
# Test when we apply a Sell order. Sell higher than open rate @ 0.00001173
trade.update(limit_sell_order)
assert trade.calc_profit_percent() == 0.06201057

2
requirements.txt
View File

@ -19,7 +19,7 @@ hyperopt==0.1
# do not upgrade networkx before this is fixed https://github.com/hyperopt/hyperopt/issues/325
networkx==1.11
tabulate==0.8.2
pymarketcap==3.3.154
pymarketcap==3.3.158
# Required for plotting data
#plotly==2.3.0

54
scripts/plot_dataframe.py
View File

@ -2,24 +2,15 @@
import sys
import logging
import argparse
import os
from pandas import DataFrame
import talib.abstract as ta
import plotly
from plotly import tools
from plotly.offline import plot
import plotly.graph_objs as go
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade import exchange, analyze
from freqtrade.misc import common_args_parser
from freqtrade.strategy.strategy import Strategy
import freqtrade.misc as misc
import freqtrade.optimize as optimize
import freqtrade.analyze as analyze
logger = logging.getLogger(__name__)
@ -61,7 +52,6 @@ def plot_analyzed_dataframe(args) -> None:
dataframe = dataframes[pair]
dataframe = analyze.populate_buy_trend(dataframe)
dataframe = analyze.populate_sell_trend(dataframe)
dates = misc.datesarray_to_datetimearray(dataframe['date'])
if (len(dataframe.index) > 750):
logger.warn('Ticker contained more than 750 candles, clipping.')
@ -80,7 +70,14 @@ def plot_analyzed_dataframe(args) -> None:
y=df_buy.close,
mode='markers',
name='buy',
marker=dict(symbol='x-dot')
marker=dict(
symbol='triangle-up-dot',
size=9,
line=dict(
width=1,
),
color='green',
)
)
df_sell = df[df['sell'] == 1]
sells = go.Scattergl(
@ -88,7 +85,14 @@ def plot_analyzed_dataframe(args) -> None:
y=df_sell.close,
mode='markers',
name='sell',
marker=dict(symbol='diamond')
marker=dict(
symbol='triangle-down-dot',
size=9,
line=dict(
width=1,
),
color='red',
)
)
bb_lower = go.Scatter(
@ -105,25 +109,17 @@ def plot_analyzed_dataframe(args) -> None:
fillcolor="rgba(0,176,246,0.2)",
line={'color': "transparent"},
)
macd = go.Scattergl(x=df['date'], y=df['macd'], name='MACD')
macdsignal = go.Scattergl(x=df['date'], y=df['macdsignal'], name='MACD signal')
volume = go.Bar(x=df['date'], y=df['volume'], name='Volume')
macd = go.Scattergl(
x=df['date'],
y=df['macd'],
name='MACD'
fig = tools.make_subplots(
rows=3,
cols=1,
shared_xaxes=True,
row_width=[1, 1, 4],
vertical_spacing=0.0001,
)
macdsignal = go.Scattergl(
x=df['date'],
y=df['macdsignal'],
name='MACD signal'
)
volume = go.Bar(
x=df['date'],
y=df['volume'],
name='Volume'
)
fig = tools.make_subplots(rows=3, cols=1, shared_xaxes=True, row_width=[1, 1, 4])
fig.append_trace(candles, 1, 1)
fig.append_trace(bb_lower, 1, 1)

2
scripts/plot_profit.py
View File

@ -4,14 +4,12 @@ import sys
import json
import numpy as np
import plotly
from plotly import tools
from plotly.offline import plot
import plotly.graph_objs as go
import freqtrade.optimize as optimize
import freqtrade.misc as misc
import freqtrade.exchange as exchange
from freqtrade.strategy.strategy import Strategy

4
setup.sh
View File

@ -116,8 +116,8 @@ function config_generator () {
-e "s/\"fiat_display_currency\": \"USD\",/\"fiat_display_currency\": \"$fiat_currency\",/g" \
-e "s/\"your_exchange_key\"/\"$api_key\"/g" \
-e "s/\"your_exchange_secret\"/\"$api_secret\"/g" \
-e "s/\"your_instagram_token\"/\"$token\"/g" \
-e "s/\"your_instagram_chat_id\"/\"$chat_id\"/g"
-e "s/\"your_telegram_token\"/\"$token\"/g" \
-e "s/\"your_telegram_chat_id\"/\"$chat_id\"/g"
-e "s/\"dry_run\": false,/\"dry_run\": true,/g" config.json.example > config.json
}