def get_signal(pair: str, signal: SignalType) -> bool:
"""
Calculates current signal based several technical analysis indicators
:param pair: pair in format BTC_ANT or BTC-ANT
:return: True if pair is good for buying, False otherwise
"""
ticker_hist = get_ticker_history(pair)
if not ticker_hist:
logger.warning('Empty ticker history for pair %s', pair)
return False
try:
dataframe = analyze_ticker(ticker_hist)
except ValueError as ex:
logger.warning('Unable to analyze ticker for pair %s: %s', pair, str(ex))
return False
except Exception as ex:
logger.exception('Unexpected error when analyzing ticker for pair %s: %s', pair, str(ex))
return False
if dataframe.empty:
return False
latest = dataframe.iloc[-1]
# Check if dataframe is out of date
signal_date = arrow.get(latest['date'])
if signal_date < arrow.now() - timedelta(minutes=10):
return False
result = latest[signal.value] == 1
logger.debug('%s_trigger: %s (pair=%s, signal=%s)', signal.value, latest['date'], pair, result)
return result
def test_get_ticker_history(default_conf, mocker):
api_mock = MagicMock()
tick = 123
api_mock.get_ticker_history = MagicMock(return_value=tick)
mocker.patch('freqtrade.exchange._API', api_mock)
# retrieve original ticker
ticks = get_ticker_history('BTC_ETH', int(default_conf['ticker_interval']))
assert ticks == 123
# change the ticker
tick = 999
api_mock.get_ticker_history = MagicMock(return_value=tick)
mocker.patch('freqtrade.exchange._API', api_mock)
# ensure caching will still return the original ticker
ticks = get_ticker_history('BTC_ETH', int(default_conf['ticker_interval']))
assert ticks == 123
def start(args):
# Initialize logger
logging.basicConfig(
level=args.loglevel,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
)
exchange._API = Bittrex({'key': '', 'secret': ''})
logger.info('Using config: %s ...', args.config)
config = misc.load_config(args.config)
logger.info('Using ticker_interval: %s ...', args.ticker_interval)
data = {}
pairs = config['exchange']['pair_whitelist']
if args.live:
logger.info('Downloading data for all pairs in whitelist ...')
for pair in pairs:
data[pair] = exchange.get_ticker_history(pair, args.ticker_interval)
else:
logger.info('Using local backtesting data (using whitelist in given config) ...')
data = optimize.load_data(args.datadir, pairs=pairs, ticker_interval=args.ticker_interval,
refresh_pairs=args.refresh_pairs)
logger.info('Using stake_currency: %s ...', config['stake_currency'])
logger.info('Using stake_amount: %s ...', config['stake_amount'])
max_open_trades = 0
if args.realistic_simulation:
logger.info('Using max_open_trades: %s ...', config['max_open_trades'])
max_open_trades = config['max_open_trades']
# Monkey patch config
from freqtrade import main
main._CONF = config
preprocessed = preprocess(data)
# Print timeframe
min_date, max_date = get_timeframe(preprocessed)
logger.info('Measuring data from %s up to %s ...', min_date.isoformat(), max_date.isoformat())
# Execute backtest and print results
results = backtest(
stake_amount=config['stake_amount'],
processed=preprocessed,
max_open_trades=max_open_trades,
realistic=args.realistic_simulation,
sell_profit_only=config.get('experimental', {}).get('sell_profit_only', False),
stoploss=config.get('stoploss'),
use_sell_signal=config.get('experimental', {}).get('use_sell_signal', False)
)
logger.info(
'\n==================================== BACKTESTING REPORT ====================================\n%s', # noqa
generate_text_table(data, results, config['stake_currency'], args.ticker_interval)
)
def get_signal(self, pair: str, interval: int) -> Tuple[bool, bool]:
"""
Calculates current signal based several technical analysis indicators
:param pair: pair in format BTC_ANT or BTC-ANT
:param interval: Interval to use (in min)
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
"""
ticker_hist = get_ticker_history(pair, interval)
if not ticker_hist:
logger.warning('Empty ticker history for pair %s', pair)
return False, False
try:
dataframe = self.analyze_ticker(ticker_hist)
except ValueError as error:
logger.warning(
'Unable to analyze ticker for pair %s: %s',
pair,
str(error)
)
return False, False
except Exception as error:
logger.exception(
'Unexpected error when analyzing ticker for pair %s: %s',
pair,
str(error)
)
return False, False
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
return False, False
latest = dataframe.iloc[-1]
# Check if dataframe is out of date
signal_date = arrow.get(latest['date'])
if signal_date < arrow.utcnow() - timedelta(minutes=(interval + 5)):
logger.warning(
'Outdated history for pair %s. Last tick is %s minutes old',
pair,
(arrow.utcnow() - signal_date).seconds // 60
)
return False, False
(buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
logger.debug(
'trigger: %s (pair=%s) buy=%s sell=%s',
latest['date'],
pair,
str(buy),
str(sell)
)
return buy, sell
def analyze_ticker(pair: str) -> DataFrame:
"""
Get ticker data for given currency pair, push it to a DataFrame and
add several TA indicators and buy signal to it
:return DataFrame with ticker data and indicator data
"""
ticker_hist = get_ticker_history(pair)
if not ticker_hist:
logger.warning('Empty ticker history for pair %s', pair)
return DataFrame()
dataframe = parse_ticker_dataframe(ticker_hist)
dataframe = populate_indicators(dataframe)
dataframe = populate_buy_trend(dataframe)
return dataframe
def download_backtesting_testdata(datadir: str,
pair: str,
interval: int = 5) -> bool:
"""
Download the latest 1 and 5 ticker intervals from Bittrex for the pairs passed in parameters
Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
:param pairs: list of pairs to download
:return: bool
"""
path = make_testdata_path(datadir)
logger.info('Download the pair: "{pair}", Interval: {interval} min'.format(
pair=pair,
interval=interval,
))
filepair = pair.replace("-", "_")
filename = os.path.join(
path, '{pair}-{interval}.json'.format(
pair=filepair,
interval=interval,
))
filename = filename.replace('USDT_BTC', 'BTC_FAKEBULL')
if os.path.isfile(filename):
with open(filename, "rt") as fp:
data = json.load(fp)
logger.debug("Current Start: {}".format(data[1]['T']))
logger.debug("Current End: {}".format(data[-1:][0]['T']))
else:
data = []
logger.debug("Current Start: None")
logger.debug("Current End: None")
new_data = get_ticker_history(pair=pair, tick_interval=int(interval))
for row in new_data:
if row not in data:
data.append(row)
logger.debug("New Start: {}".format(data[1]['T']))
logger.debug("New End: {}".format(data[-1:][0]['T']))
data = sorted(data, key=lambda data: data['T'])
with open(filename, "wt") as fp:
json.dump(data, fp)
return True
def analyze_ticker(pair: str, tick_interval: int, exchange_name: str,
analyzer: object) -> DataFrame:
"""
Get ticker data for given currency pair, push it to a DataFrame and
add several TA indicators and buy signal to it
:return DataFrame with ticker data and indicator data
"""
data = get_ticker_history(pair, tick_interval)
dataframe = parse_ticker_dataframe(data, exchange_name)
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
return dataframe
dataframe = populate_indicators(dataframe)
dataframe = populate_buy_trend(dataframe, analyzer)
return dataframe
def analyze_ticker(pair: str) -> DataFrame:
"""
Get ticker data for given currency pair, push it to a DataFrame and
add several TA indicators and buy signal to it
:return DataFrame with ticker data and indicator data
"""
minimum_date = arrow.utcnow().shift(hours=-24)
data = get_ticker_history(pair, minimum_date)
dataframe = parse_ticker_dataframe(data['result'], minimum_date)
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
return dataframe
dataframe = populate_indicators(dataframe)
dataframe = populate_buy_trend(dataframe)
return dataframe
def get_ema_signal(pair: str, tick_interval: int, exchange_name: str) -> bool:
"""
Get ticker data for given currency pair, push it to a DataFrame and
add several TA indicators and buy signal to it
:return DataFrame with ticker data and indicator data
"""
data = get_ticker_history(pair, tick_interval)
dataframe = parse_ticker_dataframe(data, exchange_name)
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
return False
dataframe = populate_indicators(dataframe)
ema5_diff = dataframe['ema5'][len(dataframe) -
1] - dataframe['ema5'][len(dataframe) - 2]
print(ema5_diff > 0.)
return ema5_diff > 0.
def analyze_ticker(pair: str) -> DataFrame:
"""
Get ticker data for given currency pair, push it to a DataFrame and
add several TA indicators and buy signal to it
:return DataFrame with ticker data and indicator data
"""
ticker_hist = get_ticker_history(pair)
if not ticker_hist:
logger.warning('Empty ticker history for pair %s', pair)
return DataFrame()
dataframe = parse_ticker_dataframe(ticker_hist)
dataframe = populate_indicators(dataframe)
dataframe = populate_buy_trend(dataframe)
dataframe = populate_sell_trend(dataframe)
# TODO: buy_price and sell_price are only used by the plotter, should probably be moved there
dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
dataframe.loc[dataframe['sell'] == 1, 'sell_price'] = dataframe['close']
return dataframe
def download_backtesting_testdata(datadir: str, pair: str, interval: int = 5) -> bool:
"""
Download the latest 1 and 5 ticker intervals from Bittrex for the pairs passed in parameters
Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
:param pairs: list of pairs to download
:return: bool
"""
path = make_testdata_path(datadir)
logger.info('Download the pair: "{pair}", Interval: {interval} min'.format(
pair=pair,
interval=interval,
))
filepair = pair.replace("-", "_")
filename = os.path.join(path, '{pair}-{interval}.json'.format(
pair=filepair,
interval=interval,
))
filename = filename.replace('USDT_BTC', 'BTC_FAKEBULL')
if os.path.isfile(filename):
with open(filename, "rt") as fp:
data = json.load(fp)
logger.debug("Current Start: {}".format(data[1]['T']))
logger.debug("Current End: {}".format(data[-1:][0]['T']))
else:
data = []
logger.debug("Current Start: None")
logger.debug("Current End: None")
new_data = get_ticker_history(pair=pair, tick_interval=int(interval))
for row in new_data:
if row not in data:
data.append(row)
logger.debug("New Start: {}".format(data[1]['T']))
logger.debug("New End: {}".format(data[-1:][0]['T']))
data = sorted(data, key=lambda data: data['T'])
with open(filename, "wt") as fp:
json.dump(data, fp)
return True
def test_backtest(backtest_conf, mocker):
print('')
exchange._API = Bittrex({'key': '', 'secret': ''})
# Load configuration file based on env variable
conf_path = os.environ.get('BACKTEST_CONFIG')
if conf_path:
print('Using config: {} ...'.format(conf_path))
config = load_config(conf_path)
else:
config = backtest_conf
# Parse ticker interval
ticker_interval = int(os.environ.get('BACKTEST_TICKER_INTERVAL') or 5)
print('Using ticker_interval: {} ...'.format(ticker_interval))
data = {}
if os.environ.get('BACKTEST_LIVE'):
print('Downloading data for all pairs in whitelist ...')
for pair in config['exchange']['pair_whitelist']:
data[pair] = exchange.get_ticker_history(pair, ticker_interval)
else:
print(
'Using local backtesting data (ignoring whitelist in given config)...'
)
data = load_backtesting_data(ticker_interval)
print('Using stake_currency: {} ...\nUsing stake_amount: {} ...'.format(
config['stake_currency'], config['stake_amount']))
# Print timeframe
min_date, max_date = get_timeframe(data)
print('Measuring data from {} up to {} ...'.format(min_date.isoformat(),
max_date.isoformat()))
# Execute backtest and print results
results = backtest(config, preprocess(data), mocker)
print(
'====================== BACKTESTING REPORT ======================================\n\n'
'NOTE: This Report doesn\'t respect the limits of max_open_trades, \n'
' so the projected values should be taken with a grain of salt.\n'
)
print(generate_text_table(data, results, config['stake_currency']))
def plot_analyzed_dataframe(args) -> None:
"""
Calls analyze() and plots the returned dataframe
:param pair: pair as str
:return: None
"""
pair = args.pair
# Init Bittrex to use public API
exchange._API = exchange.Bittrex({'key': '', 'secret': ''})
ticker = exchange.get_ticker_history(pair)
dataframe = analyze.analyze_ticker(ticker)
dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
dataframe.loc[dataframe['sell'] == 1, 'sell_price'] = dataframe['close']
# Two subplots sharing x axis
fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
fig.suptitle(pair, fontsize=14, fontweight='bold')
ax1.plot(dataframe.index.values, dataframe['close'], label='close')
# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
ax1.plot(dataframe.index.values, dataframe['sma'], '--', label='SMA')
ax1.plot(dataframe.index.values, dataframe['tema'], ':', label='TEMA')
ax1.plot(dataframe.index.values, dataframe['blower'], '-.', label='BB low')
ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
ax1.legend()
ax2.plot(dataframe.index.values, dataframe['adx'], label='ADX')
ax2.plot(dataframe.index.values, dataframe['mfi'], label='MFI')
# ax2.plot(dataframe.index.values, [25] * len(dataframe.index.values))
ax2.legend()
ax3.plot(dataframe.index.values, dataframe['fastk'], label='k')
ax3.plot(dataframe.index.values, dataframe['fastd'], label='d')
ax3.plot(dataframe.index.values, [20] * len(dataframe.index.values))
ax3.legend()
# Fine-tune figure; make subplots close to each other and hide x ticks for
# all but bottom plot.
fig.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
plt.show()
def plot_analyzed_dataframe(args) -> None:
"""
Calls analyze() and plots the returned dataframe
:param pair: pair as str
:return: None
"""
pair = args.pair
# Init Bittrex to use public API
exchange._API = exchange.Bittrex({'key': '', 'secret': ''})
ticker = exchange.get_ticker_history(pair)
dataframe = analyze.analyze_ticker(ticker)
dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
dataframe.loc[dataframe['sell'] == 1, 'sell_price'] = dataframe['close']
# Two subplots sharing x axis
fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
fig.suptitle(pair, fontsize=14, fontweight='bold')
ax1.plot(dataframe.index.values, dataframe['close'], label='close')
# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
ax1.plot(dataframe.index.values, dataframe['sma'], '--', label='SMA')
ax1.plot(dataframe.index.values, dataframe['tema'], ':', label='TEMA')
ax1.plot(dataframe.index.values, dataframe['blower'], '-.', label='BB low')
ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
ax1.legend()
ax2.plot(dataframe.index.values, dataframe['adx'], label='ADX')
ax2.plot(dataframe.index.values, dataframe['mfi'], label='MFI')
# ax2.plot(dataframe.index.values, [25] * len(dataframe.index.values))
ax2.legend()
ax3.plot(dataframe.index.values, dataframe['fastk'], label='k')
ax3.plot(dataframe.index.values, dataframe['fastd'], label='d')
ax3.plot(dataframe.index.values, [20] * len(dataframe.index.values))
ax3.legend()
# Fine-tune figure; make subplots close to each other and hide x ticks for
# all but bottom plot.
fig.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
plt.show()
def download_backtesting_testdata(datadir: str,
pair: str,
interval: int = 5) -> None:
"""
Download the latest 1 and 5 ticker intervals from Bittrex for the pairs passed in parameters
Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
"""
path = make_testdata_path(datadir)
logger.info('Download the pair: "%s", Interval: %s min', pair, interval)
filename = os.path.join(
path, '{pair}-{interval}.json'.format(
pair=pair.replace("-", "_"),
interval=interval,
))
if os.path.isfile(filename):
with open(filename, "rt") as file:
data = json.load(file)
else:
data = []
logger.debug('Current Start: %s', data[1]['T'] if data else None)
logger.debug('Current End: %s', data[-1:][0]['T'] if data else None)
# Extend data with new ticker history
data.extend([
row
for row in get_ticker_history(pair=pair, tick_interval=int(interval))
if row not in data
])
data = sorted(data, key=lambda _data: _data['T'])
logger.debug('New Start: %s', data[1]['T'])
logger.debug('New End: %s', data[-1:][0]['T'])
misc.file_dump_json(filename, data)
# ensure directory exists
base_path = os.path.join(os.path.expanduser('~'), 'freqtrade')
# get configuration
with open(os.path.join(base_path, 'config.json')) as file:
_CONF = json.load(file)
# initialize the exchange
exchange.init(_CONF)
# get ticker
data = exchange.get_ticker(pair='ETH_BTC')
print(data)
# get ticker history
df = exchange.get_ticker_history(pair='ETH_BTC', tick_interval=1)
print(pd.DataFrame(df))
# get markets
data = exchange.get_markets()
print(data)
# get name
print(exchange.get_name())
print(exchange.get_sleep_time())
print(exchange.get_fee())
def start(self) -> None:
"""
Run a backtesting end-to-end
:return: None
"""
data = {}
pairs = self.config['exchange']['pair_whitelist']
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
if self.config.get('live'):
logger.info('Downloading data for all pairs in whitelist ...')
for pair in pairs:
data[pair] = exchange.get_ticker_history(pair, self.ticker_interval)
else:
logger.info('Using local backtesting data (using whitelist in given config) ...')
timerange = Arguments.parse_timerange(self.config.get('timerange'))
data = optimize.load_data(
self.config['datadir'],
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=self.config.get('refresh_pairs', False),
timerange=timerange
)
# Ignore max_open_trades in backtesting, except realistic flag was passed
if self.config.get('realistic_simulation', False):
max_open_trades = self.config['max_open_trades']
else:
logger.info('Ignoring max_open_trades (realistic_simulation not set) ...')
max_open_trades = 0
preprocessed = self.tickerdata_to_dataframe(data)
print(preprocessed)
# Print timeframe
min_date, max_date = self.get_timeframe(preprocessed)
logger.info(
'Measuring data from %s up to %s (%s days)..',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
)
# Execute backtest and print results
sell_profit_only = self.config.get('experimental', {}).get('sell_profit_only', False)
use_sell_signal = self.config.get('experimental', {}).get('use_sell_signal', False)
results = self.backtest(
{
'stake_amount': self.config.get('stake_amount'),
'processed': preprocessed,
'max_open_trades': max_open_trades,
'realistic': self.config.get('realistic_simulation', False),
'sell_profit_only': sell_profit_only,
'use_sell_signal': use_sell_signal,
'record': self.config.get('export')
}
)
logger.info(
'\n==================================== '
'BACKTESTING REPORT'
' ====================================\n'
'%s',
self._generate_text_table(
data,
results
)
)
#!/usr/bin/env python3
"""This script generate json data from bittrex"""
import json
from os import path
from freqtrade import exchange
from freqtrade.exchange import Bittrex
PAIRS = [
'BTC_BCC', 'BTC_ETH', 'BTC_MER', 'BTC_POWR', 'BTC_ETC', 'BTC_OK',
'BTC_NEO', 'BTC_EMC2', 'BTC_DASH', 'BTC_LSK', 'BTC_LTC', 'BTC_XZC',
'BTC_OMG', 'BTC_STRAT', 'BTC_XRP', 'BTC_QTUM', 'BTC_WAVES', 'BTC_VTC',
'BTC_XLM', 'BTC_MCO'
]
TICKER_INTERVAL = 5 # ticker interval in minutes (currently implemented: 1 and 5)
OUTPUT_DIR = path.dirname(path.realpath(__file__))
# Init Bittrex exchange
exchange._API = Bittrex({'key': '', 'secret': ''})
for pair in PAIRS:
data = exchange.get_ticker_history(pair, TICKER_INTERVAL)
filename = path.join(OUTPUT_DIR, '{}-{}.json'.format(
pair,
TICKER_INTERVAL,
))
with open(filename, 'w') as fp:
json.dump(data, fp)
from freqtrade.exchange import Bittrex
parser = misc.common_args_parser('download utility')
parser.add_argument(
'-p', '--pair',
help='JSON file containing pairs to download',
dest='pair',
default=None
)
args = parser.parse_args(sys.argv[1:])
TICKER_INTERVALS = [1, 5] # ticker interval in minutes (currently implemented: 1 and 5)
PAIRS = []
if args.pair:
with open(args.pair) as file:
PAIRS = json.load(file)
PAIRS = list(set(PAIRS))
print('About to download pairs:', PAIRS)
# Init Bittrex exchange
exchange._API = Bittrex({'key': '', 'secret': ''})
for pair in PAIRS:
for tick_interval in TICKER_INTERVALS:
print('downloading pair %s, interval %s' % (pair, tick_interval))
data = exchange.get_ticker_history(pair, tick_interval)
filename = '{}-{}.json'.format(pair, tick_interval)
misc.file_dump_json(filename, data)
#!/usr/bin/env python3
"""This script generate json data from bittrex"""
import json
from os import path
from freqtrade import exchange
from freqtrade.exchange import Bittrex
PAIRS = [
'BTC_BCC', 'BTC_ETH', 'BTC_MER', 'BTC_POWR', 'BTC_ETC',
'BTC_OK', 'BTC_NEO', 'BTC_EMC2', 'BTC_DASH', 'BTC_LSK',
'BTC_LTC', 'BTC_XZC', 'BTC_OMG', 'BTC_STRAT', 'BTC_XRP',
'BTC_QTUM', 'BTC_WAVES', 'BTC_VTC', 'BTC_XLM', 'BTC_MCO'
]
TICKER_INTERVAL = 5 # ticker interval in minutes (currently implemented: 1 and 5)
OUTPUT_DIR = path.dirname(path.realpath(__file__))
# Init Bittrex exchange
exchange._API = Bittrex({'key': '', 'secret': ''})
for pair in PAIRS:
data = exchange.get_ticker_history(pair, TICKER_INTERVAL)
filename = path.join(OUTPUT_DIR, '{}-{}.json'.format(
pair,
TICKER_INTERVAL,
))
with open(filename, 'w') as fp:
json.dump(data, fp)
def start(args):
# Initialize logger
logging.basicConfig(
level=args.loglevel,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
)
exchange._API = Bittrex({'key': '', 'secret': ''})
logger.info('Using config: %s ...', args.config)
config = misc.load_config(args.config)
logger.info('Using ticker_interval: %s ...', args.ticker_interval)
data = {}
pairs = config['exchange']['pair_whitelist']
if args.live:
logger.info('Downloading data for all pairs in whitelist ...')
for pair in pairs:
data[pair] = exchange.get_ticker_history(pair,
args.ticker_interval)
else:
logger.info(
'Using local backtesting data (using whitelist in given config) ...'
)
data = optimize.load_data(args.datadir,
pairs=pairs,
ticker_interval=args.ticker_interval,
refresh_pairs=args.refresh_pairs)
logger.info('Using stake_currency: %s ...', config['stake_currency'])
logger.info('Using stake_amount: %s ...', config['stake_amount'])
max_open_trades = 0
if args.realistic_simulation:
logger.info('Using max_open_trades: %s ...', config['max_open_trades'])
max_open_trades = config['max_open_trades']
# Monkey patch config
from freqtrade import main
main._CONF = config
preprocessed = preprocess(data)
# Print timeframe
min_date, max_date = get_timeframe(preprocessed)
logger.info('Measuring data from %s up to %s ...', min_date.isoformat(),
max_date.isoformat())
# Execute backtest and print results
results = backtest(
stake_amount=config['stake_amount'],
processed=preprocessed,
max_open_trades=max_open_trades,
realistic=args.realistic_simulation,
sell_profit_only=config.get('experimental',
{}).get('sell_profit_only', False),
stoploss=config.get('stoploss'),
use_sell_signal=config.get('experimental',
{}).get('use_sell_signal', False))
logger.info(
'\n==================================== BACKTESTING REPORT ====================================\n%s', # noqa
generate_text_table(data, results, config['stake_currency'],
args.ticker_interval))
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:
analyze = Analyze({'strategy': args.strategy})
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,
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',
))
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"},
)
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_upper, 1, 1)
fig.append_trace(buys, 1, 1)
fig.append_trace(sells, 1, 1)
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')
