def load_dataframe_pair(pairs):
ld = load_data(None, ticker_interval=5, pairs=pairs)
assert isinstance(ld, dict)
assert isinstance(pairs[0], str)
dataframe = ld[pairs[0]]
analyze = Analyze({'strategy': 'DefaultStrategy'})
dataframe = analyze.analyze_ticker(dataframe)
return dataframe
def test_parse_ticker_dataframe(ticker_history, ticker_history_without_bv):
columns = ['date', 'close', 'high', 'low', 'open', 'volume']
# Test file with BV data
dataframe = Analyze.parse_ticker_dataframe(ticker_history)
assert dataframe.columns.tolist() == columns
# Test file without BV data
dataframe = Analyze.parse_ticker_dataframe(ticker_history_without_bv)
assert dataframe.columns.tolist() == columns
def _init(self) -> None:
"""
Init objects required for backtesting
:return: None
"""
self.analyze = Analyze(self.config)
self.ticker_interval = self.analyze.strategy.ticker_interval
self.tickerdata_to_dataframe = self.analyze.tickerdata_to_dataframe
self.populate_buy_trend = self.analyze.populate_buy_trend
self.populate_sell_trend = self.analyze.populate_sell_trend
exchange._API = Bittrex({'key': '', 'secret': ''})
def test_tickerdata_to_dataframe(default_conf) -> None:
"""
Test Analyze.tickerdata_to_dataframe() method
"""
analyze = Analyze(default_conf)
timerange = ((None, 'line'), None, -100)
tick = load_tickerdata_file(None, 'BTC_UNITEST', 1, timerange=timerange)
tickerlist = {'BTC_UNITEST': tick}
data = analyze.tickerdata_to_dataframe(tickerlist)
assert len(data['BTC_UNITEST']) == 100
def test_tickerdata_to_dataframe(default_conf) -> None:
"""
Test Analyze.tickerdata_to_dataframe() method
"""
analyze = Analyze(default_conf)
timerange = TimeRange(None, 'line', 0, -100)
tick = load_tickerdata_file(None,
'UNITTEST/BTC',
'1m',
timerange=timerange)
tickerlist = {'UNITTEST/BTC': tick}
data = analyze.tickerdata_to_dataframe(tickerlist)
assert len(data['UNITTEST/BTC']) == 99 # partial candle was removed
def test_common_datearray(default_conf, mocker) -> None:
"""
Test common_datearray()
:return: None
"""
analyze = Analyze(default_conf)
tick = load_tickerdata_file(None, 'BTC_UNITEST', 1)
tickerlist = {'BTC_UNITEST': tick}
dataframes = analyze.tickerdata_to_dataframe(tickerlist)
dates = common_datearray(dataframes)
assert dates.size == dataframes['BTC_UNITEST']['date'].size
assert dates[0] == dataframes['BTC_UNITEST']['date'][0]
assert dates[-1] == dataframes['BTC_UNITEST']['date'][-1]
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
self.analyze = Analyze(self.config)
self.ticker_interval = self.analyze.strategy.ticker_interval
self.tickerdata_to_dataframe = self.analyze.tickerdata_to_dataframe
self.populate_buy_trend = self.analyze.populate_buy_trend
self.populate_sell_trend = self.analyze.populate_sell_trend
# Reset keys for backtesting
self.config['exchange']['key'] = ''
self.config['exchange']['secret'] = ''
self.config['exchange']['password'] = ''
self.config['exchange']['uid'] = ''
self.config['dry_run'] = True
self.exchange = Exchange(self.config)
self.fee = self.exchange.get_fee()
def test_tickerdata_to_dataframe(default_conf, mocker) -> None:
"""
Test Backtesting.tickerdata_to_dataframe() method
"""
patch_exchange(mocker)
timerange = TimeRange(None, 'line', 0, -100)
tick = optimize.load_tickerdata_file(None,
'UNITTEST/BTC',
'1m',
timerange=timerange)
tickerlist = {'UNITTEST/BTC': tick}
backtesting = Backtesting(default_conf)
data = backtesting.tickerdata_to_dataframe(tickerlist)
assert len(data['UNITTEST/BTC']) == 99
# Load Analyze to compare the result between Backtesting function and Analyze are the same
analyze = Analyze(default_conf)
data2 = analyze.tickerdata_to_dataframe(tickerlist)
assert data['UNITTEST/BTC'].equals(data2['UNITTEST/BTC'])
def _init_modules(self, db_url: Optional[str] = None) -> None:
"""
Initializes all modules and updates the config
:param db_url: database connector string for sqlalchemy (Optional)
:return: None
"""
# Initialize all modules
self.analyze = Analyze(self.config)
self.fiat_converter = CryptoToFiatConverter()
self.rpc = RPCManager(self)
persistence.init(self.config, db_url)
exchange.init(self.config)
# Set initial application state
initial_state = self.config.get('initial_state')
if initial_state:
self.state = State[initial_state.upper()]
else:
self.state = State.STOPPED
def __init__(self, config: Dict[str, Any]) -> None:
"""
Init all variables and object the bot need to work
:param config: configuration dict, you can use the Configuration.get_config()
method to get the config dict.
"""
logger.info(
'Starting freqtrade %s',
__version__,
)
# Init bot states
self.state = State.STOPPED
# Init objects
self.config = config
self.analyze = Analyze(self.config)
self.fiat_converter = CryptoToFiatConverter()
self.rpc: RPCManager = RPCManager(self)
self.persistence = None
self.exchange = Exchange(self.config)
self._init_modules()
def test_datesarray_to_datetimearray(ticker_history):
"""
Test datesarray_to_datetimearray() function
:return: None
"""
dataframes = Analyze.parse_ticker_dataframe(ticker_history)
dates = datesarray_to_datetimearray(dataframes['date'])
assert isinstance(dates[0], datetime.datetime)
assert dates[0].year == 2017
assert dates[0].month == 11
assert dates[0].day == 26
assert dates[0].hour == 8
assert dates[0].minute == 50
date_len = len(dates)
assert date_len == 3
class FreqtradeBot(object):
"""
Freqtrade is the main class of the bot.
This is from here the bot start its logic.
"""
def __init__(self, config: Dict[str, Any]) -> None:
"""
Init all variables and object the bot need to work
:param config: configuration dict, you can use the Configuration.get_config()
method to get the config dict.
"""
logger.info(
'Starting freqtrade %s',
__version__,
)
# Init bot states
self.state = State.STOPPED
# Init objects
self.config = config
self.analyze = Analyze(self.config)
self.fiat_converter = CryptoToFiatConverter()
self.rpc: RPCManager = RPCManager(self)
self.persistence = None
self.exchange = Exchange(self.config)
self._init_modules()
def _init_modules(self) -> None:
"""
Initializes all modules and updates the config
:return: None
"""
# Initialize all modules
persistence.init(self.config)
# Set initial application state
initial_state = self.config.get('initial_state')
if initial_state:
self.state = State[initial_state.upper()]
else:
self.state = State.STOPPED
def cleanup(self) -> None:
"""
Cleanup pending resources on an already stopped bot
:return: None
"""
logger.info('Cleaning up modules ...')
self.rpc.cleanup()
persistence.cleanup()
def worker(self, old_state: State = None) -> State:
"""
Trading routine that must be run at each loop
:param old_state: the previous service state from the previous call
:return: current service state
"""
# Log state transition
state = self.state
if state != old_state:
self.rpc.send_msg(f'*Status:* `{state.name.lower()}`')
logger.info('Changing state to: %s', state.name)
if state == State.STOPPED:
time.sleep(1)
elif state == State.RUNNING:
min_secs = self.config.get('internals',
{}).get('process_throttle_secs',
constants.PROCESS_THROTTLE_SECS)
nb_assets = self.config.get('dynamic_whitelist', None)
self._throttle(func=self._process,
min_secs=min_secs,
nb_assets=nb_assets)
return state
def _throttle(self, func: Callable[..., Any], min_secs: float, *args,
**kwargs) -> Any:
"""
Throttles the given callable that it
takes at least `min_secs` to finish execution.
:param func: Any callable
:param min_secs: minimum execution time in seconds
:return: Any
"""
start = time.time()
result = func(*args, **kwargs)
end = time.time()
duration = max(min_secs - (end - start), 0.0)
logger.debug('Throttling %s for %.2f seconds', func.__name__, duration)
time.sleep(duration)
return result
def _process(self, nb_assets: Optional[int] = 0) -> bool:
"""
Queries the persistence layer for open trades and handles them,
otherwise a new trade is created.
:param: nb_assets: the maximum number of pairs to be traded at the same time
:return: True if one or more trades has been created or closed, False otherwise
"""
state_changed = False
try:
# Refresh whitelist based on wallet maintenance
sanitized_list = self._refresh_whitelist(
self._gen_pair_whitelist(self.config['stake_currency'])
if nb_assets else self.config['exchange']['pair_whitelist'])
# Keep only the subsets of pairs wanted (up to nb_assets)
final_list = sanitized_list[:nb_assets] if nb_assets else sanitized_list
self.config['exchange']['pair_whitelist'] = final_list
# Query trades from persistence layer
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
# First process current opened trades
for trade in trades:
state_changed |= self.process_maybe_execute_sell(trade)
# Then looking for buy opportunities
if len(trades) < self.config['max_open_trades']:
state_changed = self.process_maybe_execute_buy()
if 'unfilledtimeout' in self.config:
# Check and handle any timed out open orders
self.check_handle_timedout(self.config['unfilledtimeout'])
Trade.session.flush()
except TemporaryError as error:
logger.warning('%s, retrying in 30 seconds...', error)
time.sleep(constants.RETRY_TIMEOUT)
except OperationalException:
tb = traceback.format_exc()
hint = 'Issue `/start` if you think it is safe to restart.'
self.rpc.send_msg(
f'*Status:* OperationalException:\n```\n{tb}```{hint}')
logger.exception('OperationalException. Stopping trader ...')
self.state = State.STOPPED
return state_changed
@cached(TTLCache(maxsize=1, ttl=1800))
def _gen_pair_whitelist(self,
base_currency: str,
key: str = 'quoteVolume') -> List[str]:
"""
Updates the whitelist with with a dynamically generated list
:param base_currency: base currency as str
:param key: sort key (defaults to 'quoteVolume')
:return: List of pairs
"""
if not self.exchange.exchange_has('fetchTickers'):
raise OperationalException(
'Exchange does not support dynamic whitelist.'
'Please edit your config and restart the bot')
tickers = self.exchange.get_tickers()
# check length so that we make sure that '/' is actually in the string
tickers = [
v for k, v in tickers.items()
if len(k.split('/')) == 2 and k.split('/')[1] == base_currency
]
sorted_tickers = sorted(tickers, reverse=True, key=lambda t: t[key])
pairs = [s['symbol'] for s in sorted_tickers]
return pairs
def _refresh_whitelist(self, whitelist: List[str]) -> List[str]:
"""
Check available markets and remove pair from whitelist if necessary
:param whitelist: the sorted list (based on BaseVolume) of pairs the user might want to
trade
:return: the list of pairs the user wants to trade without the one unavailable or
black_listed
"""
sanitized_whitelist = whitelist
markets = self.exchange.get_markets()
markets = [
m for m in markets if m['quote'] == self.config['stake_currency']
]
known_pairs = set()
for market in markets:
pair = market['symbol']
# pair is not int the generated dynamic market, or in the blacklist ... ignore it
if pair not in whitelist or pair in self.config['exchange'].get(
'pair_blacklist', []):
continue
# else the pair is valid
known_pairs.add(pair)
# Market is not active
if not market['active']:
sanitized_whitelist.remove(pair)
logger.info(
'Ignoring %s from whitelist. Market is not active.', pair)
# We need to remove pairs that are unknown
final_list = [x for x in sanitized_whitelist if x in known_pairs]
return final_list
def get_target_bid(self, ticker: Dict[str, float]) -> float:
"""
Calculates bid target between current ask price and last price
:param ticker: Ticker to use for getting Ask and Last Price
:return: float: Price
"""
if ticker['ask'] < ticker['last']:
return ticker['ask']
balance = self.config['bid_strategy']['ask_last_balance']
return ticker['ask'] + balance * (ticker['last'] - ticker['ask'])
def _get_trade_stake_amount(self) -> Optional[float]:
stake_amount = self.config['stake_amount']
avaliable_amount = self.exchange.get_balance(
self.config['stake_currency'])
if stake_amount == constants.UNLIMITED_STAKE_AMOUNT:
open_trades = len(
Trade.query.filter(Trade.is_open.is_(True)).all())
if open_trades >= self.config['max_open_trades']:
logger.warning(
'Can\'t open a new trade: max number of trades is reached')
return None
return avaliable_amount / (self.config['max_open_trades'] -
open_trades)
# Check if stake_amount is fulfilled
if avaliable_amount < stake_amount:
raise DependencyException(
'Available balance(%f %s) is lower than stake amount(%f %s)' %
(avaliable_amount, self.config['stake_currency'], stake_amount,
self.config['stake_currency']))
return stake_amount
def _get_min_pair_stake_amount(self, pair: str,
price: float) -> Optional[float]:
markets = self.exchange.get_markets()
markets = [m for m in markets if m['symbol'] == pair]
if not markets:
raise ValueError(
f'Can\'t get market information for symbol {pair}')
market = markets[0]
if 'limits' not in market:
return None
min_stake_amounts = []
if 'cost' in market['limits'] and 'min' in market['limits']['cost']:
min_stake_amounts.append(market['limits']['cost']['min'])
if 'amount' in market['limits'] and 'min' in market['limits']['amount']:
min_stake_amounts.append(market['limits']['amount']['min'] * price)
if not min_stake_amounts:
return None
amount_reserve_percent = 1 - 0.05 # reserve 5% + stoploss
if self.analyze.get_stoploss() is not None:
amount_reserve_percent += self.analyze.get_stoploss()
# it should not be more than 50%
amount_reserve_percent = max(amount_reserve_percent, 0.5)
return min(min_stake_amounts) / amount_reserve_percent
def create_trade(self) -> bool:
"""
Checks the implemented trading indicator(s) for a randomly picked pair,
if one pair triggers the buy_signal a new trade record gets created
:return: True if a trade object has been created and persisted, False otherwise
"""
interval = self.analyze.get_ticker_interval()
stake_amount = self._get_trade_stake_amount()
if not stake_amount:
return False
stake_currency = self.config['stake_currency']
fiat_currency = self.config['fiat_display_currency']
exc_name = self.exchange.name
logger.info(
'Checking buy signals to create a new trade with stake_amount: %f ...',
stake_amount)
whitelist = copy.deepcopy(self.config['exchange']['pair_whitelist'])
# Remove currently opened and latest pairs from whitelist
for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
if trade.pair in whitelist:
whitelist.remove(trade.pair)
logger.debug('Ignoring %s in pair whitelist', trade.pair)
if not whitelist:
raise DependencyException('No currency pairs in whitelist')
# Pick pair based on buy signals
for _pair in whitelist:
(buy, sell) = self.analyze.get_signal(self.exchange, _pair,
interval)
if buy and not sell:
pair = _pair
break
else:
return False
pair_s = pair.replace('_', '/')
pair_url = self.exchange.get_pair_detail_url(pair)
# Calculate amount
buy_limit = self.get_target_bid(self.exchange.get_ticker(pair))
min_stake_amount = self._get_min_pair_stake_amount(pair_s, buy_limit)
if min_stake_amount is not None and min_stake_amount > stake_amount:
logger.warning(
f'Can\'t open a new trade for {pair_s}: stake amount'
f' is too small ({stake_amount} < {min_stake_amount})')
return False
amount = stake_amount / buy_limit
order_id = self.exchange.buy(pair, buy_limit, amount)['id']
stake_amount_fiat = self.fiat_converter.convert_amount(
stake_amount, stake_currency, fiat_currency)
# Create trade entity and return
self.rpc.send_msg(f"""*{exc_name}:* Buying [{pair_s}]({pair_url}) \
with limit `{buy_limit:.8f} ({stake_amount:.6f} \
{stake_currency}, {stake_amount_fiat:.3f} {fiat_currency})`""")
# Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
trade = Trade(pair=pair,
stake_amount=stake_amount,
amount=amount,
fee_open=fee,
fee_close=fee,
open_rate=buy_limit,
open_rate_requested=buy_limit,
open_date=datetime.utcnow(),
exchange=self.exchange.id,
open_order_id=order_id)
Trade.session.add(trade)
Trade.session.flush()
return True
def process_maybe_execute_buy(self) -> bool:
"""
Tries to execute a buy trade in a safe way
:return: True if executed
"""
try:
# Create entity and execute trade
if self.create_trade():
return True
logger.info(
'Found no buy signals for whitelisted currencies. Trying again..'
)
return False
except DependencyException as exception:
logger.warning('Unable to create trade: %s', exception)
return False
def process_maybe_execute_sell(self, trade: Trade) -> bool:
"""
Tries to execute a sell trade
:return: True if executed
"""
try:
# Get order details for actual price per unit
if trade.open_order_id:
# Update trade with order values
logger.info('Found open order for %s', trade)
order = self.exchange.get_order(trade.open_order_id,
trade.pair)
# Try update amount (binance-fix)
try:
new_amount = self.get_real_amount(trade, order)
if order['amount'] != new_amount:
order['amount'] = new_amount
# Fee was applied, so set to 0
trade.fee_open = 0
except OperationalException as exception:
logger.warning("could not update trade amount: %s",
exception)
trade.update(order)
if trade.is_open and trade.open_order_id is None:
# Check if we can sell our current pair
return self.handle_trade(trade)
except DependencyException as exception:
logger.warning('Unable to sell trade: %s', exception)
return False
def get_real_amount(self, trade: Trade, order: Dict) -> float:
"""
Get real amount for the trade
Necessary for self.exchanges which charge fees in base currency (e.g. binance)
"""
order_amount = order['amount']
# Only run for closed orders
if trade.fee_open == 0 or order['status'] == 'open':
return order_amount
# use fee from order-dict if possible
if 'fee' in order and order['fee'] and (order['fee'].keys() >=
{'currency', 'cost'}):
if trade.pair.startswith(order['fee']['currency']):
new_amount = order_amount - order['fee']['cost']
logger.info(
"Applying fee on amount for %s (from %s to %s) from Order",
trade, order['amount'], new_amount)
return new_amount
# Fallback to Trades
trades = self.exchange.get_trades_for_order(trade.open_order_id,
trade.pair,
trade.open_date)
if len(trades) == 0:
logger.info(
"Applying fee on amount for %s failed: myTrade-Dict empty found",
trade)
return order_amount
amount = 0
fee_abs = 0
for exectrade in trades:
amount += exectrade['amount']
if "fee" in exectrade and (exectrade['fee'].keys() >=
{'currency', 'cost'}):
# only applies if fee is in quote currency!
if trade.pair.startswith(exectrade['fee']['currency']):
fee_abs += exectrade['fee']['cost']
if amount != order_amount:
logger.warning(
f"amount {amount} does not match amount {trade.amount}")
raise OperationalException("Half bought? Amounts don't match")
real_amount = amount - fee_abs
if fee_abs != 0:
logger.info(f"""Applying fee on amount for {trade} \
(from {order_amount} to {real_amount}) from Trades""")
return real_amount
def handle_trade(self, trade: Trade) -> bool:
"""
Sells the current pair if the threshold is reached and updates the trade record.
:return: True if trade has been sold, False otherwise
"""
if not trade.is_open:
raise ValueError(f'attempt to handle closed trade: {trade}')
logger.debug('Handling %s ...', trade)
current_rate = self.exchange.get_ticker(trade.pair)['bid']
(buy, sell) = (False, False)
experimental = self.config.get('experimental', {})
if experimental.get('use_sell_signal') or experimental.get(
'ignore_roi_if_buy_signal'):
(buy, sell) = self.analyze.get_signal(
self.exchange, trade.pair, self.analyze.get_ticker_interval())
if self.analyze.should_sell(trade, current_rate, datetime.utcnow(),
buy, sell):
self.execute_sell(trade, current_rate)
return True
logger.info(
'Found no sell signals for whitelisted currencies. Trying again..')
return False
def check_handle_timedout(self, timeoutvalue: int) -> None:
"""
Check if any orders are timed out and cancel if neccessary
:param timeoutvalue: Number of minutes until order is considered timed out
:return: None
"""
timeoutthreashold = arrow.utcnow().shift(
minutes=-timeoutvalue).datetime
for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
try:
# FIXME: Somehow the query above returns results
# where the open_order_id is in fact None.
# This is probably because the record got
# updated via /forcesell in a different thread.
if not trade.open_order_id:
continue
order = self.exchange.get_order(trade.open_order_id,
trade.pair)
except requests.exceptions.RequestException:
logger.info('Cannot query order for %s due to %s', trade,
traceback.format_exc())
continue
ordertime = arrow.get(order['datetime']).datetime
# Check if trade is still actually open
if int(order['remaining']) == 0:
continue
if order['side'] == 'buy' and ordertime < timeoutthreashold:
self.handle_timedout_limit_buy(trade, order)
elif order['side'] == 'sell' and ordertime < timeoutthreashold:
self.handle_timedout_limit_sell(trade, order)
# FIX: 20180110, why is cancel.order unconditionally here, whereas
# it is conditionally called in the
# handle_timedout_limit_sell()?
def handle_timedout_limit_buy(self, trade: Trade, order: Dict) -> bool:
"""Buy timeout - cancel order
:return: True if order was fully cancelled
"""
pair_s = trade.pair.replace('_', '/')
self.exchange.cancel_order(trade.open_order_id, trade.pair)
if order['remaining'] == order['amount']:
# if trade is not partially completed, just delete the trade
Trade.session.delete(trade)
Trade.session.flush()
logger.info('Buy order timeout for %s.', trade)
self.rpc.send_msg(
f'*Timeout:* Unfilled buy order for {pair_s} cancelled')
return True
# if trade is partially complete, edit the stake details for the trade
# and close the order
trade.amount = order['amount'] - order['remaining']
trade.stake_amount = trade.amount * trade.open_rate
trade.open_order_id = None
logger.info('Partial buy order timeout for %s.', trade)
self.rpc.send_msg(
f'*Timeout:* Remaining buy order for {pair_s} cancelled')
return False
# FIX: 20180110, should cancel_order() be cond. or unconditionally called?
def handle_timedout_limit_sell(self, trade: Trade, order: Dict) -> bool:
"""
Sell timeout - cancel order and update trade
:return: True if order was fully cancelled
"""
pair_s = trade.pair.replace('_', '/')
if order['remaining'] == order['amount']:
# if trade is not partially completed, just cancel the trade
self.exchange.cancel_order(trade.open_order_id, trade.pair)
trade.close_rate = None
trade.close_profit = None
trade.close_date = None
trade.is_open = True
trade.open_order_id = None
self.rpc.send_msg(
f'*Timeout:* Unfilled sell order for {pair_s} cancelled')
logger.info('Sell order timeout for %s.', trade)
return True
# TODO: figure out how to handle partially complete sell orders
return False
def execute_sell(self, trade: Trade, limit: float) -> None:
"""
Executes a limit sell for the given trade and limit
:param trade: Trade instance
:param limit: limit rate for the sell order
:return: None
"""
exc = trade.exchange
pair = trade.pair
# Execute sell and update trade record
order_id = self.exchange.sell(str(trade.pair), limit,
trade.amount)['id']
trade.open_order_id = order_id
trade.close_rate_requested = limit
fmt_exp_profit = round(trade.calc_profit_percent(rate=limit) * 100, 2)
profit_trade = trade.calc_profit(rate=limit)
current_rate = self.exchange.get_ticker(trade.pair)['bid']
profit = trade.calc_profit_percent(limit)
pair_url = self.exchange.get_pair_detail_url(trade.pair)
gain = "profit" if fmt_exp_profit > 0 else "loss"
message = f"*{exc}:* Selling\n" \
f"*Current Pair:* [{pair}]({pair_url})\n" \
f"*Limit:* `{limit}`\n" \
f"*Amount:* `{round(trade.amount, 8)}`\n" \
f"*Open Rate:* `{trade.open_rate:.8f}`\n" \
f"*Current Rate:* `{current_rate:.8f}`\n" \
f"*Profit:* `{round(profit * 100, 2):.2f}%`" \
""
# For regular case, when the configuration exists
if 'stake_currency' in self.config and 'fiat_display_currency' in self.config:
stake = self.config['stake_currency']
fiat = self.config['fiat_display_currency']
fiat_converter = CryptoToFiatConverter()
profit_fiat = fiat_converter.convert_amount(
profit_trade, stake, fiat)
message += f'` ({gain}: {fmt_exp_profit:.2f}%, {profit_trade:.8f} {stake}`' \
f'` / {profit_fiat:.3f} {fiat})`'\
''
# Because telegram._forcesell does not have the configuration
# Ignore the FIAT value and does not show the stake_currency as well
else:
message += '` ({gain}: {profit_percent:.2f}%, {profit_coin:.8f})`'.format(
gain="profit" if fmt_exp_profit > 0 else "loss",
profit_percent=fmt_exp_profit,
profit_coin=profit_trade)
# Send the message
self.rpc.send_msg(message)
Trade.session.flush()
def test_dataframe_correct_length(result):
dataframe = Analyze.parse_ticker_dataframe(result)
assert len(result.index) == len(dataframe.index)
class Backtesting(object):
"""
Backtesting class, this class contains all the logic to run a backtest
To run a backtest:
backtesting = Backtesting(config)
backtesting.start()
"""
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
self.analyze = None
self.ticker_interval = None
self.ML_tickerdata_to_dataframe = None
self.populate_buy_trend = None
self.populate_sell_trend = None
self.slippage = None
self._init()
def _init(self) -> None:
"""
Init objects required for backtesting
:return: None
"""
self.analyze = Analyze(self.config)
self.ticker_interval = self.analyze.strategy.ticker_interval
self.slippage = self.analyze.strategy.slippage
self.ML_tickerdata_to_dataframe = self.analyze.ML_tickerdata_to_dataframe
self.populate_buy_trend = self.analyze.populate_buy_trend
self.populate_sell_trend = self.analyze.populate_sell_trend
exchange._API = Bittrex({'key': '', 'secret': ''})
@staticmethod
def get_timeframe(
data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
"""
Get the maximum timeframe for the given backtest data
:param data: dictionary with preprocessed backtesting data
:return: tuple containing min_date, max_date
"""
timeframe = [(arrow.get(min(frame.date)), arrow.get(max(frame.date)))
for frame in data.values()]
return min(timeframe, key=operator.itemgetter(0))[0], \
max(timeframe, key=operator.itemgetter(1))[1]
def _generate_text_table(self, data: Dict[str, Dict],
results: DataFrame) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:return: pretty printed table with tabulate as str
"""
stake_currency = self.config.get('stake_currency')
floatfmt = ('s', 'd', '.2f', '.8f', '.1f')
tabular_data = []
headers = [
'pair', 'buy count', 'avg profit %',
'total profit ' + stake_currency, 'avg duration', 'profit', 'loss'
]
for pair in data:
result = results[results.currency == pair]
tabular_data.append([
pair,
len(result.index),
result.profit_percent.mean() * 100.0,
result.profit_BTC.sum(),
result.duration.mean(),
len(result[result.profit_BTC > 0]),
len(result[result.profit_BTC < 0])
])
# Append Total
tabular_data.append([
'TOTAL',
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_BTC.sum(),
results.duration.mean(),
len(results[results.profit_BTC > 0]),
len(results[results.profit_BTC < 0])
])
return tabulate(tabular_data, headers=headers, floatfmt=floatfmt)
def _get_sell_trade_entry(self, pair: str, buy_row: DataFrame,
partial_ticker: List, trade_count_lock: Dict,
args: Dict) -> Optional[Tuple]:
stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
trade = Trade(
open_rate=buy_row.close +
self.slippage, #implement slippage 0.01 for buy_row
open_date=buy_row.date,
stake_amount=stake_amount,
amount=stake_amount / buy_row.open,
fee=exchange.get_fee())
# calculate win/lose forwards from buy point
for sell_row in partial_ticker:
if max_open_trades > 0:
# Increase trade_count_lock for every iteration
trade_count_lock[sell_row.date] = trade_count_lock.get(
sell_row.date, 0) + 1
buy_signal = sell_row.buy
#implement slippage 0.01 for sell_row
if self.analyze.should_sell(trade, sell_row.close - self.slippage,
sell_row.date, buy_signal,
sell_row.sell):
return \
sell_row, \
(
pair,
trade.calc_profit_percent(rate=sell_row.close),
trade.calc_profit(rate=sell_row.close),
(sell_row.date - buy_row.date).seconds // 60
), \
sell_row.date
return None
def backtest(self, args: Dict) -> DataFrame:
"""
Implements backtesting functionality
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
Of course try to not have ugly code. By some accessor are sometime slower than functions.
Avoid, logging on this method
:param args: a dict containing:
stake_amount: btc amount to use for each trade
processed: a processed dictionary with format {pair, data}
max_open_trades: maximum number of concurrent trades (default: 0, disabled)
realistic: do we try to simulate realistic trades? (default: True)
sell_profit_only: sell if profit only
use_sell_signal: act on sell-signal
:return: DataFrame
"""
headers = ['date', 'buy', 'open', 'close', 'sell']
processed = args['processed']
max_open_trades = args.get('max_open_trades', 0)
realistic = args.get('realistic', False)
record = args.get('record', None)
records = []
trades = []
trade_count_lock = {}
for pair, pair_data in processed.items():
pair_data['buy'], pair_data[
'sell'] = 0, 0 # cleanup from previous run
ticker_data = self.populate_sell_trend(
self.populate_buy_trend(pair_data))[headers]
ticker = [x for x in ticker_data.itertuples()]
lock_pair_until = None
for index, row in enumerate(ticker):
if row.buy == 0 or row.sell == 1:
continue # skip rows where no buy signal or that would immediately sell off
if realistic:
if lock_pair_until is not None and row.date <= 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:
continue
trade_count_lock[row.date] = trade_count_lock.get(
row.date, 0) + 1
ret = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
trade_count_lock, args)
if ret:
row2, trade_entry, next_date = ret
lock_pair_until = next_date
trades.append(trade_entry)
if record:
# Note, need to be json.dump friendly
# 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'), index, trade_entry[3]))
# For now export inside backtest(), maybe change so that backtest()
# returns a tuple like: (dataframe, records, logs, etc)
if record and record.find('trades') >= 0:
logger.info('Dumping backtest results')
file_dump_json('backtest-result.json', records)
labels = ['currency', 'profit_percent', 'profit_BTC', 'duration']
return DataFrame.from_records(trades, columns=labels)
def run_buy_sell_strategy(self, data, initial_cash, transaction_cost,
alpha):
"""Runs a simple strategy.
Buys alpha*portfolio value when predicted up and sells alpha*portfolio
value when predicted down.
Assume that data has the following format
[BTC-USD_Low
BTC-USD_High
BTC-USD_Open
BTC-USD_Close
BTC-USD_Volume
BTC-USD_Prediction]
The trading startegy takes 'Prediction' column to make buy/sell decision.
"""
trading_portfolio = pd.DataFrame(index=data.index)
trading_portfolio['Portfolio_value'] = pd.Series(index=data.index)
trading_portfolio['Coins_held'] = pd.Series(index=data.index)
trading_portfolio['Cash_held'] = pd.Series(index=data.index)
coins_held = 0
cash_held = initial_cash
portfolio_value = coins_held + cash_held
for i, row in enumerate(data.values):
date = data.index[i]
low, high, open, close, volume, prediction = row
if prediction == 1:
# Buy coin.
transaction = min(alpha * portfolio_value, cash_held)
fee = transaction * transaction_cost
coins_held += transaction / close
cash_held -= transaction - fee
if prediction == 0:
# Sell coin.
transaction = min(alpha * portfolio_value, coins_held * close)
fee = transaction * transaction_cost
coins_held -= transaction / close
cash_held += transaction - fee
portfolio_value = coins_held * close + cash_held
trading_portfolio['Portfolio_value'][date] = portfolio_value
trading_portfolio['Coins_held'][date] = coins_held
trading_portfolio['Cash_held'][date] = cash_held
profit = portfolio_value - initial_cash
ret = 100 * profit / initial_cash
avg_value = trading_portfolio['Portfolio_value'].mean()
std_value = trading_portfolio['Portfolio_value'].std()
trading_stats = {
'profit_dollar': profit,
'return_percent': ret,
'avg_value': avg_value,
'std value': std_value
}
return (trading_portfolio, trading_stats)
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'])
timerange = Arguments.parse_timerange(self.config.get('timerange'))
data = optimize.load_data(self.config['datadir'],
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=False,
timerange=timerange)
preprocessed = self.ML_tickerdata_to_dataframe(data)
out_coin = 'BTC_XMR'
test_ratio = 0.2
data = ml_utils.run_pipeline(preprocessed[out_coin], out_coin,
test_ratio)
initial_cash = 100 # in USD
transaction_cost = 0.01 # as ratio for fee
alpha = 0.05 # ratio of portfolio value that we trade each transaction
(trading_portfolio,
trading_stats) = self.run_buy_sell_strategy(data, initial_cash,
transaction_cost, alpha)
print(trading_portfolio)
#print(trading_portfolio.head(),"Head of trading series.")
print("Stats from trading: ", trading_stats)
# plots trading portfolio value in time next to out_coin price
trading_portfolio.plot(subplots=True, figsize=(6, 6))
plt.legend(loc='best')
plt.show()
class Backtesting(object):
"""
Backtesting class, this class contains all the logic to run a backtest
To run a backtest:
backtesting = Backtesting(config)
backtesting.start()
"""
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
self.analyze = None
self.ticker_interval = None
self.tickerdata_to_dataframe = None
self.populate_buy_trend = None
self.populate_sell_trend = None
self.slippage = None
self._init()
def _init(self) -> None:
"""
Init objects required for backtesting
:return: None
"""
self.analyze = Analyze(self.config)
self.ticker_interval = self.analyze.strategy.ticker_interval
self.slippage = self.analyze.strategy.slippage
self.tickerdata_to_dataframe = self.analyze.tickerdata_to_dataframe
self.populate_buy_trend = self.analyze.populate_buy_trend
self.populate_sell_trend = self.analyze.populate_sell_trend
exchange._API = Bittrex({'key': '', 'secret': ''})
@staticmethod
def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
"""
Get the maximum timeframe for the given backtest data
:param data: dictionary with preprocessed backtesting data
:return: tuple containing min_date, max_date
"""
timeframe = [
(arrow.get(min(frame.date)), arrow.get(max(frame.date)))
for frame in data.values()
]
return min(timeframe, key=operator.itemgetter(0))[0], \
max(timeframe, key=operator.itemgetter(1))[1]
def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:return: pretty printed table with tabulate as str
"""
stake_currency = self.config.get('stake_currency')
floatfmt = ('s', 'd', '.2f', '.8f', '.1f')
tabular_data = []
headers = ['pair', 'buy count', 'avg profit %',
'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
for pair in data:
result = results[results.currency == pair]
tabular_data.append([
pair,
len(result.index),
result.profit_percent.mean() * 100.0,
result.profit_BTC.sum(),
result.duration.mean(),
len(result[result.profit_BTC > 0]),
len(result[result.profit_BTC < 0])
])
# Append Total
tabular_data.append([
'TOTAL',
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_BTC.sum(),
results.duration.mean(),
len(results[results.profit_BTC > 0]),
len(results[results.profit_BTC < 0])
])
return tabulate(tabular_data, headers=headers, floatfmt=floatfmt)
def _get_sell_trade_entry(
self, pair: str, buy_row: DataFrame,
partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[Tuple]:
stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
trade = Trade(
open_rate=buy_row.close + self.slippage, #implement slippage 0.01 for buy_row
open_date=buy_row.date,
stake_amount=stake_amount,
amount=stake_amount / buy_row.open,
fee=exchange.get_fee()
)
# calculate win/lose forwards from buy point
for sell_row in partial_ticker:
if max_open_trades > 0:
# Increase trade_count_lock for every iteration
trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
buy_signal = sell_row.buy
#implement slippage 0.01 for sell_row
if self.analyze.should_sell(trade, sell_row.close - self.slippage, sell_row.date, buy_signal,
sell_row.sell):
return \
sell_row, \
(
pair,
trade.calc_profit_percent(rate=sell_row.close),
trade.calc_profit(rate=sell_row.close),
(sell_row.date - buy_row.date).seconds // 60
), \
sell_row.date
return None
def backtest(self, args: Dict) -> DataFrame:
"""
Implements backtesting functionality
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
Of course try to not have ugly code. By some accessor are sometime slower than functions.
Avoid, logging on this method
:param args: a dict containing:
stake_amount: btc amount to use for each trade
processed: a processed dictionary with format {pair, data}
max_open_trades: maximum number of concurrent trades (default: 0, disabled)
realistic: do we try to simulate realistic trades? (default: True)
sell_profit_only: sell if profit only
use_sell_signal: act on sell-signal
:return: DataFrame
"""
headers = ['date', 'buy', 'open', 'close', 'sell']
processed = args['processed']
max_open_trades = args.get('max_open_trades', 0)
realistic = args.get('realistic', False)
record = args.get('record', None)
records = []
trades = []
trade_count_lock = {}
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
ticker_data = self.populate_sell_trend(self.populate_buy_trend(pair_data))[headers]
ticker = [x for x in ticker_data.itertuples()]
lock_pair_until = None
for index, row in enumerate(ticker):
if row.buy == 0 or row.sell == 1:
continue # skip rows where no buy signal or that would immediately sell off
if realistic:
if lock_pair_until is not None and row.date <= 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:
continue
trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
ret = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
trade_count_lock, args)
if ret:
row2, trade_entry, next_date = ret
lock_pair_until = next_date
trades.append(trade_entry)
if record:
# Note, need to be json.dump friendly
# 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'),
index, trade_entry[3]))
# For now export inside backtest(), maybe change so that backtest()
# returns a tuple like: (dataframe, records, logs, etc)
if record and record.find('trades') >= 0:
logger.info('Dumping backtest results')
file_dump_json('backtest-result.json', records)
labels = ['currency', 'profit_percent', 'profit_BTC', 'duration']
return DataFrame.from_records(trades, columns=labels)
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
)
)
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()
# Get trades already made from the DB
trades: List[Trade] = []
if args.db_url:
persistence.init(_CONF)
trades = Trade.query.filter(Trade.pair.is_(pair)).all()
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.')
fig = generate_graph(pair=pair,
trades=trades,
data=dataframe.tail(750),
args=args)
plot(fig, filename=os.path.join('user_data', 'freqtrade-plot.html'))
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 test_dataframe_correct_length(result):
dataframe = Analyze.parse_ticker_dataframe(result)
assert len(result.index) - 1 == len(
dataframe.index) # last partial candle removed
class Backtesting(object):
"""
Backtesting class, this class contains all the logic to run a backtest
To run a backtest:
backtesting = Backtesting(config)
backtesting.start()
"""
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
self.analyze = Analyze(self.config)
self.ticker_interval = self.analyze.strategy.ticker_interval
self.tickerdata_to_dataframe = self.analyze.tickerdata_to_dataframe
self.populate_buy_trend = self.analyze.populate_buy_trend
self.populate_sell_trend = self.analyze.populate_sell_trend
# Reset keys for backtesting
self.config['exchange']['key'] = ''
self.config['exchange']['secret'] = ''
self.config['exchange']['password'] = ''
self.config['exchange']['uid'] = ''
self.config['dry_run'] = True
self.exchange = Exchange(self.config)
self.fee = self.exchange.get_fee()
@staticmethod
def get_timeframe(
data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
"""
Get the maximum timeframe for the given backtest data
:param data: dictionary with preprocessed backtesting data
:return: tuple containing min_date, max_date
"""
timeframe = [(arrow.get(min(frame.date)), arrow.get(max(frame.date)))
for frame in data.values()]
return min(timeframe, key=operator.itemgetter(0))[0], \
max(timeframe, key=operator.itemgetter(1))[1]
def _generate_text_table(self, data: Dict[str, Dict],
results: DataFrame) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:return: pretty printed table with tabulate as str
"""
stake_currency = str(self.config.get('stake_currency'))
floatfmt = ('s', 'd', '.2f', '.8f', '.1f')
tabular_data = []
headers = [
'pair', 'buy count', 'avg profit %',
'total profit ' + stake_currency, 'avg duration', 'profit', 'loss'
]
for pair in data:
result = results[results.pair == pair]
tabular_data.append([
pair,
len(result.index),
result.profit_percent.mean() * 100.0,
result.profit_abs.sum(),
result.trade_duration.mean(),
len(result[result.profit_abs > 0]),
len(result[result.profit_abs < 0])
])
# Append Total
tabular_data.append([
'TOTAL',
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_abs.sum(),
results.trade_duration.mean(),
len(results[results.profit_abs > 0]),
len(results[results.profit_abs < 0])
])
return tabulate(tabular_data,
headers=headers,
floatfmt=floatfmt,
tablefmt="pipe")
def _store_backtest_result(self, recordfilename: Optional[str],
results: DataFrame) -> None:
records = [(trade_entry.pair, trade_entry.profit_percent,
trade_entry.open_time.timestamp(),
trade_entry.close_time.timestamp(),
trade_entry.open_index - 1, trade_entry.trade_duration)
for index, trade_entry in results.iterrows()]
if records:
logger.info('Dumping backtest results to %s', recordfilename)
file_dump_json(recordfilename, records)
def _get_sell_trade_entry(self, pair: str, buy_row: DataFrame,
partial_ticker: List, trade_count_lock: Dict,
args: Dict) -> Optional[BacktestResult]:
stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
trade = Trade(open_rate=buy_row.close,
open_date=buy_row.date,
stake_amount=stake_amount,
amount=stake_amount / buy_row.open,
fee_open=self.fee,
fee_close=self.fee)
# calculate win/lose forwards from buy point
for sell_row in partial_ticker:
if max_open_trades > 0:
# Increase trade_count_lock for every iteration
trade_count_lock[sell_row.date] = trade_count_lock.get(
sell_row.date, 0) + 1
buy_signal = sell_row.buy
if self.analyze.should_sell(trade, sell_row.close, sell_row.date,
buy_signal, sell_row.sell):
return BacktestResult(
pair=pair,
profit_percent=trade.calc_profit_percent(
rate=sell_row.close),
profit_abs=trade.calc_profit(rate=sell_row.close),
open_time=buy_row.date,
close_time=sell_row.date,
trade_duration=(sell_row.date - buy_row.date).seconds //
60,
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=False)
if partial_ticker:
# no sell condition found - trade stil open at end of backtest period
sell_row = partial_ticker[-1]
btr = BacktestResult(
pair=pair,
profit_percent=trade.calc_profit_percent(rate=sell_row.close),
profit_abs=trade.calc_profit(rate=sell_row.close),
open_time=buy_row.date,
close_time=sell_row.date,
trade_duration=(sell_row.date - buy_row.date).seconds // 60,
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=True)
logger.debug('Force_selling still open trade %s with %s perc - %s',
btr.pair, btr.profit_percent, btr.profit_abs)
return btr
return None
def backtest(self, args: Dict) -> DataFrame:
"""
Implements backtesting functionality
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
Of course try to not have ugly code. By some accessor are sometime slower than functions.
Avoid, logging on this method
:param args: a dict containing:
stake_amount: btc amount to use for each trade
processed: a processed dictionary with format {pair, data}
max_open_trades: maximum number of concurrent trades (default: 0, disabled)
realistic: do we try to simulate realistic trades? (default: True)
:return: DataFrame
"""
headers = ['date', 'buy', 'open', 'close', 'sell']
processed = args['processed']
max_open_trades = args.get('max_open_trades', 0)
realistic = args.get('realistic', False)
trades = []
trade_count_lock: Dict = {}
for pair, pair_data in processed.items():
pair_data['buy'], pair_data[
'sell'] = 0, 0 # cleanup from previous run
ticker_data = self.populate_sell_trend(
self.populate_buy_trend(pair_data))[headers].copy()
# to avoid using data from future, we buy/sell with signal from previous candle
ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
ticker_data.drop(ticker_data.head(1).index, inplace=True)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
ticker = [x for x in ticker_data.itertuples()]
lock_pair_until = None
for index, row in enumerate(ticker):
if row.buy == 0 or row.sell == 1:
continue # skip rows where no buy signal or that would immediately sell off
if realistic:
if lock_pair_until is not None and row.date <= 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:
continue
trade_count_lock[row.date] = trade_count_lock.get(
row.date, 0) + 1
trade_entry = self._get_sell_trade_entry(
pair, row, ticker[index + 1:], trade_count_lock, args)
if trade_entry:
lock_pair_until = trade_entry.close_time
trades.append(trade_entry)
else:
# Set lock_pair_until to end of testing period if trade could not be closed
# This happens only if the buy-signal was with the last candle
lock_pair_until = ticker_data.iloc[-1].date
return DataFrame.from_records(trades, columns=BacktestResult._fields)
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] = self.exchange.get_ticker_history(
pair, self.ticker_interval)
else:
logger.info(
'Using local backtesting data (using whitelist in given config) ...'
)
timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(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),
exchange=self.exchange,
timerange=timerange)
if not data:
logger.critical("No data found. Terminating.")
return
# 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 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
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),
})
if self.config.get('export', False):
self._store_backtest_result(self.config.get('exportfilename'),
results)
logger.info(
'\n======================================== '
'BACKTESTING REPORT'
' =========================================\n'
'%s', self._generate_text_table(data, results))
logger.info(
'\n====================================== '
'LEFT OPEN TRADES REPORT'
' ======================================\n'
'%s',
self._generate_text_table(data, results.loc[results.open_at_end]))
class FreqtradeBot(object):
"""
Freqtrade is the main class of the bot.
This is from here the bot start its logic.
"""
def __init__(self, config: Dict[str, Any], db_url: Optional[str] = None):
"""
Init all variables and object the bot need to work
:param config: configuration dict, you can use the Configuration.get_config()
method to get the config dict.
:param db_url: database connector string for sqlalchemy (Optional)
"""
logger.info(
'Starting freqtrade %s',
__version__,
)
# Init bot states
self.state = State.STOPPED
# Init objects
self.config = config
self.analyze = None
self.fiat_converter = None
self.rpc = None
self.persistence = None
self.exchange = None
self._init_modules(db_url=db_url)
def _init_modules(self, db_url: Optional[str] = None) -> None:
"""
Initializes all modules and updates the config
:param db_url: database connector string for sqlalchemy (Optional)
:return: None
"""
# Initialize all modules
self.analyze = Analyze(self.config)
self.fiat_converter = CryptoToFiatConverter()
self.rpc = RPCManager(self)
persistence.init(self.config, db_url)
exchange.init(self.config)
# Set initial application state
initial_state = self.config.get('initial_state')
if initial_state:
self.state = State[initial_state.upper()]
else:
self.state = State.STOPPED
def clean(self) -> bool:
"""
Cleanup the application state und finish all pending tasks
:return: None
"""
self.rpc.send_msg('*Status:* `Stopping trader...`')
logger.info('Stopping trader and cleaning up modules...')
self.state = State.STOPPED
self.rpc.cleanup()
persistence.cleanup()
return True
def worker(self, old_state: None) -> State:
"""
Trading routine that must be run at each loop
:param old_state: the previous service state from the previous call
:return: current service state
"""
# Log state transition
state = self.state
if state != old_state:
self.rpc.send_msg('*Status:* `{}`'.format(state.name.lower()))
logger.info('Changing state to: %s', state.name)
if state == State.STOPPED:
time.sleep(1)
elif state == State.RUNNING:
min_secs = self.config.get('internals',
{}).get('process_throttle_secs',
constants.PROCESS_THROTTLE_SECS)
nb_assets = self.config.get('dynamic_whitelist', None)
self._throttle(func=self._process,
min_secs=min_secs,
nb_assets=nb_assets)
return state
def _throttle(self, func: Callable[..., Any], min_secs: float, *args,
**kwargs) -> Any:
"""
Throttles the given callable that it
takes at least `min_secs` to finish execution.
:param func: Any callable
:param min_secs: minimum execution time in seconds
:return: Any
"""
start = time.time()
result = func(*args, **kwargs)
end = time.time()
duration = max(min_secs - (end - start), 0.0)
logger.debug('Throttling %s for %.2f seconds', func.__name__, duration)
time.sleep(duration)
return result
def _process(self, nb_assets: Optional[int] = 0) -> bool:
"""
Queries the persistence layer for open trades and handles them,
otherwise a new trade is created.
:param: nb_assets: the maximum number of pairs to be traded at the same time
:return: True if one or more trades has been created or closed, False otherwise
"""
state_changed = False
try:
# Refresh whitelist based on wallet maintenance
sanitized_list = self._refresh_whitelist(
self._gen_pair_whitelist(self.config['stake_currency'])
if nb_assets else self.config['exchange']['pair_whitelist'])
# Keep only the subsets of pairs wanted (up to nb_assets)
final_list = sanitized_list[:nb_assets] if nb_assets else sanitized_list
self.config['exchange']['pair_whitelist'] = final_list
# Query trades from persistence layer
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
# First process current opened trades
for trade in trades:
state_changed |= self.process_maybe_execute_sell(trade)
# Then looking for buy opportunities
if len(trades) < self.config['max_open_trades']:
state_changed = self.process_maybe_execute_buy()
if 'unfilledtimeout' in self.config:
# Check and handle any timed out open orders
self.check_handle_timedout(self.config['unfilledtimeout'])
Trade.session.flush()
except (requests.exceptions.RequestException,
json.JSONDecodeError) as error:
logger.warning('%s, retrying in 30 seconds...', error)
time.sleep(constants.RETRY_TIMEOUT)
except OperationalException:
self.rpc.send_msg(
'*Status:* OperationalException:\n```\n{traceback}```{hint}'.
format(
traceback=traceback.format_exc(),
hint='Issue `/start` if you think it is safe to restart.'))
logger.exception('OperationalException. Stopping trader ...')
self.state = State.STOPPED
return state_changed
@cached(TTLCache(maxsize=1, ttl=1800))
def _gen_pair_whitelist(self,
base_currency: str,
key: str = 'BaseVolume') -> List[str]:
"""
Updates the whitelist with with a dynamically generated list
:param base_currency: base currency as str
:param key: sort key (defaults to 'BaseVolume')
:return: List of pairs
"""
summaries = sorted((s for s in exchange.get_market_summaries()
if s['MarketName'].startswith(base_currency)),
key=lambda s: s.get(key) or 0.0,
reverse=True)
return [s['MarketName'].replace('-', '_') for s in summaries]
def _refresh_whitelist(self, whitelist: List[str]) -> List[str]:
"""
Check wallet health and remove pair from whitelist if necessary
:param whitelist: the sorted list (based on BaseVolume) of pairs the user might want to
trade
:return: the list of pairs the user wants to trade without the one unavailable or
black_listed
"""
sanitized_whitelist = whitelist
health = exchange.get_wallet_health()
known_pairs = set()
for status in health:
pair = '{}_{}'.format(self.config['stake_currency'],
status['Currency'])
# pair is not int the generated dynamic market, or in the blacklist ... ignore it
if pair not in whitelist or pair in self.config['exchange'].get(
'pair_blacklist', []):
continue
# else the pair is valid
known_pairs.add(pair)
# Market is not active
if not status['IsActive']:
sanitized_whitelist.remove(pair)
logger.info('Ignoring %s from whitelist (reason: %s).', pair,
status.get('Notice') or 'wallet is not active')
# We need to remove pairs that are unknown
final_list = [x for x in sanitized_whitelist if x in known_pairs]
return final_list
def get_target_bid(self, ticker: Dict[str, float]) -> float:
"""
Calculates bid target between current ask price and last price
:param ticker: Ticker to use for getting Ask and Last Price
:return: float: Price
"""
if ticker['ask'] < ticker['last']:
return ticker['ask']
balance = self.config['bid_strategy']['ask_last_balance']
return ticker['ask'] + balance * (ticker['last'] - ticker['ask'])
def create_trade(self) -> bool:
"""
Checks the implemented trading indicator(s) for a randomly picked pair,
if one pair triggers the buy_signal a new trade record gets created
:param stake_amount: amount of btc to spend
:param interval: Ticker interval used for Analyze
:return: True if a trade object has been created and persisted, False otherwise
"""
stake_amount = self.config['stake_amount']
interval = self.analyze.get_ticker_interval()
logger.info(
'Checking buy signals to create a new trade with stake_amount: %f ...',
stake_amount)
whitelist = copy.deepcopy(self.config['exchange']['pair_whitelist'])
# Check if stake_amount is fulfilled
if exchange.get_balance(self.config['stake_currency']) < stake_amount:
raise DependencyException(
'stake amount is not fulfilled (currency={})'.format(
self.config['stake_currency']))
# Remove currently opened and latest pairs from whitelist
for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
if trade.pair in whitelist:
whitelist.remove(trade.pair)
logger.debug('Ignoring %s in pair whitelist', trade.pair)
if not whitelist:
raise DependencyException('No currency pairs in whitelist')
# Pick pair based on StochRSI buy signals
for _pair in whitelist:
(buy, sell) = self.analyze.get_signal(_pair, interval)
if buy and not sell:
pair = _pair
break
else:
return False
# Calculate amount
buy_limit = self.get_target_bid(exchange.get_ticker(pair))
amount = stake_amount / buy_limit
order_id = exchange.buy(pair, buy_limit, amount)
stake_amount_fiat = self.fiat_converter.convert_amount(
stake_amount, self.config['stake_currency'],
self.config['fiat_display_currency'])
# Create trade entity and return
self.rpc.send_msg(
'*{}:* Buying [{}]({}) with limit `{:.8f} ({:.6f} {}, {:.3f} {})` '
.format(exchange.get_name().upper(), pair.replace('_', '/'),
exchange.get_pair_detail_url(pair), buy_limit,
stake_amount, self.config['stake_currency'],
stake_amount_fiat, self.config['fiat_display_currency']))
# Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
trade = Trade(pair=pair,
stake_amount=stake_amount,
amount=amount,
fee=exchange.get_fee(),
open_rate=buy_limit,
open_date=datetime.utcnow(),
exchange=exchange.get_name().upper(),
open_order_id=order_id)
Trade.session.add(trade)
Trade.session.flush()
return True
def process_maybe_execute_buy(self) -> bool:
"""
Tries to execute a buy trade in a safe way
:return: True if executed
"""
try:
# Create entity and execute trade
if self.create_trade():
return True
logger.info(
'Found no buy signals for whitelisted currencies. Trying again..'
)
return False
except DependencyException as exception:
logger.warning('Unable to create trade: %s', exception)
return False
def process_maybe_execute_sell(self, trade: Trade) -> bool:
"""
Tries to execute a sell trade
:return: True if executed
"""
# Get order details for actual price per unit
if trade.open_order_id:
# Update trade with order values
logger.info('Found open order for %s', trade)
trade.update(exchange.get_order(trade.open_order_id))
if trade.is_open and trade.open_order_id is None:
# Check if we can sell our current pair
return self.handle_trade(trade)
return False
def handle_trade(self, trade: Trade) -> bool:
"""
Sells the current pair if the threshold is reached and updates the trade record.
:return: True if trade has been sold, False otherwise
"""
if not trade.is_open:
raise ValueError(
'attempt to handle closed trade: {}'.format(trade))
logger.debug('Handling %s ...', trade)
current_rate = exchange.get_ticker(trade.pair)['bid']
(buy, sell) = (False, False)
if self.config.get('experimental', {}).get('use_sell_signal'):
(buy, sell) = self.analyze.get_signal(
trade.pair, self.analyze.get_ticker_interval())
if self.analyze.should_sell(trade, current_rate, datetime.utcnow(),
buy, sell):
self.execute_sell(trade, current_rate)
return True
return False
def check_handle_timedout(self, timeoutvalue: int) -> None:
"""
Check if any orders are timed out and cancel if neccessary
:param timeoutvalue: Number of minutes until order is considered timed out
:return: None
"""
timeoutthreashold = arrow.utcnow().shift(
minutes=-timeoutvalue).datetime
for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
try:
order = exchange.get_order(trade.open_order_id)
except requests.exceptions.RequestException:
logger.info('Cannot query order for %s due to %s', trade,
traceback.format_exc())
continue
ordertime = arrow.get(order['opened'])
# Check if trade is still actually open
if int(order['remaining']) == 0:
continue
if order['type'] == "LIMIT_BUY" and ordertime < timeoutthreashold:
self.handle_timedout_limit_buy(trade, order)
elif order[
'type'] == "LIMIT_SELL" and ordertime < timeoutthreashold:
self.handle_timedout_limit_sell(trade, order)
# FIX: 20180110, why is cancel.order unconditionally here, whereas
# it is conditionally called in the
# handle_timedout_limit_sell()?
def handle_timedout_limit_buy(self, trade: Trade, order: Dict) -> bool:
"""Buy timeout - cancel order
:return: True if order was fully cancelled
"""
exchange.cancel_order(trade.open_order_id)
if order['remaining'] == order['amount']:
# if trade is not partially completed, just delete the trade
Trade.session.delete(trade)
# FIX? do we really need to flush, caller of
# check_handle_timedout will flush afterwards
Trade.session.flush()
logger.info('Buy order timeout for %s.', trade)
self.rpc.send_msg(
'*Timeout:* Unfilled buy order for {} cancelled'.format(
trade.pair.replace('_', '/')))
return True
# if trade is partially complete, edit the stake details for the trade
# and close the order
trade.amount = order['amount'] - order['remaining']
trade.stake_amount = trade.amount * trade.open_rate
trade.open_order_id = None
logger.info('Partial buy order timeout for %s.', trade)
self.rpc.send_msg(
'*Timeout:* Remaining buy order for {} cancelled'.format(
trade.pair.replace('_', '/')))
return False
# FIX: 20180110, should cancel_order() be cond. or unconditionally called?
def handle_timedout_limit_sell(self, trade: Trade, order: Dict) -> bool:
"""
Sell timeout - cancel order and update trade
:return: True if order was fully cancelled
"""
if order['remaining'] == order['amount']:
# if trade is not partially completed, just cancel the trade
exchange.cancel_order(trade.open_order_id)
trade.close_rate = None
trade.close_profit = None
trade.close_date = None
trade.is_open = True
trade.open_order_id = None
self.rpc.send_msg(
'*Timeout:* Unfilled sell order for {} cancelled'.format(
trade.pair.replace('_', '/')))
logger.info('Sell order timeout for %s.', trade)
return True
# TODO: figure out how to handle partially complete sell orders
return False
def execute_sell(self, trade: Trade, limit: float) -> None:
"""
Executes a limit sell for the given trade and limit
:param trade: Trade instance
:param limit: limit rate for the sell order
:return: None
"""
# Execute sell and update trade record
order_id = exchange.sell(str(trade.pair), limit, trade.amount)
trade.open_order_id = order_id
fmt_exp_profit = round(trade.calc_profit_percent(rate=limit) * 100, 2)
profit_trade = trade.calc_profit(rate=limit)
current_rate = exchange.get_ticker(trade.pair, False)['bid']
profit = trade.calc_profit_percent(current_rate)
message = "*{exchange}:* Selling\n" \
"*Current Pair:* [{pair}]({pair_url})\n" \
"*Limit:* `{limit}`\n" \
"*Amount:* `{amount}`\n" \
"*Open Rate:* `{open_rate:.8f}`\n" \
"*Current Rate:* `{current_rate:.8f}`\n" \
"*Profit:* `{profit:.2f}%`" \
"".format(
exchange=trade.exchange,
pair=trade.pair,
pair_url=exchange.get_pair_detail_url(trade.pair),
limit=limit,
open_rate=trade.open_rate,
current_rate=current_rate,
amount=round(trade.amount, 8),
profit=round(profit * 100, 2),
)
# For regular case, when the configuration exists
if 'stake_currency' in self.config and 'fiat_display_currency' in self.config:
fiat_converter = CryptoToFiatConverter()
profit_fiat = fiat_converter.convert_amount(
profit_trade, self.config['stake_currency'],
self.config['fiat_display_currency'])
message += '` ({gain}: {profit_percent:.2f}%, {profit_coin:.8f} {coin}`' \
'` / {profit_fiat:.3f} {fiat})`' \
''.format(
gain="profit" if fmt_exp_profit > 0 else "loss",
profit_percent=fmt_exp_profit,
profit_coin=profit_trade,
coin=self.config['stake_currency'],
profit_fiat=profit_fiat,
fiat=self.config['fiat_display_currency'],
)
# Because telegram._forcesell does not have the configuration
# Ignore the FIAT value and does not show the stake_currency as well
else:
message += '` ({gain}: {profit_percent:.2f}%, {profit_coin:.8f})`'.format(
gain="profit" if fmt_exp_profit > 0 else "loss",
profit_percent=fmt_exp_profit,
profit_coin=profit_trade)
# Send the message
self.rpc.send_msg(message)
Trade.session.flush()
Unit test file for analyse.py
"""
import datetime
import logging
from unittest.mock import MagicMock
import arrow
from pandas import DataFrame
from freqtrade.analyze import Analyze, SignalType
from freqtrade.optimize.__init__ import load_tickerdata_file
from freqtrade.tests.conftest import log_has
# Avoid to reinit the same object again and again
_ANALYZE = Analyze({'strategy': 'DefaultStrategy'})
def test_signaltype_object() -> None:
"""
Test the SignalType object has the mandatory Constants
:return: None
"""
assert hasattr(SignalType, 'BUY')
assert hasattr(SignalType, 'SELL')
def test_analyze_object() -> None:
"""
Test the Analyze object has the mandatory methods
:return: None
def result():
with open('freqtrade/tests/testdata/UNITTEST_BTC-1m.json') as data_file:
return Analyze.parse_ticker_dataframe(json.load(data_file))
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')