adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge pull request #2522 from freqtrade/replace_tickerinterval

Browse Source 
Replace tickerinterval
This commit is contained in:
hroff-1902
2019-11-13 13:50:07 +03:00
committed by GitHub
parent 1d7fb2ffac 6ac73f7cde
commit baea06eac7
24 changed files with 198 additions and 197 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
freqtrade/configuration/timerange.py
View File

@@ -39,12 +39,12 @@ class TimeRange:
if self.startts:
self.startts = self.startts - seconds
def adjust_start_if_necessary(self, ticker_interval_secs: int, startup_candles: int,
def adjust_start_if_necessary(self, timeframe_secs: int, startup_candles: int,
min_date: arrow.Arrow) -> None:
"""
Adjust startts by <startup_candles> candles.
Applies only if no startup-candles have been available.
:param ticker_interval_secs: Ticker interval in seconds e.g. `timeframe_to_seconds('5m')`
:param timeframe_secs: Ticker timeframe in seconds e.g. `timeframe_to_seconds('5m')`
:param startup_candles: Number of candles to move start-date forward
:param min_date: Minimum data date loaded. Key kriterium to decide if start-time
has to be moved
@@ -55,7 +55,7 @@ class TimeRange:
# If no startts was defined, or backtest-data starts at the defined backtest-date
logger.warning("Moving start-date by %s candles to account for startup time.",
startup_candles)
self.startts = (min_date.timestamp + ticker_interval_secs * startup_candles)
self.startts = (min_date.timestamp + timeframe_secs * startup_candles)
self.starttype = 'date'
@staticmethod

4
freqtrade/constants.py
View File

@@ -24,7 +24,7 @@ AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList']
DRY_RUN_WALLET = 999.9
MATH_CLOSE_PREC = 1e-14 # Precision used for float comparisons
TICKER_INTERVALS = [
TIMEFRAMES = [
'1m', '3m', '5m', '15m', '30m',
'1h', '2h', '4h', '6h', '8h', '12h',
'1d', '3d', '1w',
@@ -57,7 +57,7 @@ CONF_SCHEMA = {
'type': 'object',
'properties': {
'max_open_trades': {'type': 'integer', 'minimum': -1},
'ticker_interval': {'type': 'string', 'enum': TICKER_INTERVALS},
'ticker_interval': {'type': 'string', 'enum': TIMEFRAMES},
'stake_currency': {'type': 'string', 'enum': ['BTC', 'XBT', 'ETH', 'USDT', 'EUR', 'USD']},
'stake_amount': {
"type": ["number", "string"],

6
freqtrade/data/btanalysis.py
View File

@@ -178,9 +178,9 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
:return: Returns df with one additional column, col_name, containing the cumulative profit.
"""
from freqtrade.exchange import timeframe_to_minutes
ticker_minutes = timeframe_to_minutes(timeframe)
# Resample to ticker_interval to make sure trades match candles
_trades_sum = trades.resample(f'{ticker_minutes}min', on='close_time')[['profitperc']].sum()
timeframe_minutes = timeframe_to_minutes(timeframe)
# Resample to timeframe to make sure trades match candles
_trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_time')[['profitperc']].sum()
df.loc[:, col_name] = _trades_sum.cumsum()
# Set first value to 0
df.loc[df.iloc[0].name, col_name] = 0

10
freqtrade/data/converter.py
View File

@@ -10,13 +10,13 @@ from pandas import DataFrame, to_datetime
logger = logging.getLogger(__name__)
def parse_ticker_dataframe(ticker: list, ticker_interval: str, pair: str, *,
def parse_ticker_dataframe(ticker: list, timeframe: str, pair: str, *,
fill_missing: bool = True,
drop_incomplete: bool = True) -> DataFrame:
"""
Converts a ticker-list (format ccxt.fetch_ohlcv) to a Dataframe
:param ticker: ticker list, as returned by exchange.async_get_candle_history
:param ticker_interval: ticker_interval (e.g. 5m). Used to fill up eventual missing data
:param timeframe: timeframe (e.g. 5m). Used to fill up eventual missing data
:param pair: Pair this data is for (used to warn if fillup was necessary)
:param fill_missing: fill up missing candles with 0 candles
(see ohlcv_fill_up_missing_data for details)
@@ -52,12 +52,12 @@ def parse_ticker_dataframe(ticker: list, ticker_interval: str, pair: str, *,
logger.debug('Dropping last candle')
if fill_missing:
return ohlcv_fill_up_missing_data(frame, ticker_interval, pair)
return ohlcv_fill_up_missing_data(frame, timeframe, pair)
else:
return frame
def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: str, pair: str) -> DataFrame:
def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str) -> DataFrame:
"""
Fills up missing data with 0 volume rows,
using the previous close as price for "open", "high" "low" and "close", volume is set to 0
@@ -72,7 +72,7 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: str, pair:
'close': 'last',
'volume': 'sum'
}
ticker_minutes = timeframe_to_minutes(ticker_interval)
ticker_minutes = timeframe_to_minutes(timeframe)
# Resample to create "NAN" values
df = dataframe.resample(f'{ticker_minutes}min', on='date').agg(ohlc_dict)

25
freqtrade/data/dataprovider.py
View File

@@ -37,52 +37,53 @@ class DataProvider:
@property
def available_pairs(self) -> List[Tuple[str, str]]:
"""
Return a list of tuples containing pair, ticker_interval for which data is currently cached.
Return a list of tuples containing (pair, timeframe) for which data is currently cached.
Should be whitelist + open trades.
"""
return list(self._exchange._klines.keys())
def ohlcv(self, pair: str, ticker_interval: str = None, copy: bool = True) -> DataFrame:
def ohlcv(self, pair: str, timeframe: str = None, copy: bool = True) -> DataFrame:
"""
Get ohlcv data for the given pair as DataFrame
Please use the `available_pairs` method to verify which pairs are currently cached.
:param pair: pair to get the data for
:param ticker_interval: ticker interval to get data for
:param timeframe: Ticker timeframe to get data for
:param copy: copy dataframe before returning if True.
Use False only for read-only operations (where the dataframe is not modified)
"""
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
return self._exchange.klines((pair, ticker_interval or self._config['ticker_interval']),
return self._exchange.klines((pair, timeframe or self._config['ticker_interval']),
copy=copy)
else:
return DataFrame()
def historic_ohlcv(self, pair: str, ticker_interval: str = None) -> DataFrame:
def historic_ohlcv(self, pair: str, timeframe: str = None) -> DataFrame:
"""
Get stored historic ohlcv data
:param pair: pair to get the data for
:param ticker_interval: ticker interval to get data for
:param timeframe: timeframe to get data for
"""
return load_pair_history(pair=pair,
ticker_interval=ticker_interval or self._config['ticker_interval'],
timeframe=timeframe or self._config['ticker_interval'],
datadir=Path(self._config['datadir'])
)
def get_pair_dataframe(self, pair: str, ticker_interval: str = None) -> DataFrame:
def get_pair_dataframe(self, pair: str, timeframe: str = None) -> DataFrame:
"""
Return pair ohlcv data, either live or cached historical -- depending
on the runmode.
:param pair: pair to get the data for
:param ticker_interval: ticker interval to get data for
:param timeframe: timeframe to get data for
:return: Dataframe for this pair
"""
if self.runmode in (RunMode.DRY_RUN, RunMode.LIVE):
# Get live ohlcv data.
data = self.ohlcv(pair=pair, ticker_interval=ticker_interval)
data = self.ohlcv(pair=pair, timeframe=timeframe)
else:
# Get historic ohlcv data (cached on disk).
data = self.historic_ohlcv(pair=pair, ticker_interval=ticker_interval)
data = self.historic_ohlcv(pair=pair, timeframe=timeframe)
if len(data) == 0:
logger.warning(f"No data found for ({pair}, {ticker_interval}).")
logger.warning(f"No data found for ({pair}, {timeframe}).")
return data
def market(self, pair: str) -> Optional[Dict[str, Any]]:

74
freqtrade/data/history.py
View File

@@ -63,13 +63,13 @@ def trim_dataframe(df: DataFrame, timerange: TimeRange) -> DataFrame:
return df
def load_tickerdata_file(datadir: Path, pair: str, ticker_interval: str,
def load_tickerdata_file(datadir: Path, pair: str, timeframe: str,
timerange: Optional[TimeRange] = None) -> Optional[list]:
"""
Load a pair from file, either .json.gz or .json
:return: tickerlist or None if unsuccessful
"""
filename = pair_data_filename(datadir, pair, ticker_interval)
filename = pair_data_filename(datadir, pair, timeframe)
pairdata = misc.file_load_json(filename)
if not pairdata:
return []
@@ -80,11 +80,11 @@ def load_tickerdata_file(datadir: Path, pair: str, ticker_interval: str,
def store_tickerdata_file(datadir: Path, pair: str,
ticker_interval: str, data: list, is_zip: bool = False):
timeframe: str, data: list, is_zip: bool = False):
"""
Stores tickerdata to file
"""
filename = pair_data_filename(datadir, pair, ticker_interval)
filename = pair_data_filename(datadir, pair, timeframe)
misc.file_dump_json(filename, data, is_zip=is_zip)
@@ -121,7 +121,7 @@ def _validate_pairdata(pair, pairdata, timerange: TimeRange):
def load_pair_history(pair: str,
ticker_interval: str,
timeframe: str,
datadir: Path,
timerange: Optional[TimeRange] = None,
refresh_pairs: bool = False,
@@ -133,7 +133,7 @@ def load_pair_history(pair: str,
"""
Loads cached ticker history for the given pair.
:param pair: Pair to load data for
:param ticker_interval: Ticker-interval (e.g. "5m")
:param timeframe: Ticker timeframe (e.g. "5m")
:param datadir: Path to the data storage location.
:param timerange: Limit data to be loaded to this timerange
:param refresh_pairs: Refresh pairs from exchange.
@@ -147,34 +147,34 @@ def load_pair_history(pair: str,
timerange_startup = deepcopy(timerange)
if startup_candles > 0 and timerange_startup:
timerange_startup.subtract_start(timeframe_to_seconds(ticker_interval) * startup_candles)
timerange_startup.subtract_start(timeframe_to_seconds(timeframe) * startup_candles)
# The user forced the refresh of pairs
if refresh_pairs:
download_pair_history(datadir=datadir,
exchange=exchange,
pair=pair,
ticker_interval=ticker_interval,
timeframe=timeframe,
timerange=timerange)
pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange_startup)
pairdata = load_tickerdata_file(datadir, pair, timeframe, timerange=timerange_startup)
if pairdata:
if timerange_startup:
_validate_pairdata(pair, pairdata, timerange_startup)
return parse_ticker_dataframe(pairdata, ticker_interval, pair=pair,
return parse_ticker_dataframe(pairdata, timeframe, pair=pair,
fill_missing=fill_up_missing,
drop_incomplete=drop_incomplete)
else:
logger.warning(
f'No history data for pair: "{pair}", interval: {ticker_interval}. '
f'No history data for pair: "{pair}", timeframe: {timeframe}. '
'Use `freqtrade download-data` to download the data'
)
return None
def load_data(datadir: Path,
ticker_interval: str,
timeframe: str,
pairs: List[str],
refresh_pairs: bool = False,
exchange: Optional[Exchange] = None,
@@ -186,7 +186,7 @@ def load_data(datadir: Path,
"""
Loads ticker history data for a list of pairs
:param datadir: Path to the data storage location.
:param ticker_interval: Ticker-interval (e.g. "5m")
:param timeframe: Ticker Timeframe (e.g. "5m")
:param pairs: List of pairs to load
:param refresh_pairs: Refresh pairs from exchange.
(Note: Requires exchange to be passed as well.)
@@ -206,7 +206,7 @@ def load_data(datadir: Path,
logger.info(f'Using indicator startup period: {startup_candles} ...')
for pair in pairs:
hist = load_pair_history(pair=pair, ticker_interval=ticker_interval,
hist = load_pair_history(pair=pair, timeframe=timeframe,
datadir=datadir, timerange=timerange,
refresh_pairs=refresh_pairs,
exchange=exchange,
@@ -220,9 +220,9 @@ def load_data(datadir: Path,
return result
def pair_data_filename(datadir: Path, pair: str, ticker_interval: str) -> Path:
def pair_data_filename(datadir: Path, pair: str, timeframe: str) -> Path:
pair_s = pair.replace("/", "_")
filename = datadir.joinpath(f'{pair_s}-{ticker_interval}.json')
filename = datadir.joinpath(f'{pair_s}-{timeframe}.json')
return filename
@@ -232,7 +232,7 @@ def pair_trades_filename(datadir: Path, pair: str) -> Path:
return filename
def _load_cached_data_for_updating(datadir: Path, pair: str, ticker_interval: str,
def _load_cached_data_for_updating(datadir: Path, pair: str, timeframe: str,
timerange: Optional[TimeRange]) -> Tuple[List[Any],
Optional[int]]:
"""
@@ -250,12 +250,12 @@ def _load_cached_data_for_updating(datadir: Path, pair: str, ticker_interval: st
if timerange.starttype == 'date':
since_ms = timerange.startts * 1000
elif timerange.stoptype == 'line':
num_minutes = timerange.stopts * timeframe_to_minutes(ticker_interval)
num_minutes = timerange.stopts * timeframe_to_minutes(timeframe)
since_ms = arrow.utcnow().shift(minutes=num_minutes).timestamp * 1000
# read the cached file
# Intentionally don't pass timerange in - since we need to load the full dataset.
data = load_tickerdata_file(datadir, pair, ticker_interval)
data = load_tickerdata_file(datadir, pair, timeframe)
# remove the last item, could be incomplete candle
if data:
data.pop()
@@ -276,18 +276,18 @@ def _load_cached_data_for_updating(datadir: Path, pair: str, ticker_interval: st
def download_pair_history(datadir: Path,
exchange: Optional[Exchange],
pair: str,
ticker_interval: str = '5m',
timeframe: str = '5m',
timerange: Optional[TimeRange] = None) -> bool:
"""
Download the latest ticker intervals from the exchange for the pair passed in parameters
The data is downloaded starting from the last correct ticker interval data that
Download latest candles from the exchange for the pair and timeframe passed in parameters
The data is downloaded starting from the last correct data that
exists in a cache. If timerange starts earlier than the data in the cache,
the full data will be redownloaded
Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
:param pair: pair to download
:param ticker_interval: ticker interval
:param timeframe: Ticker Timeframe (e.g 5m)
:param timerange: range of time to download
:return: bool with success state
"""
@@ -298,17 +298,17 @@ def download_pair_history(datadir: Path,
try:
logger.info(
f'Download history data for pair: "{pair}", interval: {ticker_interval} '
f'Download history data for pair: "{pair}", timeframe: {timeframe} '
f'and store in {datadir}.'
)
data, since_ms = _load_cached_data_for_updating(datadir, pair, ticker_interval, timerange)
data, since_ms = _load_cached_data_for_updating(datadir, pair, timeframe, timerange)
logger.debug("Current Start: %s", misc.format_ms_time(data[1][0]) if data else 'None')
logger.debug("Current End: %s", misc.format_ms_time(data[-1][0]) if data else 'None')
# Default since_ms to 30 days if nothing is given
new_data = exchange.get_historic_ohlcv(pair=pair, ticker_interval=ticker_interval,
new_data = exchange.get_historic_ohlcv(pair=pair, timeframe=timeframe,
since_ms=since_ms if since_ms
else
int(arrow.utcnow().shift(
@@ -318,12 +318,12 @@ def download_pair_history(datadir: Path,
logger.debug("New Start: %s", misc.format_ms_time(data[0][0]))
logger.debug("New End: %s", misc.format_ms_time(data[-1][0]))
store_tickerdata_file(datadir, pair, ticker_interval, data=data)
store_tickerdata_file(datadir, pair, timeframe, data=data)
return True
except Exception as e:
logger.error(
f'Failed to download history data for pair: "{pair}", interval: {ticker_interval}. '
f'Failed to download history data for pair: "{pair}", timeframe: {timeframe}. '
f'Error: {e}'
)
return False
@@ -343,17 +343,17 @@ def refresh_backtest_ohlcv_data(exchange: Exchange, pairs: List[str], timeframes
pairs_not_available.append(pair)
logger.info(f"Skipping pair {pair}...")
continue
for ticker_interval in timeframes:
for timeframe in timeframes:
dl_file = pair_data_filename(dl_path, pair, ticker_interval)
dl_file = pair_data_filename(dl_path, pair, timeframe)
if erase and dl_file.exists():
logger.info(
f'Deleting existing data for pair {pair}, interval {ticker_interval}.')
f'Deleting existing data for pair {pair}, interval {timeframe}.')
dl_file.unlink()
logger.info(f'Downloading pair {pair}, interval {ticker_interval}.')
logger.info(f'Downloading pair {pair}, interval {timeframe}.')
download_pair_history(datadir=dl_path, exchange=exchange,
pair=pair, ticker_interval=str(ticker_interval),
pair=pair, timeframe=str(timeframe),
timerange=timerange)
return pairs_not_available
@@ -459,7 +459,7 @@ def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]
def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
max_date: datetime, ticker_interval_mins: int) -> bool:
max_date: datetime, timeframe_mins: int) -> bool:
"""
Validates preprocessed backtesting data for missing values and shows warnings about it that.
@@ -467,10 +467,10 @@ def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,
:param pair: pair used for log output.
:param min_date: start-date of the data
:param max_date: end-date of the data
:param ticker_interval_mins: ticker interval in minutes
:param timeframe_mins: ticker Timeframe in minutes
"""
# total difference in minutes / interval-minutes
expected_frames = int((max_date - min_date).total_seconds() // 60 // ticker_interval_mins)
# total difference in minutes / timeframe-minutes
expected_frames = int((max_date - min_date).total_seconds() // 60 // timeframe_mins)
found_missing = False
dflen = len(data)
if dflen < expected_frames:

2
freqtrade/edge/__init__.py
View File

@@ -97,7 +97,7 @@ class Edge:
data = history.load_data(
datadir=Path(self.config['datadir']),
pairs=pairs,
ticker_interval=self.strategy.ticker_interval,
timeframe=self.strategy.ticker_interval,
refresh_pairs=self._refresh_pairs,
exchange=self.exchange,
timerange=self._timerange,

67
freqtrade/exchange/exchange.py
View File

@@ -536,40 +536,40 @@ class Exchange:
logger.info("returning cached ticker-data for %s", pair)
return self._cached_ticker[pair]
def get_historic_ohlcv(self, pair: str, ticker_interval: str,
def get_historic_ohlcv(self, pair: str, timeframe: str,
since_ms: int) -> List:
"""
Gets candle history using asyncio and returns the list of candles.
Handles all async doing.
Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
:param pair: Pair to download
:param ticker_interval: Interval to get
:param timeframe: Ticker Timeframe to get
:param since_ms: Timestamp in milliseconds to get history from
:returns List of tickers
"""
return asyncio.get_event_loop().run_until_complete(
self._async_get_historic_ohlcv(pair=pair, ticker_interval=ticker_interval,
self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
since_ms=since_ms))
async def _async_get_historic_ohlcv(self, pair: str,
ticker_interval: str,
timeframe: str,
since_ms: int) -> List:
one_call = timeframe_to_msecs(ticker_interval) * self._ohlcv_candle_limit
one_call = timeframe_to_msecs(timeframe) * self._ohlcv_candle_limit
logger.debug(
"one_call: %s msecs (%s)",
one_call,
arrow.utcnow().shift(seconds=one_call // 1000).humanize(only_distance=True)
)
input_coroutines = [self._async_get_candle_history(
pair, ticker_interval, since) for since in
pair, timeframe, since) for since in
range(since_ms, arrow.utcnow().timestamp * 1000, one_call)]
tickers = await asyncio.gather(*input_coroutines, return_exceptions=True)
# Combine tickers
data: List = []
for p, ticker_interval, ticker in tickers:
for p, timeframe, ticker in tickers:
if p == pair:
data.extend(ticker)
# Sort data again after extending the result - above calls return in "async order"
@@ -589,14 +589,14 @@ class Exchange:
input_coroutines = []
# Gather coroutines to run
for pair, ticker_interval in set(pair_list):
if (not ((pair, ticker_interval) in self._klines)
or self._now_is_time_to_refresh(pair, ticker_interval)):
input_coroutines.append(self._async_get_candle_history(pair, ticker_interval))
for pair, timeframe in set(pair_list):
if (not ((pair, timeframe) in self._klines)
or self._now_is_time_to_refresh(pair, timeframe)):
input_coroutines.append(self._async_get_candle_history(pair, timeframe))
else:
logger.debug(
"Using cached ohlcv data for pair %s, interval %s ...",
pair, ticker_interval
"Using cached ohlcv data for pair %s, timeframe %s ...",
pair, timeframe
)
tickers = asyncio.get_event_loop().run_until_complete(
@@ -608,40 +608,40 @@ class Exchange:
logger.warning("Async code raised an exception: %s", res.__class__.__name__)
continue
pair = res[0]
ticker_interval = res[1]
timeframe = res[1]
ticks = res[2]
# keeping last candle time as last refreshed time of the pair
if ticks:
self._pairs_last_refresh_time[(pair, ticker_interval)] = ticks[-1][0] // 1000
self._pairs_last_refresh_time[(pair, timeframe)] = ticks[-1][0] // 1000
# keeping parsed dataframe in cache
self._klines[(pair, ticker_interval)] = parse_ticker_dataframe(
ticks, ticker_interval, pair=pair, fill_missing=True,
self._klines[(pair, timeframe)] = parse_ticker_dataframe(
ticks, timeframe, pair=pair, fill_missing=True,
drop_incomplete=self._ohlcv_partial_candle)
return tickers
def _now_is_time_to_refresh(self, pair: str, ticker_interval: str) -> bool:
def _now_is_time_to_refresh(self, pair: str, timeframe: str) -> bool:
# Calculating ticker interval in seconds
interval_in_sec = timeframe_to_seconds(ticker_interval)
interval_in_sec = timeframe_to_seconds(timeframe)
return not ((self._pairs_last_refresh_time.get((pair, ticker_interval), 0)
return not ((self._pairs_last_refresh_time.get((pair, timeframe), 0)
+ interval_in_sec) >= arrow.utcnow().timestamp)
@retrier_async
async def _async_get_candle_history(self, pair: str, ticker_interval: str,
async def _async_get_candle_history(self, pair: str, timeframe: str,
since_ms: Optional[int] = None) -> Tuple[str, str, List]:
"""
Asynchronously gets candle histories using fetch_ohlcv
returns tuple: (pair, ticker_interval, ohlcv_list)
returns tuple: (pair, timeframe, ohlcv_list)
"""
try:
# fetch ohlcv asynchronously
s = '(' + arrow.get(since_ms // 1000).isoformat() + ') ' if since_ms is not None else ''
logger.debug(
"Fetching pair %s, interval %s, since %s %s...",
pair, ticker_interval, since_ms, s
pair, timeframe, since_ms, s
)
data = await self._api_async.fetch_ohlcv(pair, timeframe=ticker_interval,
data = await self._api_async.fetch_ohlcv(pair, timeframe=timeframe,
since=since_ms)
# Because some exchange sort Tickers ASC and other DESC.
@@ -653,9 +653,9 @@ class Exchange:
data = sorted(data, key=lambda x: x[0])
except IndexError:
logger.exception("Error loading %s. Result was %s.", pair, data)
return pair, ticker_interval, []
logger.debug("Done fetching pair %s, interval %s ...", pair, ticker_interval)
return pair, ticker_interval, data
return pair, timeframe, []
logger.debug("Done fetching pair %s, interval %s ...", pair, timeframe)
return pair, timeframe, data
except ccxt.NotSupported as e:
raise OperationalException(
@@ -802,7 +802,6 @@ class Exchange:
Handles all async doing.
Async over one pair, assuming we get `_ohlcv_candle_limit` candles per call.
:param pair: Pair to download
:param ticker_interval: Interval to get
:param since: Timestamp in milliseconds to get history from
:param until: Timestamp in milliseconds. Defaults to current timestamp if not defined.
:param from_id: Download data starting with ID (if id is known)
@@ -958,27 +957,27 @@ def available_exchanges(ccxt_module=None) -> List[str]:
return [x for x in exchanges if not is_exchange_bad(x)]
def timeframe_to_seconds(ticker_interval: str) -> int:
def timeframe_to_seconds(timeframe: str) -> int:
"""
Translates the timeframe interval value written in the human readable
form ('1m', '5m', '1h', '1d', '1w', etc.) to the number
of seconds for one timeframe interval.
"""
return ccxt.Exchange.parse_timeframe(ticker_interval)
return ccxt.Exchange.parse_timeframe(timeframe)
def timeframe_to_minutes(ticker_interval: str) -> int:
def timeframe_to_minutes(timeframe: str) -> int:
"""
Same as timeframe_to_seconds, but returns minutes.
"""
return ccxt.Exchange.parse_timeframe(ticker_interval) // 60
return ccxt.Exchange.parse_timeframe(timeframe) // 60
def timeframe_to_msecs(ticker_interval: str) -> int:
def timeframe_to_msecs(timeframe: str) -> int:
"""
Same as timeframe_to_seconds, but returns milliseconds.
"""
return ccxt.Exchange.parse_timeframe(ticker_interval) * 1000
return ccxt.Exchange.parse_timeframe(timeframe) * 1000
def timeframe_to_prev_date(timeframe: str, date: datetime = None) -> datetime:

12
freqtrade/optimize/backtesting.py
View File

@@ -83,8 +83,8 @@ class Backtesting:
if "ticker_interval" not in self.config:
raise OperationalException("Ticker-interval needs to be set in either configuration "
"or as cli argument `--ticker-interval 5m`")
self.ticker_interval = str(self.config.get('ticker_interval'))
self.ticker_interval_mins = timeframe_to_minutes(self.ticker_interval)
self.timeframe = str(self.config.get('ticker_interval'))
self.timeframe_mins = timeframe_to_minutes(self.timeframe)
# Get maximum required startup period
self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
@@ -108,7 +108,7 @@ class Backtesting:
data = history.load_data(
datadir=Path(self.config['datadir']),
pairs=self.config['exchange']['pair_whitelist'],
ticker_interval=self.ticker_interval,
timeframe=self.timeframe,
timerange=timerange,
startup_candles=self.required_startup,
fail_without_data=True,
@@ -121,7 +121,7 @@ class Backtesting:
min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
)
# Adjust startts forward if not enough data is available
timerange.adjust_start_if_necessary(timeframe_to_seconds(self.ticker_interval),
timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
self.required_startup, min_date)
return data, timerange
@@ -375,7 +375,7 @@ class Backtesting:
lock_pair_until: Dict = {}
# Indexes per pair, so some pairs are allowed to have a missing start.
indexes: Dict = {}
tmp = start_date + timedelta(minutes=self.ticker_interval_mins)
tmp = start_date + timedelta(minutes=self.timeframe_mins)
# Loop timerange and get candle for each pair at that point in time
while tmp < end_date:
@@ -427,7 +427,7 @@ class Backtesting:
lock_pair_until[pair] = end_date.datetime
# Move time one configured time_interval ahead.
tmp += timedelta(minutes=self.ticker_interval_mins)
tmp += timedelta(minutes=self.timeframe_mins)
return DataFrame.from_records(trades, columns=BacktestResult._fields)
def start(self) -> None:

8
freqtrade/optimize/hyperopt_interface.py
View File

@@ -106,10 +106,10 @@ class IHyperOpt(ABC):
roi_t_alpha = 1.0
roi_p_alpha = 1.0
ticker_interval_mins = timeframe_to_minutes(IHyperOpt.ticker_interval)
timeframe_mins = timeframe_to_minutes(IHyperOpt.ticker_interval)
# We define here limits for the ROI space parameters automagically adapted to the
# ticker_interval used by the bot:
# timeframe used by the bot:
#
# * 'roi_t' (limits for the time intervals in the ROI tables) components
# are scaled linearly.
@@ -117,8 +117,8 @@ class IHyperOpt(ABC):
#
# The scaling is designed so that it maps exactly to the legacy Freqtrade roi_space()
# method for the 5m ticker interval.
roi_t_scale = ticker_interval_mins / 5
roi_p_scale = math.log1p(ticker_interval_mins) / math.log1p(5)
roi_t_scale = timeframe_mins / 5
roi_p_scale = math.log1p(timeframe_mins) / math.log1p(5)
roi_limits = {
'roi_t1_min': int(10 * roi_t_scale * roi_t_alpha),
'roi_t1_max': int(120 * roi_t_scale * roi_t_alpha),

13
freqtrade/plot/plotting.py
View File

@@ -39,7 +39,7 @@ def init_plotscript(config):
tickers = history.load_data(
datadir=Path(str(config.get("datadir"))),
pairs=pairs,
ticker_interval=config.get('ticker_interval', '5m'),
timeframe=config.get('ticker_interval', '5m'),
timerange=timerange,
)
@@ -300,12 +300,12 @@ def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
return fig
def generate_plot_filename(pair, ticker_interval) -> str:
def generate_plot_filename(pair, timeframe) -> str:
"""
Generate filenames per pair/ticker_interval to be used for storing plots
Generate filenames per pair/timeframe to be used for storing plots
"""
pair_name = pair.replace("/", "_")
file_name = 'freqtrade-plot-' + pair_name + '-' + ticker_interval + '.html'
file_name = 'freqtrade-plot-' + pair_name + '-' + timeframe + '.html'
logger.info('Generate plot file for %s', pair)
@@ -316,8 +316,9 @@ def store_plot_file(fig, filename: str, directory: Path, auto_open: bool = False
"""
Generate a plot html file from pre populated fig plotly object
:param fig: Plotly Figure to plot
:param pair: Pair to plot (used as filename and Plot title)
:param ticker_interval: Used as part of the filename
:param filename: Name to store the file as
:param directory: Directory to store the file in
:param auto_open: Automatically open files saved
:return: None
"""
directory.mkdir(parents=True, exist_ok=True)