Merge pull request #2522 from freqtrade/replace_tickerinterval

Replace tickerinterval

docs/strategy-customization.md

@@ -314,9 +314,9 @@ Please always check the mode of operation to select the correct method to get da
 #### Possible options for DataProvider
 
 - `available_pairs` - Property with tuples listing cached pairs with their intervals (pair, interval).
-- `ohlcv(pair, ticker_interval)` - Currently cached ticker data for the pair, returns DataFrame or empty DataFrame.
-- `historic_ohlcv(pair, ticker_interval)` - Returns historical data stored on disk.
-- `get_pair_dataframe(pair, ticker_interval)` - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
+- `ohlcv(pair, timeframe)` - Currently cached ticker data for the pair, returns DataFrame or empty DataFrame.
+- `historic_ohlcv(pair, timeframe)` - Returns historical data stored on disk.
+- `get_pair_dataframe(pair, timeframe)` - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
 - `orderbook(pair, maximum)` - Returns latest orderbook data for the pair, a dict with bids/asks with a total of `maximum` entries.
 - `market(pair)` - Returns market data for the pair: fees, limits, precisions, activity flag, etc. See [ccxt documentation](https://github.com/ccxt/ccxt/wiki/Manual#markets) for more details on Market data structure.
 - `runmode` - Property containing the current runmode.
@@ -327,7 +327,7 @@ Please always check the mode of operation to select the correct method to get da
 if self.dp:
     inf_pair, inf_timeframe = self.informative_pairs()[0]
     informative = self.dp.get_pair_dataframe(pair=inf_pair,
-                                             ticker_interval=inf_timeframe)
+                                             timeframe=inf_timeframe)
 ```
 
 !!! Warning "Warning about backtesting"

docs/strategy_analysis_example.md

@@ -10,7 +10,7 @@ from pathlib import Path
 # Customize these according to your needs.
 
 # Define some constants
-ticker_interval = "5m"
+timeframe = "5m"
 # Name of the strategy class
 strategy_name = 'SampleStrategy'
 # Path to user data
@@ -29,7 +29,7 @@ pair = "BTC_USDT"
 from freqtrade.data.history import load_pair_history
 
 candles = load_pair_history(datadir=data_location,
-                            ticker_interval=ticker_interval,
+                            timeframe=timeframe,
                             pair=pair)
 
 # Confirm success

freqtrade/configuration/timerange.py

@@ -39,12 +39,12 @@ def subtract_start(self, seconds) -> None:
         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 @@ def adjust_start_if_necessary(self, ticker_interval_secs: int, startup_candles:
             # 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

freqtrade/constants.py

@@ -24,7 +24,7 @@
 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 @@
     '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"],

freqtrade/data/btanalysis.py

@@ -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

freqtrade/data/converter.py

@@ -10,13 +10,13 @@
 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)
 

freqtrade/data/dataprovider.py

@@ -37,52 +37,53 @@ def refresh(self,
     @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]]: