Merge branch 'develop' of github.com:lolongcovas/freqtrade into strategies

freqtrade/__init__.py

@@ -1,19 +1,20 @@
 """ Freqtrade bot """
-__version__ = '2022.12.dev'
+__version__ = '2023.1.dev'
 
 if 'dev' in __version__:
+    from pathlib import Path
     try:
         import subprocess
+        freqtrade_basedir = Path(__file__).parent
 
         __version__ = __version__ + '-' + subprocess.check_output(
             ['git', 'log', '--format="%h"', '-n 1'],
-            stderr=subprocess.DEVNULL).decode("utf-8").rstrip().strip('"')
+            stderr=subprocess.DEVNULL, cwd=freqtrade_basedir).decode("utf-8").rstrip().strip('"')
 
     except Exception:  # pragma: no cover
         # git not available, ignore
         try:
             # Try Fallback to freqtrade_commit file (created by CI while building docker image)
-            from pathlib import Path
             versionfile = Path('./freqtrade_commit')
             if versionfile.is_file():
                 __version__ = f"docker-{__version__}-{versionfile.read_text()[:8]}"

freqtrade/configuration/config_validation.py

@@ -355,6 +355,13 @@ def _validate_freqai_include_timeframes(conf: Dict[str, Any]) -> None:
                 f"Main timeframe of {main_tf} must be smaller or equal to FreqAI "
                 f"`include_timeframes`.Offending include-timeframes: {', '.join(offending_lines)}")
 
+        # Ensure that the base timeframe is included in the include_timeframes list
+        if main_tf not in freqai_include_timeframes:
+            feature_parameters = conf.get('freqai', {}).get('feature_parameters', {})
+            include_timeframes = [main_tf] + freqai_include_timeframes
+            conf.get('freqai', {}).get('feature_parameters', {}) \
+                .update({**feature_parameters, 'include_timeframes': include_timeframes})
+
 
 def _validate_freqai_backtest(conf: Dict[str, Any]) -> None:
     if conf.get('runmode', RunMode.OTHER) == RunMode.BACKTEST:

freqtrade/constants.py

@@ -31,7 +31,7 @@ HYPEROPT_LOSS_BUILTIN = ['ShortTradeDurHyperOptLoss', 'OnlyProfitHyperOptLoss',
                          'CalmarHyperOptLoss',
                          'MaxDrawDownHyperOptLoss', 'MaxDrawDownRelativeHyperOptLoss',
                          'ProfitDrawDownHyperOptLoss']
-AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'ProducerPairList',
+AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'ProducerPairList', 'RemotePairList',
                        'AgeFilter', 'OffsetFilter', 'PerformanceFilter',
                        'PrecisionFilter', 'PriceFilter', 'RangeStabilityFilter',
                        'ShuffleFilter', 'SpreadFilter', 'VolatilityFilter']
@@ -61,6 +61,7 @@ USERPATH_FREQAIMODELS = 'freqaimodels'
 
 TELEGRAM_SETTING_OPTIONS = ['on', 'off', 'silent']
 WEBHOOK_FORMAT_OPTIONS = ['form', 'json', 'raw']
+FULL_DATAFRAME_THRESHOLD = 100
 
 ENV_VAR_PREFIX = 'FREQTRADE__'
 
@@ -608,9 +609,8 @@ CONF_SCHEMA = {
                 "backtest_period_days",
                 "identifier",
                 "feature_parameters",
-                "data_split_parameters",
-                "model_training_parameters"
-                ]
+                "data_split_parameters"
+            ]
         },
     },
 }

freqtrade/data/btanalysis.py

@@ -20,8 +20,8 @@ from freqtrade.persistence import LocalTrade, Trade, init_db
 logger = logging.getLogger(__name__)
 
 # Newest format
-BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
-                   'open_rate', 'close_rate',
+BT_DATA_COLUMNS = ['pair', 'stake_amount', 'max_stake_amount', 'amount',
+                   'open_date', 'close_date', 'open_rate', 'close_rate',
                    'fee_open', 'fee_close', 'trade_duration',
                    'profit_ratio', 'profit_abs', 'exit_reason',
                    'initial_stop_loss_abs', 'initial_stop_loss_ratio', 'stop_loss_abs',
@@ -241,6 +241,33 @@ def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, s
     return results
 
 
+def _load_backtest_data_df_compatibility(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Compatibility support for older backtest data.
+    """
+    df['open_date'] = pd.to_datetime(df['open_date'],
+                                     utc=True,
+                                     infer_datetime_format=True
+                                     )
+    df['close_date'] = pd.to_datetime(df['close_date'],
+                                      utc=True,
+                                      infer_datetime_format=True
+                                      )
+    # Compatibility support for pre short Columns
+    if 'is_short' not in df.columns:
+        df['is_short'] = False
+    if 'leverage' not in df.columns:
+        df['leverage'] = 1.0
+    if 'enter_tag' not in df.columns:
+        df['enter_tag'] = df['buy_tag']
+        df = df.drop(['buy_tag'], axis=1)
+    if 'max_stake_amount' not in df.columns:
+        df['max_stake_amount'] = df['stake_amount']
+    if 'orders' not in df.columns:
+        df['orders'] = None
+    return df
+
+
 def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = None) -> pd.DataFrame:
     """
     Load backtest data file.
@@ -269,24 +296,7 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
         data = data['strategy'][strategy]['trades']
         df = pd.DataFrame(data)
         if not df.empty:
-            df['open_date'] = pd.to_datetime(df['open_date'],
-                                             utc=True,
-                                             infer_datetime_format=True
-                                             )
-            df['close_date'] = pd.to_datetime(df['close_date'],
-                                              utc=True,
-                                              infer_datetime_format=True
-                                              )
-            # Compatibility support for pre short Columns
-            if 'is_short' not in df.columns:
-                df['is_short'] = 0
-            if 'leverage' not in df.columns:
-                df['leverage'] = 1.0
-            if 'enter_tag' not in df.columns:
-                df['enter_tag'] = df['buy_tag']
-                df = df.drop(['buy_tag'], axis=1)
-            if 'orders' not in df.columns:
-                df['orders'] = None
+            df = _load_backtest_data_df_compatibility(df)
 
     else:
         # old format - only with lists.

freqtrade/data/dataprovider.py

@@ -9,14 +9,16 @@ from collections import deque
 from datetime import datetime, timezone
 from typing import Any, Dict, List, Optional, Tuple
 
-from pandas import DataFrame
+from pandas import DataFrame, to_timedelta
 
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import Config, ListPairsWithTimeframes, PairWithTimeframe
+from freqtrade.constants import (FULL_DATAFRAME_THRESHOLD, Config, ListPairsWithTimeframes,
+                                 PairWithTimeframe)
 from freqtrade.data.history import load_pair_history
 from freqtrade.enums import CandleType, RPCMessageType, RunMode
 from freqtrade.exceptions import ExchangeError, OperationalException
 from freqtrade.exchange import Exchange, timeframe_to_seconds
+from freqtrade.misc import append_candles_to_dataframe
 from freqtrade.rpc import RPCManager
 from freqtrade.util import PeriodicCache
 
@@ -120,7 +122,7 @@ class DataProvider:
                     'type': RPCMessageType.ANALYZED_DF,
                     'data': {
                         'key': pair_key,
-                        'df': dataframe,
+                        'df': dataframe.tail(1),
                         'la': datetime.now(timezone.utc)
                     }
                 }
@@ -131,7 +133,7 @@ class DataProvider:
                         'data': pair_key,
                     })
 
-    def _add_external_df(
+    def _replace_external_df(
         self,
         pair: str,
         dataframe: DataFrame,
@@ -157,6 +159,85 @@ class DataProvider:
         self.__producer_pairs_df[producer_name][pair_key] = (dataframe, _last_analyzed)
         logger.debug(f"External DataFrame for {pair_key} from {producer_name} added.")
 
+    def _add_external_df(
+        self,
+        pair: str,
+        dataframe: DataFrame,
+        last_analyzed: datetime,
+        timeframe: str,
+        candle_type: CandleType,
+        producer_name: str = "default"
+    ) -> Tuple[bool, int]:
+        """
+        Append a candle to the existing external dataframe. The incoming dataframe
+        must have at least 1 candle.
+
+        :param pair: pair to get the data for
+        :param timeframe: Timeframe to get data for
+        :param candle_type: Any of the enum CandleType (must match trading mode!)
+        :returns: False if the candle could not be appended, or the int number of missing candles.
+        """
+        pair_key = (pair, timeframe, candle_type)
+
+        if dataframe.empty:
+            # The incoming dataframe must have at least 1 candle
+            return (False, 0)
+
+        if len(dataframe) >= FULL_DATAFRAME_THRESHOLD:
+            # This is likely a full dataframe
+            # Add the dataframe to the dataprovider
+            self._replace_external_df(
+                pair,
+                dataframe,
+                last_analyzed=last_analyzed,
+                timeframe=timeframe,
+                candle_type=candle_type,
+                producer_name=producer_name
+            )
+            return (True, 0)
+
+        if (producer_name not in self.__producer_pairs_df
+           or pair_key not in self.__producer_pairs_df[producer_name]):
+            # We don't have data from this producer yet,
+            # or we don't have data for this pair_key
+            # return False and 1000 for the full df
+            return (False, 1000)
+
+        existing_df, _ = self.__producer_pairs_df[producer_name][pair_key]
+
+        # CHECK FOR MISSING CANDLES
+        timeframe_delta = to_timedelta(timeframe)  # Convert the timeframe to a timedelta for pandas
+        local_last = existing_df.iloc[-1]['date']  # We want the last date from our copy
+        incoming_first = dataframe.iloc[0]['date']  # We want the first date from the incoming
+
+        # Remove existing candles that are newer than the incoming first candle
+        existing_df1 = existing_df[existing_df['date'] < incoming_first]
+
+        candle_difference = (incoming_first - local_last) / timeframe_delta
+
+        # If the difference divided by the timeframe is 1, then this
+        # is the candle we want and the incoming data isn't missing any.
+        # If the candle_difference is more than 1, that means
+        # we missed some candles between our data and the incoming
+        # so return False and candle_difference.
+        if candle_difference > 1:
+            return (False, candle_difference)
+        if existing_df1.empty:
+            appended_df = dataframe
+        else:
+            appended_df = append_candles_to_dataframe(existing_df1, dataframe)
+
+        # Everything is good, we appended
+        self._replace_external_df(
+                    pair,
+                    appended_df,
+                    last_analyzed=last_analyzed,
+                    timeframe=timeframe,
+                    candle_type=candle_type,
+                    producer_name=producer_name
+                    )
+        return (True, 0)
+
     def get_producer_df(
         self,
         pair: str,

freqtrade/data/entryexitanalysis.py

@@ -52,7 +52,7 @@ def _process_candles_and_indicators(pairlist, strategy_name, trades, signal_cand
     return analysed_trades_dict
 
 
-def _analyze_candles_and_indicators(pair, trades, signal_candles):
+def _analyze_candles_and_indicators(pair, trades: pd.DataFrame, signal_candles: pd.DataFrame):
     buyf = signal_candles
 
     if len(buyf) > 0:
@@ -120,7 +120,7 @@ def _do_group_table_output(bigdf, glist):
 
         else:
             agg_mask = {'profit_abs': ['count', 'sum', 'median', 'mean'],
-                        'profit_ratio': ['sum', 'median', 'mean']}
+                        'profit_ratio': ['median', 'mean', 'sum']}
             agg_cols = ['num_buys', 'profit_abs_sum', 'profit_abs_median',
                         'profit_abs_mean', 'median_profit_pct', 'mean_profit_pct',
                         'total_profit_pct']

freqtrade/data/metrics.py

@@ -1,4 +1,6 @@
 import logging
+import math
+from datetime import datetime
 from typing import Dict, Tuple
 
 import numpy as np
@@ -190,3 +192,119 @@ def calculate_cagr(days_passed: int, starting_balance: float, final_balance: flo
     :return: CAGR
     """
     return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1
+
+
+def calculate_expectancy(trades: pd.DataFrame) -> float:
+    """
+    Calculate expectancy
+    :param trades: DataFrame containing trades (requires columns close_date and profit_abs)
+    :return: expectancy
+    """
+    if len(trades) == 0:
+        return 0
+
+    expectancy = 1
+
+    profit_sum = trades.loc[trades['profit_abs'] > 0, 'profit_abs'].sum()
+    loss_sum = abs(trades.loc[trades['profit_abs'] < 0, 'profit_abs'].sum())
+    nb_win_trades = len(trades.loc[trades['profit_abs'] > 0])
+    nb_loss_trades = len(trades.loc[trades['profit_abs'] < 0])
+
+    if (nb_win_trades > 0) and (nb_loss_trades > 0):
+        average_win = profit_sum / nb_win_trades
+        average_loss = loss_sum / nb_loss_trades
+        risk_reward_ratio = average_win / average_loss
+        winrate = nb_win_trades / len(trades)
+        expectancy = ((1 + risk_reward_ratio) * winrate) - 1
+    elif nb_win_trades == 0:
+        expectancy = 0
+
+    return expectancy
+
+
+def calculate_sortino(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
+                      starting_balance: float) -> float:
+    """
+    Calculate sortino
+    :param trades: DataFrame containing trades (requires columns profit_abs)
+    :return: sortino
+    """
+    if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
+        return 0
+
+    total_profit = trades['profit_abs'] / starting_balance
+    days_period = max(1, (max_date - min_date).days)
+
+    expected_returns_mean = total_profit.sum() / days_period
+
+    down_stdev = np.std(trades.loc[trades['profit_abs'] < 0, 'profit_abs'] / starting_balance)
+
+    if down_stdev != 0 and not np.isnan(down_stdev):
+        sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
+    else:
+        # Define high (negative) sortino ratio to be clear that this is NOT optimal.
+        sortino_ratio = -100
+
+    # print(expected_returns_mean, down_stdev, sortino_ratio)
+    return sortino_ratio
+
+
+def calculate_sharpe(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
+                     starting_balance: float) -> float:
+    """
+    Calculate sharpe
+    :param trades: DataFrame containing trades (requires column profit_abs)
+    :return: sharpe
+    """
+    if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
+        return 0
+
+    total_profit = trades['profit_abs'] / starting_balance
+    days_period = max(1, (max_date - min_date).days)
+
+    expected_returns_mean = total_profit.sum() / days_period
+    up_stdev = np.std(total_profit)
+
+    if up_stdev != 0:
+        sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
+    else:
+        # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
+        sharp_ratio = -100
+
+    # print(expected_returns_mean, up_stdev, sharp_ratio)
+    return sharp_ratio
+
+
+def calculate_calmar(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
+                     starting_balance: float) -> float:
+    """
+    Calculate calmar
+    :param trades: DataFrame containing trades (requires columns close_date and profit_abs)
+    :return: calmar
+    """
+    if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
+        return 0
+
+    total_profit = trades['profit_abs'].sum() / starting_balance
+    days_period = max(1, (max_date - min_date).days)
+
+    # adding slippage of 0.1% per trade
+    # total_profit = total_profit - 0.0005
+    expected_returns_mean = total_profit / days_period * 100
+
+    # calculate max drawdown
+    try:
+        _, _, _, _, _, max_drawdown = calculate_max_drawdown(
+            trades, value_col="profit_abs", starting_balance=starting_balance
+        )
+    except ValueError:
+        max_drawdown = 0
+
+    if max_drawdown != 0:
+        calmar_ratio = expected_returns_mean / max_drawdown * math.sqrt(365)
+    else:
+        # Define high (negative) calmar ratio to be clear that this is NOT optimal.
+        calmar_ratio = -100
+
+    # print(expected_returns_mean, max_drawdown, calmar_ratio)
+    return calmar_ratio

freqtrade/exchange/__init__.py

@@ -3,7 +3,6 @@
 from freqtrade.exchange.common import remove_credentials, MAP_EXCHANGE_CHILDCLASS
 from freqtrade.exchange.exchange import Exchange
 # isort: on
-from freqtrade.exchange.bibox import Bibox
 from freqtrade.exchange.binance import Binance
 from freqtrade.exchange.bitpanda import Bitpanda
 from freqtrade.exchange.bittrex import Bittrex

freqtrade/exchange/bibox.py

@@ -1,28 +0,0 @@
-""" Bibox exchange subclass """
-import logging
-from typing import Dict
-
-from freqtrade.exchange import Exchange
-
-
-logger = logging.getLogger(__name__)
-
-
-class Bibox(Exchange):
-    """
-    Bibox exchange class. Contains adjustments needed for Freqtrade to work
-    with this exchange.
-
-    Please note that this exchange is not included in the list of exchanges
-    officially supported by the Freqtrade development team. So some features
-    may still not work as expected.
-    """
-
-    # fetchCurrencies API point requires authentication for Bibox,
-    # so switch it off for Freqtrade load_markets()
-    @property
-    def _ccxt_config(self) -> Dict:
-        # Parameters to add directly to ccxt sync/async initialization.
-        config = {"has": {"fetchCurrencies": False}}
-        config.update(super()._ccxt_config)
-        return config

freqtrade/exchange/binance.py

@@ -11,7 +11,7 @@ from freqtrade.enums import CandleType, MarginMode, TradingMode
 from freqtrade.exceptions import DDosProtection, OperationalException, TemporaryError
 from freqtrade.exchange import Exchange
 from freqtrade.exchange.common import retrier
-from freqtrade.exchange.types import Tickers
+from freqtrade.exchange.types import OHLCVResponse, Tickers
 from freqtrade.misc import deep_merge_dicts, json_load
 
 
@@ -31,7 +31,7 @@ class Binance(Exchange):
         "ccxt_futures_name": "future"
     }
     _ft_has_futures: Dict = {
-        "stoploss_order_types": {"limit": "limit", "market": "market"},
+        "stoploss_order_types": {"limit": "stop", "market": "stop_market"},
         "tickers_have_price": False,
     }
 
@@ -112,7 +112,7 @@ class Binance(Exchange):
                                         since_ms: int, candle_type: CandleType,
                                         is_new_pair: bool = False, raise_: bool = False,
                                         until_ms: Optional[int] = None
-                                        ) -> Tuple[str, str, str, List]:
+                                        ) -> OHLCVResponse:
         """
         Overwrite to introduce "fast new pair" functionality by detecting the pair's listing date
         Does not work for other exchanges, which don't return the earliest data when called with "0"

freqtrade/exchange/exchange.py

@@ -36,7 +36,7 @@ from freqtrade.exchange.exchange_utils import (CcxtModuleType, amount_to_contrac
                                                price_to_precision, timeframe_to_minutes,
                                                timeframe_to_msecs, timeframe_to_next_date,
                                                timeframe_to_prev_date, timeframe_to_seconds)
-from freqtrade.exchange.types import Ticker, Tickers
+from freqtrade.exchange.types import OHLCVResponse, Ticker, Tickers
 from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_json,
                             safe_value_fallback2)
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
@@ -474,7 +474,7 @@ class Exchange:
         try:
             if self._api_async:
                 self.loop.run_until_complete(
-                    self._api_async.load_markets(reload=reload))
+                    self._api_async.load_markets(reload=reload, params={}))
 
         except (asyncio.TimeoutError, ccxt.BaseError) as e:
             logger.warning('Could not load async markets. Reason: %s', e)
@@ -483,7 +483,7 @@ class Exchange:
     def _load_markets(self) -> None:
         """ Initialize markets both sync and async """
         try:
-            self._markets = self._api.load_markets()
+            self._markets = self._api.load_markets(params={})
             self._load_async_markets()
             self._last_markets_refresh = arrow.utcnow().int_timestamp
             if self._ft_has['needs_trading_fees']:
@@ -501,7 +501,7 @@ class Exchange:
             return None
         logger.debug("Performing scheduled market reload..")
         try:
-            self._markets = self._api.load_markets(reload=True)
+            self._markets = self._api.load_markets(reload=True, params={})
             # Also reload async markets to avoid issues with newly listed pairs
             self._load_async_markets(reload=True)
             self._last_markets_refresh = arrow.utcnow().int_timestamp
@@ -1705,7 +1705,7 @@ class Exchange:
                 return self._config['fee']
             # validate that markets are loaded before trying to get fee
             if self._api.markets is None or len(self._api.markets) == 0:
-                self._api.load_markets()
+                self._api.load_markets(params={})
 
             return self._api.calculate_fee(symbol=symbol, type=type, side=side, amount=amount,
                                            price=price, takerOrMaker=taker_or_maker)['rate']
@@ -1813,32 +1813,18 @@ class Exchange:
         :param candle_type: '', mark, index, premiumIndex, or funding_rate
         :return: List with candle (OHLCV) data
         """
-        pair, _, _, data = self.loop.run_until_complete(
+        pair, _, _, data, _ = self.loop.run_until_complete(
             self._async_get_historic_ohlcv(pair=pair, timeframe=timeframe,
                                            since_ms=since_ms, until_ms=until_ms,
                                            is_new_pair=is_new_pair, candle_type=candle_type))
         logger.info(f"Downloaded data for {pair} with length {len(data)}.")
         return data
 
-    def get_historic_ohlcv_as_df(self, pair: str, timeframe: str,
-                                 since_ms: int, candle_type: CandleType) -> DataFrame:
-        """
-        Minimal wrapper around get_historic_ohlcv - converting the result into a dataframe
-        :param pair: Pair to download
-        :param timeframe: Timeframe to get data for
-        :param since_ms: Timestamp in milliseconds to get history from
-        :param candle_type: Any of the enum CandleType (must match trading mode!)
-        :return: OHLCV DataFrame
-        """
-        ticks = self.get_historic_ohlcv(pair, timeframe, since_ms=since_ms, candle_type=candle_type)
-        return ohlcv_to_dataframe(ticks, timeframe, pair=pair, fill_missing=True,
-                                  drop_incomplete=self._ohlcv_partial_candle)
-
     async def _async_get_historic_ohlcv(self, pair: str, timeframe: str,
                                         since_ms: int, candle_type: CandleType,
                                         is_new_pair: bool = False, raise_: bool = False,
                                         until_ms: Optional[int] = None
-                                        ) -> Tuple[str, str, str, List]:
+                                        ) -> OHLCVResponse:
         """
         Download historic ohlcv
         :param is_new_pair: used by binance subclass to allow "fast" new pair downloading
@@ -1869,15 +1855,16 @@ class Exchange:
                     continue
                 else:
                     # Deconstruct tuple if it's not an exception
-                    p, _, c, new_data = res
+                    p, _, c, new_data, _ = res
                     if p == pair and c == candle_type:
                         data.extend(new_data)
         # Sort data again after extending the result - above calls return in "async order"
         data = sorted(data, key=lambda x: x[0])
-        return pair, timeframe, candle_type, data
+        return pair, timeframe, candle_type, data, self._ohlcv_partial_candle
 
-    def _build_coroutine(self, pair: str, timeframe: str, candle_type: CandleType,
-                         since_ms: Optional[int], cache: bool) -> Coroutine:
+    def _build_coroutine(
+            self, pair: str, timeframe: str, candle_type: CandleType,
+            since_ms: Optional[int], cache: bool) -> Coroutine[Any, Any, OHLCVResponse]:
         not_all_data = cache and self.required_candle_call_count > 1
         if cache and (pair, timeframe, candle_type) in self._klines:
             candle_limit = self.ohlcv_candle_limit(timeframe, candle_type)
@@ -1914,7 +1901,7 @@ class Exchange:
         """
         Build Coroutines to execute as part of refresh_latest_ohlcv
         """
-        input_coroutines = []
+        input_coroutines: List[Coroutine[Any, Any, OHLCVResponse]] = []
         cached_pairs = []
         for pair, timeframe, candle_type in set(pair_list):
             if (timeframe not in self.timeframes
@@ -1978,7 +1965,6 @@ class Exchange:
         :return: Dict of [{(pair, timeframe): Dataframe}]
         """
         logger.debug("Refreshing candle (OHLCV) data for %d pairs", len(pair_list))
-        drop_incomplete = self._ohlcv_partial_candle if drop_incomplete is None else drop_incomplete
 
         # Gather coroutines to run
         input_coroutines, cached_pairs = self._build_ohlcv_dl_jobs(pair_list, since_ms, cache)
@@ -1996,8 +1982,9 @@ class Exchange:
                 if isinstance(res, Exception):
                     logger.warning(f"Async code raised an exception: {repr(res)}")
                     continue
-                # Deconstruct tuple (has 4 elements)
-                pair, timeframe, c_type, ticks = res
+                # Deconstruct tuple (has 5 elements)
+                pair, timeframe, c_type, ticks, drop_hint = res
+                drop_incomplete = drop_hint if drop_incomplete is None else drop_incomplete
                 ohlcv_df = self._process_ohlcv_df(
                     pair, timeframe, c_type, ticks, cache, drop_incomplete)
 
@@ -2025,7 +2012,7 @@ class Exchange:
         timeframe: str,
         candle_type: CandleType,
         since_ms: Optional[int] = None,
-    ) -> Tuple[str, str, str, List]:
+    ) -> OHLCVResponse:
         """
         Asynchronously get candle history data using fetch_ohlcv
         :param candle_type: '', mark, index, premiumIndex, or funding_rate
@@ -2035,8 +2022,8 @@ class Exchange:
             # 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, timeframe, since_ms, s
+                "Fetching pair %s, %s, interval %s, since %s %s...",
+                pair, candle_type, timeframe, since_ms, s
             )
             params = deepcopy(self._ft_has.get('ohlcv_params', {}))
             candle_limit = self.ohlcv_candle_limit(
@@ -2050,11 +2037,12 @@ class Exchange:
                     limit=candle_limit, params=params)
             else:
                 # Funding rate
-                data = await self._api_async.fetch_funding_rate_history(
-                    pair, since=since_ms,
-                    limit=candle_limit)
-                # Convert funding rate to candle pattern
-                data = [[x['timestamp'], x['fundingRate'], 0, 0, 0, 0] for x in data]
+                data = await self._fetch_funding_rate_history(
+                    pair=pair,
+                    timeframe=timeframe,
+                    limit=candle_limit,
+                    since_ms=since_ms,
+                )
             # Some exchanges sort OHLCV in ASC order and others in DESC.
             # Ex: Bittrex returns the list of OHLCV in ASC order (oldest first, newest last)
             # while GDAX returns the list of OHLCV in DESC order (newest first, oldest last)
@@ -2064,9 +2052,9 @@ class Exchange:
                     data = sorted(data, key=lambda x: x[0])
             except IndexError:
                 logger.exception("Error loading %s. Result was %s.", pair, data)
-                return pair, timeframe, candle_type, []
+                return pair, timeframe, candle_type, [], self._ohlcv_partial_candle
             logger.debug("Done fetching pair %s, interval %s ...", pair, timeframe)
-            return pair, timeframe, candle_type, data
+            return pair, timeframe, candle_type, data, self._ohlcv_partial_candle
 
         except ccxt.NotSupported as e:
             raise OperationalException(
@@ -2082,6 +2070,24 @@ class Exchange:
             raise OperationalException(f'Could not fetch historical candle (OHLCV) data '
                                        f'for pair {pair}. Message: {e}') from e
 
+    async def _fetch_funding_rate_history(
+        self,
+        pair: str,
+        timeframe: str,
+        limit: int,
+        since_ms: Optional[int] = None,
+    ) -> List[List]:
+        """
+        Fetch funding rate history - used to selectively override this by subclasses.
+        """
+        # Funding rate
+        data = await self._api_async.fetch_funding_rate_history(
+            pair, since=since_ms,
+            limit=limit)
+        # Convert funding rate to candle pattern
+        data = [[x['timestamp'], x['fundingRate'], 0, 0, 0, 0] for x in data]
+        return data
+
     # Fetch historic trades
 
     @retrier_async
@@ -2745,11 +2751,16 @@ class Exchange:
         """
         Important: Must be fetching data from cached values as this is used by backtesting!
         PERPETUAL:
-         gateio: https://www.gate.io/help/futures/perpetual/22160/calculation-of-liquidation-price
+         gateio: https://www.gate.io/help/futures/futures/27724/liquidation-price-bankruptcy-price
+         > Liquidation Price = (Entry Price ± Margin / Contract Multiplier / Size) /
+                                [ 1 ± (Maintenance Margin Ratio + Taker Rate)]
+            Wherein, "+" or "-" depends on whether the contract goes long or short:
+            "-" for long, and "+" for short.
+
          okex: https://www.okex.com/support/hc/en-us/articles/
             360053909592-VI-Introduction-to-the-isolated-mode-of-Single-Multi-currency-Portfolio-margin
 
-        :param exchange_name:
+        :param pair: Pair to calculate liquidation price for
         :param open_rate: Entry price of position
         :param is_short: True if the trade is a short, false otherwise
         :param amount: Absolute value of position size incl. leverage (in base currency)
@@ -2789,7 +2800,7 @@ class Exchange:
     def get_maintenance_ratio_and_amt(
         self,
         pair: str,
-        nominal_value: float = 0.0,
+        nominal_value: float,
     ) -> Tuple[float, Optional[float]]:
         """
         Important: Must be fetching data from cached values as this is used by backtesting!

freqtrade/exchange/types.py

@@ -1,4 +1,6 @@
-from typing import Dict, Optional, TypedDict
+from typing import Dict, List, Optional, Tuple, TypedDict
+
+from freqtrade.enums import CandleType
 
 
 class Ticker(TypedDict):
@@ -14,3 +16,6 @@ class Ticker(TypedDict):
 
 
 Tickers = Dict[str, Ticker]
+
+# pair, timeframe, candleType, OHLCV, drop last?,
+OHLCVResponse = Tuple[str, str, CandleType, List, bool]

freqtrade/freqai/RL/Base3ActionRLEnv.py

@@ -0,0 +1,125 @@
+import logging
+from enum import Enum
+
+from gym import spaces
+
+from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, Positions
+
+
+logger = logging.getLogger(__name__)
+
+
+class Actions(Enum):
+    Neutral = 0
+    Buy = 1
+    Sell = 2
+
+
+class Base3ActionRLEnv(BaseEnvironment):
+    """
+    Base class for a 3 action environment
+    """
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.actions = Actions
+
+    def set_action_space(self):
+        self.action_space = spaces.Discrete(len(Actions))
+
+    def step(self, action: int):
+        """
+        Logic for a single step (incrementing one candle in time)
+        by the agent
+        :param: action: int = the action type that the agent plans
+            to take for the current step.
+        :returns:
+            observation = current state of environment
+            step_reward = the reward from `calculate_reward()`
+            _done = if the agent "died" or if the candles finished
+            info = dict passed back to openai gym lib
+        """
+        self._done = False
+        self._current_tick += 1
+
+        if self._current_tick == self._end_tick:
+            self._done = True
+
+        self._update_unrealized_total_profit()
+        step_reward = self.calculate_reward(action)
+        self.total_reward += step_reward
+        self.tensorboard_log(self.actions._member_names_[action])
+
+        trade_type = None
+        if self.is_tradesignal(action):
+            if action == Actions.Buy.value:
+                if self._position == Positions.Short:
+                    self._update_total_profit()
+                self._position = Positions.Long
+                trade_type = "long"
+                self._last_trade_tick = self._current_tick
+            elif action == Actions.Sell.value and self.can_short:
+                if self._position == Positions.Long:
+                    self._update_total_profit()
+                self._position = Positions.Short
+                trade_type = "short"
+                self._last_trade_tick = self._current_tick
+            elif action == Actions.Sell.value and not self.can_short:
+                self._update_total_profit()
+                self._position = Positions.Neutral
+                trade_type = "neutral"
+                self._last_trade_tick = None
+            else:
+                print("case not defined")
+
+            if trade_type is not None:
+                self.trade_history.append(
+                    {'price': self.current_price(), 'index': self._current_tick,
+                     'type': trade_type})
+
+        if (self._total_profit < self.max_drawdown or
+                self._total_unrealized_profit < self.max_drawdown):
+            self._done = True
+
+        self._position_history.append(self._position)
+
+        info = dict(
+            tick=self._current_tick,
+            action=action,
+            total_reward=self.total_reward,
+            total_profit=self._total_profit,
+            position=self._position.value,
+            trade_duration=self.get_trade_duration(),
+            current_profit_pct=self.get_unrealized_profit()
+        )
+
+        observation = self._get_observation()
+
+        self._update_history(info)
+
+        return observation, step_reward, self._done, info
+
+    def is_tradesignal(self, action: int) -> bool:
+        """
+        Determine if the signal is a trade signal
+        e.g.: agent wants a Actions.Buy while it is in a Positions.short
+        """
+        return (
+            (action == Actions.Buy.value and self._position == Positions.Neutral)
+            or (action == Actions.Sell.value and self._position == Positions.Long)
+            or (action == Actions.Sell.value and self._position == Positions.Neutral
+                and self.can_short)
+            or (action == Actions.Buy.value and self._position == Positions.Short
+                and self.can_short)
+        )
+
+    def _is_valid(self, action: int) -> bool:
+        """
+        Determine if the signal is valid.
+        e.g.: agent wants a Actions.Sell while it is in a Positions.Long
+        """
+        if self.can_short:
+            return action in [Actions.Buy.value, Actions.Sell.value, Actions.Neutral.value]
+        else:
+            if action == Actions.Sell.value and self._position != Positions.Long:
+                return False
+            return True

freqtrade/freqai/RL/Base4ActionRLEnv.py

@@ -20,6 +20,9 @@ class Base4ActionRLEnv(BaseEnvironment):
     """
     Base class for a 4 action environment
     """
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.actions = Actions
 
     def set_action_space(self):
         self.action_space = spaces.Discrete(len(Actions))
@@ -43,9 +46,9 @@ class Base4ActionRLEnv(BaseEnvironment):
             self._done = True
 
         self._update_unrealized_total_profit()
-
         step_reward = self.calculate_reward(action)
         self.total_reward += step_reward
+        self.tensorboard_log(self.actions._member_names_[action])
 
         trade_type = None
         if self.is_tradesignal(action):
@@ -85,16 +88,20 @@ class Base4ActionRLEnv(BaseEnvironment):
                     {'price': self.current_price(), 'index': self._current_tick,
                      'type': trade_type})
 
-        if self._total_profit < 1 - self.rl_config.get('max_training_drawdown_pct', 0.8):
+        if (self._total_profit < self.max_drawdown or
+                self._total_unrealized_profit < self.max_drawdown):
             self._done = True
 
         self._position_history.append(self._position)
 
         info = dict(
             tick=self._current_tick,
+            action=action,
             total_reward=self.total_reward,
             total_profit=self._total_profit,
-            position=self._position.value
+            position=self._position.value,
+            trade_duration=self.get_trade_duration(),
+            current_profit_pct=self.get_unrealized_profit()
         )
 
         observation = self._get_observation()

freqtrade/freqai/RL/Base5ActionRLEnv.py

@@ -21,6 +21,9 @@ class Base5ActionRLEnv(BaseEnvironment):
     """
     Base class for a 5 action environment
     """
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.actions = Actions
 
     def set_action_space(self):
         self.action_space = spaces.Discrete(len(Actions))
@@ -46,6 +49,7 @@ class Base5ActionRLEnv(BaseEnvironment):
         self._update_unrealized_total_profit()
         step_reward = self.calculate_reward(action)
         self.total_reward += step_reward
+        self.tensorboard_log(self.actions._member_names_[action])
 
         trade_type = None
         if self.is_tradesignal(action):
@@ -98,9 +102,12 @@ class Base5ActionRLEnv(BaseEnvironment):
 
         info = dict(
             tick=self._current_tick,
+            action=action,
             total_reward=self.total_reward,
             total_profit=self._total_profit,
-            position=self._position.value
+            position=self._position.value,
+            trade_duration=self.get_trade_duration(),
+            current_profit_pct=self.get_unrealized_profit()
         )
 
         observation = self._get_observation()

freqtrade/freqai/RL/BaseEnvironment.py

@@ -2,7 +2,7 @@ import logging
 import random
 from abc import abstractmethod
 from enum import Enum
-from typing import Optional
+from typing import Optional, Type, Union
 
 import gym
 import numpy as np
@@ -11,12 +11,21 @@ from gym import spaces
 from gym.utils import seeding
 from pandas import DataFrame
 
-from freqtrade.data.dataprovider import DataProvider
-
 
 logger = logging.getLogger(__name__)
 
 
+class BaseActions(Enum):
+    """
+    Default action space, mostly used for type handling.
+    """
+    Neutral = 0
+    Long_enter = 1
+    Long_exit = 2
+    Short_enter = 3
+    Short_exit = 4
+
+
 class Positions(Enum):
     Short = 0
     Long = 1
@@ -35,8 +44,8 @@ class BaseEnvironment(gym.Env):
 
     def __init__(self, df: DataFrame = DataFrame(), prices: DataFrame = DataFrame(),
                  reward_kwargs: dict = {}, window_size=10, starting_point=True,
-                 id: str = 'baseenv-1', seed: int = 1, config: dict = {},
-                 dp: Optional[DataProvider] = None):
+                 id: str = 'baseenv-1', seed: int = 1, config: dict = {}, live: bool = False,
+                 fee: float = 0.0015, can_short: bool = False):
         """
         Initializes the training/eval environment.
         :param df: dataframe of features
@@ -47,22 +56,31 @@ class BaseEnvironment(gym.Env):
         :param id: string id of the environment (used in backend for multiprocessed env)
         :param seed: Sets the seed of the environment higher in the gym.Env object
         :param config: Typical user configuration file
-        :param dp: dataprovider from freqtrade
+        :param live: Whether or not this environment is active in dry/live/backtesting
+        :param fee: The fee to use for environmental interactions.
+        :param can_short: Whether or not the environment can short
         """
         self.config = config
         self.rl_config = config['freqai']['rl_config']
         self.add_state_info = self.rl_config.get('add_state_info', False)
         self.id = id
-        self.seed(seed)
-        self.reset_env(df, prices, window_size, reward_kwargs, starting_point)
         self.max_drawdown = 1 - self.rl_config.get('max_training_drawdown_pct', 0.8)
         self.compound_trades = config['stake_amount'] == 'unlimited'
         if self.config.get('fee', None) is not None:
             self.fee = self.config['fee']
-        elif dp is not None:
-            self.fee = dp._exchange.get_fee(symbol=dp.current_whitelist()[0])  # type: ignore
         else:
-            self.fee = 0.0015
+            self.fee = fee
+
+        # set here to default 5Ac, but all children envs can override this
+        self.actions: Type[Enum] = BaseActions
+        self.tensorboard_metrics: dict = {}
+        self.can_short = can_short
+        self.live = live
+        if not self.live and self.add_state_info:
+            self.add_state_info = False
+            logger.warning("add_state_info is not available in backtesting. Deactivating.")
+        self.seed(seed)
+        self.reset_env(df, prices, window_size, reward_kwargs, starting_point)
 
     def reset_env(self, df: DataFrame, prices: DataFrame, window_size: int,
                   reward_kwargs: dict, starting_point=True):
@@ -117,7 +135,38 @@ class BaseEnvironment(gym.Env):
         self.np_random, seed = seeding.np_random(seed)
         return [seed]
 
+    def tensorboard_log(self, metric: str, value: Union[int, float] = 1, inc: bool = True):
+        """
+        Function builds the tensorboard_metrics dictionary
+        to be parsed by the TensorboardCallback. This
+        function is designed for tracking incremented objects,
+        events, actions inside the training environment.
+        For example, a user can call this to track the
+        frequency of occurence of an `is_valid` call in
+        their `calculate_reward()`:
+
+        def calculate_reward(self, action: int) -> float:
+            if not self._is_valid(action):
+                self.tensorboard_log("is_valid")
+                return -2
+
+        :param metric: metric to be tracked and incremented
+        :param value: value to increment `metric` by
+        :param inc: sets whether the `value` is incremented or not
+        """
+        if not inc or metric not in self.tensorboard_metrics:
+            self.tensorboard_metrics[metric] = value
+        else:
+            self.tensorboard_metrics[metric] += value
+
+    def reset_tensorboard_log(self):
+        self.tensorboard_metrics = {}
+
     def reset(self):
+        """
+        Reset is called at the beginning of every episode
+        """
+        self.reset_tensorboard_log()
 
         self._done = False
 
@@ -271,6 +320,13 @@ class BaseEnvironment(gym.Env):
     def current_price(self) -> float:
         return self.prices.iloc[self._current_tick].open
 
+    def get_actions(self) -> Type[Enum]:
+        """
+        Used by SubprocVecEnv to get actions from
+        initialized env for tensorboard callback
+        """
+        return self.actions
+
     # Keeping around incase we want to start building more complex environment
     # templates in the future.
     # def most_recent_return(self):

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -21,7 +21,8 @@ from freqtrade.exceptions import OperationalException
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.freqai_interface import IFreqaiModel
 from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv
-from freqtrade.freqai.RL.BaseEnvironment import Positions
+from freqtrade.freqai.RL.BaseEnvironment import BaseActions, Positions
+from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
 from freqtrade.persistence import Trade
 
 
@@ -44,8 +45,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
             'cpu_count', 1), max(int(self.max_system_threads / 2), 1))
         th.set_num_threads(self.max_threads)
         self.reward_params = self.freqai_info['rl_config']['model_reward_parameters']
-        self.train_env: Union[SubprocVecEnv, gym.Env] = None
-        self.eval_env: Union[SubprocVecEnv, gym.Env] = None
+        self.train_env: Union[SubprocVecEnv, Type[gym.Env]] = gym.Env()
+        self.eval_env: Union[SubprocVecEnv, Type[gym.Env]] = gym.Env()
         self.eval_callback: Optional[EvalCallback] = None
         self.model_type = self.freqai_info['rl_config']['model_type']
         self.rl_config = self.freqai_info['rl_config']
@@ -65,6 +66,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         self.unset_outlier_removal()
         self.net_arch = self.rl_config.get('net_arch', [128, 128])
         self.dd.model_type = import_str
+        self.tensorboard_callback: TensorboardCallback = \
+            TensorboardCallback(verbose=1, actions=BaseActions)
 
     def unset_outlier_removal(self):
         """
@@ -140,22 +143,36 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         train_df = data_dictionary["train_features"]
         test_df = data_dictionary["test_features"]
 
+        env_info = self.pack_env_dict()
+
         self.train_env = self.MyRLEnv(df=train_df,
                                       prices=prices_train,
-                                      window_size=self.CONV_WIDTH,
-                                      reward_kwargs=self.reward_params,
-                                      config=self.config,
-                                      dp=self.data_provider)
+                                      **env_info)
         self.eval_env = Monitor(self.MyRLEnv(df=test_df,
                                              prices=prices_test,
-                                             window_size=self.CONV_WIDTH,
-                                             reward_kwargs=self.reward_params,
-                                             config=self.config,
-                                             dp=self.data_provider))
+                                             **env_info))
         self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
                                           render=False, eval_freq=len(train_df),
                                           best_model_save_path=str(dk.data_path))
 
+        actions = self.train_env.get_actions()
+        self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)
+
+    def pack_env_dict(self) -> Dict[str, Any]:
+        """
+        Create dictionary of environment arguments
+        """
+        env_info = {"window_size": self.CONV_WIDTH,
+                    "reward_kwargs": self.reward_params,
+                    "config": self.config,
+                    "live": self.live,
+                    "can_short": self.can_short}
+        if self.data_provider:
+            env_info["fee"] = self.data_provider._exchange \
+                .get_fee(symbol=self.data_provider.current_whitelist()[0])  # type: ignore
+
+        return env_info
+
     @abstractmethod
     def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
         """
@@ -263,26 +280,36 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         train_df = data_dictionary["train_features"]
         test_df = data_dictionary["test_features"]
 
+        # %-raw_volume_gen_shift-2_ETH/USDT_1h
         # price data for model training and evaluation
         tf = self.config['timeframe']
-        ohlc_list = [f'%-{pair}raw_open_{tf}', f'%-{pair}raw_low_{tf}',
-                     f'%-{pair}raw_high_{tf}', f'%-{pair}raw_close_{tf}']
-        rename_dict = {f'%-{pair}raw_open_{tf}': 'open', f'%-{pair}raw_low_{tf}': 'low',
-                       f'%-{pair}raw_high_{tf}': ' high', f'%-{pair}raw_close_{tf}': 'close'}
+        rename_dict = {'%-raw_open': 'open', '%-raw_low': 'low',
+                       '%-raw_high': ' high', '%-raw_close': 'close'}
+        rename_dict_old = {f'%-{pair}raw_open_{tf}': 'open', f'%-{pair}raw_low_{tf}': 'low',
+                           f'%-{pair}raw_high_{tf}': ' high', f'%-{pair}raw_close_{tf}': 'close'}
+
+        prices_train = train_df.filter(rename_dict.keys(), axis=1)
+        prices_train_old = train_df.filter(rename_dict_old.keys(), axis=1)
+        if prices_train.empty or not prices_train_old.empty:
+            if not prices_train_old.empty:
+                prices_train = prices_train_old
+                rename_dict = rename_dict_old
+            logger.warning('Reinforcement learning module didnt find the correct raw prices '
+                           'assigned in feature_engineering_standard(). '
+                           'Please assign them with:\n'
+                           'dataframe["%-raw_close"] = dataframe["close"]\n'
+                           'dataframe["%-raw_open"] = dataframe["open"]\n'
+                           'dataframe["%-raw_high"] = dataframe["high"]\n'
+                           'dataframe["%-raw_low"] = dataframe["low"]\n'
+                           'inside `feature_engineering_standard()')
+        elif prices_train.empty:
+            raise OperationalException("No prices found, please follow log warning "
+                                       "instructions to correct the strategy.")
 
-        prices_train = train_df.filter(ohlc_list, axis=1)
-        if prices_train.empty:
-            raise OperationalException('Reinforcement learning module didnt find the raw prices '
-                                       'assigned in populate_any_indicators. Please assign them '
-                                       'with:\n'
-                                       'informative[f"%-{pair}raw_close"] = informative["close"]\n'
-                                       'informative[f"%-{pair}raw_open"] = informative["open"]\n'
-                                       'informative[f"%-{pair}raw_high"] = informative["high"]\n'
-                                       'informative[f"%-{pair}raw_low"] = informative["low"]\n')
         prices_train.rename(columns=rename_dict, inplace=True)
         prices_train.reset_index(drop=True)
 
-        prices_test = test_df.filter(ohlc_list, axis=1)
+        prices_test = test_df.filter(rename_dict.keys(), axis=1)
         prices_test.rename(columns=rename_dict, inplace=True)
         prices_test.reset_index(drop=True)
 
@@ -377,8 +404,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
 
 def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
              seed: int, train_df: DataFrame, price: DataFrame,
-             reward_params: Dict[str, int], window_size: int, monitor: bool = False,
-             config: Dict[str, Any] = {}) -> Callable:
+             monitor: bool = False,
+             env_info: Dict[str, Any] = {}) -> Callable:
     """
     Utility function for multiprocessed env.
 
@@ -386,13 +413,14 @@ def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
     :param num_env: (int) the number of environment you wish to have in subprocesses
     :param seed: (int) the inital seed for RNG
     :param rank: (int) index of the subprocess
+    :param env_info: (dict) all required arguments to instantiate the environment.
     :return: (Callable)
     """
 
     def _init() -> gym.Env:
 
-        env = MyRLEnv(df=train_df, prices=price, window_size=window_size,
-                      reward_kwargs=reward_params, id=env_id, seed=seed + rank, config=config)
+        env = MyRLEnv(df=train_df, prices=price, id=env_id, seed=seed + rank,
+                      **env_info)
         if monitor:
             env = Monitor(env)
         return env

freqtrade/freqai/RL/TensorboardCallback.py

@@ -0,0 +1,59 @@
+from enum import Enum
+from typing import Any, Dict, Type, Union
+
+from stable_baselines3.common.callbacks import BaseCallback
+from stable_baselines3.common.logger import HParam
+
+from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment
+
+
+class TensorboardCallback(BaseCallback):
+    """
+    Custom callback for plotting additional values in tensorboard and
+    episodic summary reports.
+    """
+    def __init__(self, verbose=1, actions: Type[Enum] = BaseActions):
+        super(TensorboardCallback, self).__init__(verbose)
+        self.model: Any = None
+        self.logger = None  # type: Any
+        self.training_env: BaseEnvironment = None  # type: ignore
+        self.actions: Type[Enum] = actions
+
+    def _on_training_start(self) -> None:
+        hparam_dict = {
+            "algorithm": self.model.__class__.__name__,
+            "learning_rate": self.model.learning_rate,
+            # "gamma": self.model.gamma,
+            # "gae_lambda": self.model.gae_lambda,
+            # "batch_size": self.model.batch_size,
+            # "n_steps": self.model.n_steps,
+        }
+        metric_dict: Dict[str, Union[float, int]] = {
+            "eval/mean_reward": 0,
+            "rollout/ep_rew_mean": 0,
+            "rollout/ep_len_mean": 0,
+            "train/value_loss": 0,
+            "train/explained_variance": 0,
+        }
+        self.logger.record(
+            "hparams",
+            HParam(hparam_dict, metric_dict),
+            exclude=("stdout", "log", "json", "csv"),
+        )
+
+    def _on_step(self) -> bool:
+
+        local_info = self.locals["infos"][0]
+        tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
+
+        for info in local_info:
+            if info not in ["episode", "terminal_observation"]:
+                self.logger.record(f"_info/{info}", local_info[info])
+
+        for info in tensorboard_metrics:
+            if info in [action.name for action in self.actions]:
+                self.logger.record(f"_actions/{info}", tensorboard_metrics[info])
+            else:
+                self.logger.record(f"_custom/{info}", tensorboard_metrics[info])
+
+        return True

freqtrade/freqai/base_models/BaseClassifierModel.py

@@ -95,9 +95,14 @@ class BaseClassifierModel(IFreqaiModel):
         self.data_cleaning_predict(dk)
 
         predictions = self.model.predict(dk.data_dictionary["prediction_features"])
+        if self.CONV_WIDTH == 1:
+            predictions = np.reshape(predictions, (-1, len(dk.label_list)))
+
         pred_df = DataFrame(predictions, columns=dk.label_list)
 
         predictions_prob = self.model.predict_proba(dk.data_dictionary["prediction_features"])
+        if self.CONV_WIDTH == 1:
+            predictions_prob = np.reshape(predictions_prob, (-1, len(self.model.classes_)))
         pred_df_prob = DataFrame(predictions_prob, columns=self.model.classes_)
 
         pred_df = pd.concat([pred_df, pred_df_prob], axis=1)

freqtrade/freqai/base_models/BaseRegressionModel.py

@@ -95,6 +95,9 @@ class BaseRegressionModel(IFreqaiModel):
         self.data_cleaning_predict(dk)
 
         predictions = self.model.predict(dk.data_dictionary["prediction_features"])
+        if self.CONV_WIDTH == 1:
+            predictions = np.reshape(predictions, (-1, len(dk.label_list)))
+
         pred_df = DataFrame(predictions, columns=dk.label_list)
 
         pred_df = dk.denormalize_labels_from_metadata(pred_df)

freqtrade/freqai/data_kitchen.py

@@ -1,4 +1,5 @@
 import copy
+import inspect
 import logging
 import shutil
 from datetime import datetime, timezone
@@ -23,6 +24,7 @@ from freqtrade.constants import Config
 from freqtrade.data.converter import reduce_dataframe_footprint
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_seconds
+from freqtrade.strategy import merge_informative_pair
 from freqtrade.strategy.interface import IStrategy
 
 
@@ -1159,9 +1161,9 @@ class FreqaiDataKitchen:
 
         for pair in pairs:
             pair = pair.replace(':', '')  # lightgbm doesnt like colons
-            valid_strs = [f"%-{pair}", f"%{pair}", f"%_{pair}"]
-            pair_cols = [col for col in dataframe.columns if
-                         any(substr in col for substr in valid_strs)]
+            pair_cols = [col for col in dataframe.columns if col.startswith("%")
+                         and f"{pair}_" in col]
+
             if pair_cols:
                 pair_cols.insert(0, 'date')
                 corr_dataframes[pair] = dataframe.filter(pair_cols, axis=1)
@@ -1190,6 +1192,103 @@ class FreqaiDataKitchen:
 
         return dataframe
 
+    def get_pair_data_for_features(self,
+                                   pair: str,
+                                   tf: str,
+                                   strategy: IStrategy,
+                                   corr_dataframes: dict = {},
+                                   base_dataframes: dict = {},
+                                   is_corr_pairs: bool = False) -> DataFrame:
+        """
+        Get the data for the pair. If it's not in the dictionary, get it from the data provider
+        :param pair: str = pair to get data for
+        :param tf: str = timeframe to get data for
+        :param strategy: IStrategy = user defined strategy object
+        :param corr_dataframes: dict = dict containing the df pair dataframes
+                                (for user defined timeframes)
+        :param base_dataframes: dict = dict containing the current pair dataframes
+                                (for user defined timeframes)
+        :param is_corr_pairs: bool = whether the pair is a corr pair or not
+        :return: dataframe = dataframe containing the pair data
+        """
+        if is_corr_pairs:
+            dataframe = corr_dataframes[pair][tf]
+            if not dataframe.empty:
+                return dataframe
+            else:
+                dataframe = strategy.dp.get_pair_dataframe(pair=pair, timeframe=tf)
+                return dataframe
+        else:
+            dataframe = base_dataframes[tf]
+            if not dataframe.empty:
+                return dataframe
+            else:
+                dataframe = strategy.dp.get_pair_dataframe(pair=pair, timeframe=tf)
+                return dataframe
+
+    def merge_features(self, df_main: DataFrame, df_to_merge: DataFrame,
+                       tf: str, timeframe_inf: str, suffix: str) -> DataFrame:
+        """
+        Merge the features of the dataframe and remove HLCV and date added columns
+        :param df_main: DataFrame = main dataframe
+        :param df_to_merge: DataFrame = dataframe to merge
+        :param tf: str = timeframe of the main dataframe
+        :param timeframe_inf: str = timeframe of the dataframe to merge
+        :param suffix: str = suffix to add to the columns of the dataframe to merge
+        :return: dataframe = merged dataframe
+        """
+        dataframe = merge_informative_pair(df_main, df_to_merge, tf, timeframe_inf=timeframe_inf,
+                                           append_timeframe=False, suffix=suffix, ffill=True)
+        skip_columns = [
+            (f"{s}_{suffix}") for s in ["date", "open", "high", "low", "close", "volume"]
+        ]
+        dataframe = dataframe.drop(columns=skip_columns)
+        return dataframe
+
+    def populate_features(self, dataframe: DataFrame, pair: str, strategy: IStrategy,
+                          corr_dataframes: dict, base_dataframes: dict,
+                          is_corr_pairs: bool = False) -> DataFrame:
+        """
+        Use the user defined strategy functions for populating features
+        :param dataframe: DataFrame = dataframe to populate
+        :param pair: str = pair to populate
+        :param strategy: IStrategy = user defined strategy object
+        :param corr_dataframes: dict = dict containing the df pair dataframes
+        :param base_dataframes: dict = dict containing the current pair dataframes
+        :param is_corr_pairs: bool = whether the pair is a corr pair or not
+        :return: dataframe = populated dataframe
+        """
+        tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
+
+        for tf in tfs:
+            informative_df = self.get_pair_data_for_features(
+                pair, tf, strategy, corr_dataframes, base_dataframes, is_corr_pairs)
+            informative_copy = informative_df.copy()
+
+            for t in self.freqai_config["feature_parameters"]["indicator_periods_candles"]:
+                df_features = strategy.feature_engineering_expand_all(
+                    informative_copy.copy(), t)
+                suffix = f"{t}"
+                informative_df = self.merge_features(informative_df, df_features, tf, tf, suffix)
+
+            generic_df = strategy.feature_engineering_expand_basic(informative_copy.copy())
+            suffix = "gen"
+
+            informative_df = self.merge_features(informative_df, generic_df, tf, tf, suffix)
+
+            indicators = [col for col in informative_df if col.startswith("%")]
+            for n in range(self.freqai_config["feature_parameters"]["include_shifted_candles"] + 1):
+                if n == 0:
+                    continue
+                df_shift = informative_df[indicators].shift(n)
+                df_shift = df_shift.add_suffix("_shift-" + str(n))
+                informative_df = pd.concat((informative_df, df_shift), axis=1)
+
+            dataframe = self.merge_features(dataframe.copy(), informative_df,
+                                            self.config["timeframe"], tf, f'{pair}_{tf}')
+
+        return dataframe
+
     def use_strategy_to_populate_indicators(
         self,
         strategy: IStrategy,
@@ -1202,7 +1301,87 @@ class FreqaiDataKitchen:
         """
         Use the user defined strategy for populating indicators during retrain
         :param strategy: IStrategy = user defined strategy object
-        :param corr_dataframes: dict = dict containing the informative pair dataframes
+        :param corr_dataframes: dict = dict containing the df pair dataframes
+                                (for user defined timeframes)
+        :param base_dataframes: dict = dict containing the current pair dataframes
+                                (for user defined timeframes)
+        :param pair: str = pair to populate
+        :param prediction_dataframe: DataFrame = dataframe containing the pair data
+        used for prediction
+        :param do_corr_pairs: bool = whether to populate corr pairs or not
+        :return:
+        dataframe: DataFrame = dataframe containing populated indicators
+        """
+
+        # this is a hack to check if the user is using the populate_any_indicators function
+        new_version = inspect.getsource(strategy.populate_any_indicators) == (
+            inspect.getsource(IStrategy.populate_any_indicators))
+
+        if new_version:
+            tfs: List[str] = self.freqai_config["feature_parameters"].get("include_timeframes")
+            pairs: List[str] = self.freqai_config["feature_parameters"].get(
+                "include_corr_pairlist", [])
+
+            for tf in tfs:
+                if tf not in base_dataframes:
+                    base_dataframes[tf] = pd.DataFrame()
+                for p in pairs:
+                    if p not in corr_dataframes:
+                        corr_dataframes[p] = {}
+                    if tf not in corr_dataframes[p]:
+                        corr_dataframes[p][tf] = pd.DataFrame()
+
+            if not prediction_dataframe.empty:
+                dataframe = prediction_dataframe.copy()
+            else:
+                dataframe = base_dataframes[self.config["timeframe"]].copy()
+
+            corr_pairs: List[str] = self.freqai_config["feature_parameters"].get(
+                "include_corr_pairlist", [])
+            dataframe = self.populate_features(dataframe.copy(), pair, strategy,
+                                               corr_dataframes, base_dataframes)
+
+            dataframe = strategy.feature_engineering_standard(dataframe.copy())
+            # ensure corr pairs are always last
+            for corr_pair in corr_pairs:
+                if pair == corr_pair:
+                    continue  # dont repeat anything from whitelist
+                if corr_pairs and do_corr_pairs:
+                    dataframe = self.populate_features(dataframe.copy(), corr_pair, strategy,
+                                                       corr_dataframes, base_dataframes, True)
+
+            dataframe = strategy.set_freqai_targets(dataframe.copy())
+
+            self.get_unique_classes_from_labels(dataframe)
+
+            dataframe = self.remove_special_chars_from_feature_names(dataframe)
+
+            if self.config.get('reduce_df_footprint', False):
+                dataframe = reduce_dataframe_footprint(dataframe)
+
+            return dataframe
+
+        else:
+            # the user is using the populate_any_indicators functions which is deprecated
+
+            df = self.use_strategy_to_populate_indicators_old_version(
+                strategy, corr_dataframes, base_dataframes, pair,
+                prediction_dataframe, do_corr_pairs)
+            return df
+
+    def use_strategy_to_populate_indicators_old_version(
+        self,
+        strategy: IStrategy,
+        corr_dataframes: dict = {},
+        base_dataframes: dict = {},
+        pair: str = "",
+        prediction_dataframe: DataFrame = pd.DataFrame(),
+        do_corr_pairs: bool = True,
+    ) -> DataFrame:
+        """
+        Use the user defined strategy for populating indicators during retrain
+        :param strategy: IStrategy = user defined strategy object
+        :param corr_dataframes: dict = dict containing the df pair dataframes
                                 (for user defined timeframes)
         :param base_dataframes: dict = dict containing the current pair dataframes
                                 (for user defined timeframes)

freqtrade/freqai/freqai_interface.py

@@ -1,3 +1,4 @@
+import inspect
 import logging
 import threading
 import time
@@ -104,6 +105,9 @@ class IFreqaiModel(ABC):
         self.metadata: Dict[str, Any] = self.dd.load_global_metadata_from_disk()
         self.data_provider: Optional[DataProvider] = None
         self.max_system_threads = max(int(psutil.cpu_count() * 2 - 2), 1)
+        self.can_short = True  # overridden in start() with strategy.can_short
+
+        self.warned_deprecated_populate_any_indicators = False
 
         record_params(config, self.full_path)
 
@@ -133,6 +137,10 @@ class IFreqaiModel(ABC):
         self.live = strategy.dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
         self.dd.set_pair_dict_info(metadata)
         self.data_provider = strategy.dp
+        self.can_short = strategy.can_short
+
+        # check if the strategy has deprecated populate_any_indicators function
+        self.check_deprecated_populate_any_indicators(strategy)
 
         if self.live:
             self.inference_timer('start')
@@ -147,12 +155,9 @@ class IFreqaiModel(ABC):
         # the concatenated results for the full backtesting period back to the strategy.
         elif not self.follow_mode:
             self.dk = FreqaiDataKitchen(self.config, self.live, metadata["pair"])
-            dataframe = self.dk.use_strategy_to_populate_indicators(
-                strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
-            )
             if not self.config.get("freqai_backtest_live_models", False):
                 logger.info(f"Training {len(self.dk.training_timeranges)} timeranges")
-                dk = self.start_backtesting(dataframe, metadata, self.dk)
+                dk = self.start_backtesting(dataframe, metadata, self.dk, strategy)
                 dataframe = dk.remove_features_from_df(dk.return_dataframe)
             else:
                 logger.info(
@@ -253,7 +258,7 @@ class IFreqaiModel(ABC):
                     self.dd.save_metric_tracker_to_disk()
 
     def start_backtesting(
-        self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen
+        self, dataframe: DataFrame, metadata: dict, dk: FreqaiDataKitchen, strategy: IStrategy
     ) -> FreqaiDataKitchen:
         """
         The main broad execution for backtesting. For backtesting, each pair enters and then gets
@@ -265,19 +270,22 @@ class IFreqaiModel(ABC):
         :param dataframe: DataFrame = strategy passed dataframe
         :param metadata: Dict = pair metadata
         :param dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
+        :param strategy: Strategy to train on
         :return:
             FreqaiDataKitchen = Data management/analysis tool associated to present pair only
         """
 
         self.pair_it += 1
         train_it = 0
+        pair = metadata["pair"]
+        populate_indicators = True
+        check_features = True
         # Loop enforcing the sliding window training/backtesting paradigm
         # tr_train is the training time range e.g. 1 historical month
         # tr_backtest is the backtesting time range e.g. the week directly
         # following tr_train. Both of these windows slide through the
         # entire backtest
         for tr_train, tr_backtest in zip(dk.training_timeranges, dk.backtesting_timeranges):
-            pair = metadata["pair"]
             (_, _, _) = self.dd.get_pair_dict_info(pair)
             train_it += 1
             total_trains = len(dk.backtesting_timeranges)
@@ -299,18 +307,42 @@ class IFreqaiModel(ABC):
             dk.set_new_model_names(pair, timestamp_model_id)
 
             if dk.check_if_backtest_prediction_is_valid(len_backtest_df):
-                self.dd.load_metadata(dk)
-                dk.find_features(dataframe)
-                self.check_if_feature_list_matches_strategy(dk)
+                if check_features:
+                    self.dd.load_metadata(dk)
+                    dataframe_dummy_features = self.dk.use_strategy_to_populate_indicators(
+                        strategy, prediction_dataframe=dataframe.tail(1), pair=metadata["pair"]
+                    )
+                    dk.find_features(dataframe_dummy_features)
+                    self.check_if_feature_list_matches_strategy(dk)
+                    check_features = False
                 append_df = dk.get_backtesting_prediction()
                 dk.append_predictions(append_df)
             else:
-                dataframe_train = dk.slice_dataframe(tr_train, dataframe)
-                dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
+                if populate_indicators:
+                    dataframe = self.dk.use_strategy_to_populate_indicators(
+                        strategy, prediction_dataframe=dataframe, pair=metadata["pair"]
+                    )
+                    populate_indicators = False
+
+                dataframe_base_train = dataframe.loc[dataframe["date"] < tr_train.stopdt, :]
+                dataframe_base_train = strategy.set_freqai_targets(dataframe_base_train)
+                dataframe_base_backtest = dataframe.loc[dataframe["date"] < tr_backtest.stopdt, :]
+                dataframe_base_backtest = strategy.set_freqai_targets(dataframe_base_backtest)
+
+                dataframe_train = dk.slice_dataframe(tr_train, dataframe_base_train)
+                dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe_base_backtest)
+
                 if not self.model_exists(dk):
                     dk.find_features(dataframe_train)
                     dk.find_labels(dataframe_train)
-                    self.model = self.train(dataframe_train, pair, dk)
+
+                    try:
+                        self.model = self.train(dataframe_train, pair, dk)
+                    except Exception as msg:
+                        logger.warning(
+                            f"Training {pair} raised exception {msg.__class__.__name__}. "
+                            f"Message: {msg}, skipping.")
+
                     self.dd.pair_dict[pair]["trained_timestamp"] = int(
                         tr_train.stopts)
                     if self.plot_features:
@@ -347,7 +379,6 @@ class IFreqaiModel(ABC):
         :returns:
         dk: FreqaiDataKitchen = Data management/analysis tool associated to present pair only
         """
-
         # update follower
         if self.follow_mode:
             self.dd.update_follower_metadata()
@@ -911,9 +942,28 @@ class IFreqaiModel(ABC):
         dk.return_dataframe = dk.return_dataframe.drop(columns=list(columns_to_drop))
         dk.return_dataframe = pd.merge(
             dk.return_dataframe, saved_dataframe, how='left', left_on='date', right_on="date_pred")
-        # dk.return_dataframe = dk.return_dataframe[saved_dataframe.columns].fillna(0)
         return dk
 
+    def check_deprecated_populate_any_indicators(self, strategy: IStrategy):
+        """
+        Check and warn if the deprecated populate_any_indicators function is used.
+        :param strategy: strategy object
+        """
+
+        if not self.warned_deprecated_populate_any_indicators:
+            self.warned_deprecated_populate_any_indicators = True
+            old_version = inspect.getsource(strategy.populate_any_indicators) != (
+                inspect.getsource(IStrategy.populate_any_indicators))
+
+            if old_version:
+                logger.warning("DEPRECATION WARNING: "
+                               "You are using the deprecated populate_any_indicators function. "
+                               "This function will raise an error on March 1 2023. "
+                               "Please update your strategy by using "
+                               "the new feature_engineering functions. See \n"
+                               "https://www.freqtrade.io/en/latest/freqai-feature-engineering/"
+                               "for details.")
+
     # Following methods which are overridden by user made prediction models.
     # See freqai/prediction_models/CatboostPredictionModel.py for an example.
 

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -61,7 +61,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
             model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
                                     tensorboard_log=Path(
                                         dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
-                                    **self.freqai_info['model_training_parameters']
+                                    **self.freqai_info.get('model_training_parameters', {})
                                     )
         else:
             logger.info('Continual training activated - starting training from previously '
@@ -71,7 +71,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
 
         model.learn(
             total_timesteps=int(total_timesteps),
-            callback=self.eval_callback
+            callback=[self.eval_callback, self.tensorboard_callback]
         )
 
         if Path(dk.data_path / "best_model.zip").is_file():
@@ -100,13 +100,17 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
             """
             # first, penalize if the action is not valid
             if not self._is_valid(action):
+                self.tensorboard_log("is_valid")
                 return -2
 
             pnl = self.get_unrealized_profit()
             factor = 100.
 
             # reward agent for entering trades
-            if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
+            if (action == Actions.Long_enter.value
+                    and self._position == Positions.Neutral):
+                return 25
+            if (action == Actions.Short_enter.value
                     and self._position == Positions.Neutral):
                 return 25
             # discourage agent from not entering trades

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

@@ -1,7 +1,6 @@
 import logging
-from typing import Any, Dict  # , Tuple
+from typing import Any, Dict
 
-# import numpy.typing as npt
 from pandas import DataFrame
 from stable_baselines3.common.callbacks import EvalCallback
 from stable_baselines3.common.vec_env import SubprocVecEnv
@@ -9,6 +8,7 @@ from stable_baselines3.common.vec_env import SubprocVecEnv
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
 from freqtrade.freqai.RL.BaseReinforcementLearningModel import make_env
+from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
 
 
 logger = logging.getLogger(__name__)
@@ -34,18 +34,24 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
         train_df = data_dictionary["train_features"]
         test_df = data_dictionary["test_features"]
 
+        env_info = self.pack_env_dict()
+
         env_id = "train_env"
-        self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1, train_df, prices_train,
-                                        self.reward_params, self.CONV_WIDTH, monitor=True,
-                                        config=self.config) for i
+        self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,
+                                        train_df, prices_train,
+                                        monitor=True,
+                                        env_info=env_info) for i
                                         in range(self.max_threads)])
 
         eval_env_id = 'eval_env'
         self.eval_env = SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
                                                 test_df, prices_test,
-                                                self.reward_params, self.CONV_WIDTH, monitor=True,
-                                                config=self.config) for i
+                                                monitor=True,
+                                                env_info=env_info) for i
                                        in range(self.max_threads)])
         self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
                                           render=False, eval_freq=len(train_df),
                                           best_model_save_path=str(dk.data_path))
+
+        actions = self.train_env.env_method("get_actions")[0]
+        self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)

freqtrade/freqtradebot.py

@@ -155,6 +155,8 @@ class FreqtradeBot(LoggingMixin):
                 self.cancel_all_open_orders()
 
             self.check_for_open_trades()
+        except Exception as e:
+            logger.warning(f'Exception during cleanup: {e.__class__.__name__} {e}')
 
         finally:
             self.strategy.ft_bot_cleanup()
@@ -162,8 +164,13 @@ class FreqtradeBot(LoggingMixin):
         self.rpc.cleanup()
         if self.emc:
             self.emc.shutdown()
-        Trade.commit()
         self.exchange.close()
+        try:
+            Trade.commit()
+        except Exception:
+            # Exeptions here will be happening if the db disappeared.
+            # At which point we can no longer commit anyway.
+            pass
 
     def startup(self) -> None:
         """
@@ -367,7 +374,7 @@ class FreqtradeBot(LoggingMixin):
         for trade in trades:
             if not trade.is_open and not trade.fee_updated(trade.exit_side):
                 # Get sell fee
-                order = trade.select_order(trade.exit_side, False)
+                order = trade.select_order(trade.exit_side, False, only_filled=True)
                 if not order:
                     order = trade.select_order('stoploss', False)
                 if order:
@@ -383,7 +390,7 @@ class FreqtradeBot(LoggingMixin):
         for trade in trades:
             with self._exit_lock:
                 if trade.is_open and not trade.fee_updated(trade.entry_side):
-                    order = trade.select_order(trade.entry_side, False)
+                    order = trade.select_order(trade.entry_side, False, only_filled=True)
                     open_order = trade.select_order(trade.entry_side, True)
                     if order and open_order is None:
                         logger.info(
@@ -713,7 +720,7 @@ class FreqtradeBot(LoggingMixin):
             time_in_force=time_in_force,
             leverage=leverage
         )
-        order_obj = Order.parse_from_ccxt_object(order, pair, side)
+        order_obj = Order.parse_from_ccxt_object(order, pair, side, amount, enter_limit_requested)
         order_id = order['id']
         order_status = order.get('status')
         logger.info(f"Order #{order_id} was created for {pair} and status is {order_status}.")
@@ -905,6 +912,7 @@ class FreqtradeBot(LoggingMixin):
             stake_amount=stake_amount,
             min_stake_amount=min_stake_amount,
             max_stake_amount=max_stake_amount,
+            trade_amount=trade.stake_amount if trade else None,
         )
 
         return enter_limit_requested, stake_amount, leverage
@@ -1086,7 +1094,8 @@ class FreqtradeBot(LoggingMixin):
                 leverage=trade.leverage
             )
 
-            order_obj = Order.parse_from_ccxt_object(stoploss_order, trade.pair, 'stoploss')
+            order_obj = Order.parse_from_ccxt_object(stoploss_order, trade.pair, 'stoploss',
+                                                     trade.amount, stop_price)
             trade.orders.append(order_obj)
             trade.stoploss_order_id = str(stoploss_order['id'])
             trade.stoploss_last_update = datetime.now(timezone.utc)
@@ -1587,7 +1596,7 @@ class FreqtradeBot(LoggingMixin):
             self.handle_insufficient_funds(trade)
             return False
 
-        order_obj = Order.parse_from_ccxt_object(order, trade.pair, trade.exit_side)
+        order_obj = Order.parse_from_ccxt_object(order, trade.pair, trade.exit_side, amount, limit)
         trade.orders.append(order_obj)
 
         trade.open_order_id = order['id']

freqtrade/misc.py

@@ -269,6 +269,8 @@ def dataframe_to_json(dataframe: pd.DataFrame) -> str:
     def default(z):
         if isinstance(z, pd.Timestamp):
             return z.timestamp() * 1e3
+        if z is pd.NaT:
+            return 'NaT'
         raise TypeError
 
     return str(orjson.dumps(dataframe.to_dict(orient='split'), default=default), 'utf-8')
@@ -301,3 +303,21 @@ def remove_entry_exit_signals(dataframe: pd.DataFrame):
     dataframe[SignalTagType.EXIT_TAG.value] = None
 
     return dataframe
+
+
+def append_candles_to_dataframe(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame:
+    """
+    Append the `right` dataframe to the `left` dataframe
+
+    :param left: The full dataframe you want appended to
+    :param right: The new dataframe containing the data you want appended
+    :returns: The dataframe with the right data in it
+    """
+    if left.iloc[-1]['date'] != right.iloc[-1]['date']:
+        left = pd.concat([left, right])
+
+    # Only keep the last 1500 candles in memory
+    left = left[-1500:] if len(left) > 1500 else left
+    left.reset_index(drop=True, inplace=True)
+
+    return left

freqtrade/optimize/backtesting.py

@@ -769,6 +769,7 @@ class Backtesting:
             stake_amount=stake_amount,
             min_stake_amount=min_stake_amount,
             max_stake_amount=max_stake_amount,
+            trade_amount=trade.stake_amount if trade else None
         )
 
         return propose_rate, stake_amount_val, leverage, min_stake_amount
@@ -1050,7 +1051,8 @@ class Backtesting:
 
     def backtest_loop(
             self, row: Tuple, pair: str, current_time: datetime, end_date: datetime,
-            max_open_trades: int, open_trade_count_start: int, is_first: bool = True) -> int:
+            max_open_trades: int, open_trade_count_start: int, trade_dir: Optional[LongShort],
+            is_first: bool = True) -> int:
         """
         NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
 
@@ -1069,7 +1071,6 @@ class Backtesting:
         # max_open_trades must be respected
         # don't open on the last row
         # We only open trades on the main candle, not on detail candles
-        trade_dir = self.check_for_trade_entry(row)
         if (
             (self._position_stacking or len(LocalTrade.bt_trades_open_pp[pair]) == 0)
             and is_first
@@ -1163,7 +1164,15 @@ class Backtesting:
                 indexes[pair] = row_index
                 self.dataprovider._set_dataframe_max_index(row_index)
                 current_detail_time: datetime = row[DATE_IDX].to_pydatetime()
-                if self.timeframe_detail and pair in self.detail_data:
+                trade_dir: Optional[LongShort] = self.check_for_trade_entry(row)
+
+                if (
+                    (trade_dir is not None or len(LocalTrade.bt_trades_open_pp[pair]) > 0)
+                    and self.timeframe_detail and pair in self.detail_data
+                ):
+                    # Spread out into detail timeframe.
+                    # Should only happen when we are either in a trade for this pair
+                    # or when we got the signal for a new trade.
                     exit_candle_end = current_detail_time + timedelta(minutes=self.timeframe_min)
 
                     detail_data = self.detail_data[pair]
@@ -1175,7 +1184,8 @@ class Backtesting:
                         # Fall back to "regular" data if no detail data was found for this candle
                         open_trade_count_start = self.backtest_loop(
                             row, pair, current_time, end_date, max_open_trades,
-                            open_trade_count_start)
+                            open_trade_count_start, trade_dir)
+                        continue
                     detail_data.loc[:, 'enter_long'] = row[LONG_IDX]
                     detail_data.loc[:, 'exit_long'] = row[ELONG_IDX]
                     detail_data.loc[:, 'enter_short'] = row[SHORT_IDX]
@@ -1187,12 +1197,13 @@ class Backtesting:
                     for det_row in detail_data[HEADERS].values.tolist():
                         open_trade_count_start = self.backtest_loop(
                             det_row, pair, current_time_det, end_date, max_open_trades,
-                            open_trade_count_start, is_first)
+                            open_trade_count_start, trade_dir, is_first)
                         current_time_det += timedelta(minutes=self.timeframe_detail_min)
                         is_first = False
                 else:
                     open_trade_count_start = self.backtest_loop(
-                        row, pair, current_time, end_date, max_open_trades, open_trade_count_start)
+                        row, pair, current_time, end_date, max_open_trades,
+                        open_trade_count_start, trade_dir)
 
             # Move time one configured time_interval ahead.
             self.progress.increment()

freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py

@@ -5,13 +5,11 @@ This module defines the alternative HyperOptLoss class which can be used for
 Hyperoptimization.
 """
 from datetime import datetime
-from math import sqrt as msqrt
-from typing import Any, Dict
 
 from pandas import DataFrame
 
 from freqtrade.constants import Config
-from freqtrade.data.metrics import calculate_max_drawdown
+from freqtrade.data.metrics import calculate_calmar
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 
@@ -23,42 +21,15 @@ class CalmarHyperOptLoss(IHyperOptLoss):
     """
 
     @staticmethod
-    def hyperopt_loss_function(
-        results: DataFrame,
-        trade_count: int,
-        min_date: datetime,
-        max_date: datetime,
-        config: Config,
-        processed: Dict[str, DataFrame],
-        backtest_stats: Dict[str, Any],
-        *args,
-        **kwargs
-    ) -> float:
+    def hyperopt_loss_function(results: DataFrame, trade_count: int,
+                               min_date: datetime, max_date: datetime,
+                               config: Config, *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for more optimal results.
 
         Uses Calmar Ratio calculation.
         """
-        total_profit = backtest_stats["profit_total"]
-        days_period = (max_date - min_date).days
-
-        # adding slippage of 0.1% per trade
-        total_profit = total_profit - 0.0005
-        expected_returns_mean = total_profit.sum() / days_period * 100
-
-        # calculate max drawdown
-        try:
-            _, _, _, _, _, max_drawdown = calculate_max_drawdown(
-                results, value_col="profit_abs"
-            )
-        except ValueError:
-            max_drawdown = 0
-
-        if max_drawdown != 0:
-            calmar_ratio = expected_returns_mean / max_drawdown * msqrt(365)
-        else:
-            # Define high (negative) calmar ratio to be clear that this is NOT optimal.
-            calmar_ratio = -20.0
-
+        starting_balance = config['dry_run_wallet']
+        calmar_ratio = calculate_calmar(results, min_date, max_date, starting_balance)
         # print(expected_returns_mean, max_drawdown, calmar_ratio)
         return -calmar_ratio

freqtrade/optimize/hyperopt_loss/hyperopt_loss_sharpe.py

@@ -6,9 +6,10 @@ Hyperoptimization.
 """
 from datetime import datetime
 
-import numpy as np
 from pandas import DataFrame
 
+from freqtrade.constants import Config
+from freqtrade.data.metrics import calculate_sharpe
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 
@@ -22,25 +23,13 @@ class SharpeHyperOptLoss(IHyperOptLoss):
     @staticmethod
     def hyperopt_loss_function(results: DataFrame, trade_count: int,
                                min_date: datetime, max_date: datetime,
-                               *args, **kwargs) -> float:
+                               config: Config, *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for more optimal results.
 
         Uses Sharpe Ratio calculation.
         """
-        total_profit = results["profit_ratio"]
-        days_period = (max_date - min_date).days
-
-        # adding slippage of 0.1% per trade
-        total_profit = total_profit - 0.0005
-        expected_returns_mean = total_profit.sum() / days_period
-        up_stdev = np.std(total_profit)
-
-        if up_stdev != 0:
-            sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
-        else:
-            # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
-            sharp_ratio = -20.
-
+        starting_balance = config['dry_run_wallet']
+        sharp_ratio = calculate_sharpe(results, min_date, max_date, starting_balance)
         # print(expected_returns_mean, up_stdev, sharp_ratio)
         return -sharp_ratio

freqtrade/optimize/hyperopt_loss/hyperopt_loss_sortino.py

@@ -6,9 +6,10 @@ Hyperoptimization.
 """
 from datetime import datetime
 
-import numpy as np
 from pandas import DataFrame
 
+from freqtrade.constants import Config
+from freqtrade.data.metrics import calculate_sortino
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 
@@ -22,28 +23,13 @@ class SortinoHyperOptLoss(IHyperOptLoss):
     @staticmethod
     def hyperopt_loss_function(results: DataFrame, trade_count: int,
                                min_date: datetime, max_date: datetime,
-                               *args, **kwargs) -> float:
+                               config: Config, *args, **kwargs) -> float:
         """
         Objective function, returns smaller number for more optimal results.
 
         Uses Sortino Ratio calculation.
         """
-        total_profit = results["profit_ratio"]
-        days_period = (max_date - min_date).days
-
-        # adding slippage of 0.1% per trade
-        total_profit = total_profit - 0.0005
-        expected_returns_mean = total_profit.sum() / days_period
-
-        results['downside_returns'] = 0
-        results.loc[total_profit < 0, 'downside_returns'] = results['profit_ratio']
-        down_stdev = np.std(results['downside_returns'])
-
-        if down_stdev != 0:
-            sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
-        else:
-            # Define high (negative) sortino ratio to be clear that this is NOT optimal.
-            sortino_ratio = -20.
-
+        starting_balance = config['dry_run_wallet']
+        sortino_ratio = calculate_sortino(results, min_date, max_date, starting_balance)
         # print(expected_returns_mean, down_stdev, sortino_ratio)
         return -sortino_ratio

freqtrade/optimize/optimize_reports.py

@@ -9,8 +9,9 @@ from tabulate import tabulate
 
 from freqtrade.constants import (DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT,
                                  Config)
-from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change,
-                                    calculate_max_drawdown)
+from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
+                                    calculate_expectancy, calculate_market_change,
+                                    calculate_max_drawdown, calculate_sharpe, calculate_sortino)
 from freqtrade.misc import decimals_per_coin, file_dump_joblib, file_dump_json, round_coin_value
 from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
 
@@ -448,6 +449,10 @@ def generate_strategy_stats(pairlist: List[str],
         'profit_total_long_abs': results.loc[~results['is_short'], 'profit_abs'].sum(),
         'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(),
         'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']),
+        'expectancy': calculate_expectancy(results),
+        'sortino': calculate_sortino(results, min_date, max_date, start_balance),
+        'sharpe': calculate_sharpe(results, min_date, max_date, start_balance),
+        'calmar': calculate_calmar(results, min_date, max_date, start_balance),
         'profit_factor': profit_factor,
         'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
         'backtest_start_ts': int(min_date.timestamp() * 1000),
@@ -785,8 +790,13 @@ def text_table_add_metrics(strat_results: Dict) -> str:
                                                   strat_results['stake_currency'])),
             ('Total profit %', f"{strat_results['profit_total']:.2%}"),
             ('CAGR %', f"{strat_results['cagr']:.2%}" if 'cagr' in strat_results else 'N/A'),
+            ('Sortino', f"{strat_results['sortino']:.2f}" if 'sortino' in strat_results else 'N/A'),
+            ('Sharpe', f"{strat_results['sharpe']:.2f}" if 'sharpe' in strat_results else 'N/A'),
+            ('Calmar', f"{strat_results['calmar']:.2f}" if 'calmar' in strat_results else 'N/A'),
             ('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor'
                               in strat_results else 'N/A'),
+            ('Expectancy', f"{strat_results['expectancy']:.2f}" if 'expectancy'
+                           in strat_results else 'N/A'),
             ('Trades per day', strat_results['trades_per_day']),
             ('Avg. daily profit %',
              f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),

freqtrade/persistence/migrations.py

@@ -109,11 +109,10 @@ def migrate_trades_and_orders_table(
     else:
         is_short = get_column_def(cols, 'is_short', '0')
 
-    # Margin Properties
+    # Futures Properties
     interest_rate = get_column_def(cols, 'interest_rate', '0.0')
-
-    # Futures properties
     funding_fees = get_column_def(cols, 'funding_fees', '0.0')
+    max_stake_amount = get_column_def(cols, 'max_stake_amount', 'stake_amount')
 
     # If ticker-interval existed use that, else null.
     if has_column(cols, 'ticker_interval'):
@@ -162,7 +161,8 @@ def migrate_trades_and_orders_table(
             timeframe, open_trade_value, close_profit_abs,
             trading_mode, leverage, liquidation_price, is_short,
             interest_rate, funding_fees, realized_profit,
-            amount_precision, price_precision, precision_mode, contract_size
+            amount_precision, price_precision, precision_mode, contract_size,
+            max_stake_amount
             )
         select id, lower(exchange), pair, {base_currency} base_currency,
             {stake_currency} stake_currency,
@@ -190,7 +190,8 @@ def migrate_trades_and_orders_table(
             {is_short} is_short, {interest_rate} interest_rate,
             {funding_fees} funding_fees, {realized_profit} realized_profit,
             {amount_precision} amount_precision, {price_precision} price_precision,
-            {precision_mode} precision_mode, {contract_size} contract_size
+            {precision_mode} precision_mode, {contract_size} contract_size,
+            {max_stake_amount} max_stake_amount
             from {trade_back_name}
             """))
 
@@ -213,17 +214,22 @@ def migrate_orders_table(engine, table_back_name: str, cols_order: List):
     average = get_column_def(cols_order, 'average', 'null')
     stop_price = get_column_def(cols_order, 'stop_price', 'null')
     funding_fee = get_column_def(cols_order, 'funding_fee', '0.0')
+    ft_amount = get_column_def(cols_order, 'ft_amount', 'coalesce(amount, 0.0)')
+    ft_price = get_column_def(cols_order, 'ft_price', 'coalesce(price, 0.0)')
 
     # sqlite does not support literals for booleans
     with engine.begin() as connection:
         connection.execute(text(f"""
             insert into orders (id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id,
             status, symbol, order_type, side, price, amount, filled, average, remaining, cost,
-            stop_price, order_date, order_filled_date, order_update_date, ft_fee_base, funding_fee)
+            stop_price, order_date, order_filled_date, order_update_date, ft_fee_base, funding_fee,
+            ft_amount, ft_price
+            )
             select id, ft_trade_id, ft_order_side, ft_pair, ft_is_open, order_id,
             status, symbol, order_type, side, price, amount, filled, {average} average, remaining,
             cost, {stop_price} stop_price, order_date, order_filled_date,
-            order_update_date, {ft_fee_base} ft_fee_base, {funding_fee} funding_fee
+            order_update_date, {ft_fee_base} ft_fee_base, {funding_fee} funding_fee,
+            {ft_amount} ft_amount, {ft_price} ft_price
             from {table_back_name}
             """))
 
@@ -310,8 +316,8 @@ def check_migrate(engine, decl_base, previous_tables) -> None:
     # if ('orders' not in previous_tables
     # or not has_column(cols_orders, 'funding_fee')):
     migrating = False
-    # if not has_column(cols_trades, 'contract_size'):
-    if not has_column(cols_orders, 'funding_fee'):
+    # if not has_column(cols_trades, 'max_stake_amount'):
+    if not has_column(cols_orders, 'ft_price'):
         migrating = True
         logger.info(f"Running database migration for trades - "
                     f"backup: {table_back_name}, {order_table_bak_name}")

freqtrade/persistence/trade_model.py

@@ -49,6 +49,8 @@ class Order(_DECL_BASE):
     ft_order_side: str = Column(String(25), nullable=False)
     ft_pair: str = Column(String(25), nullable=False)
     ft_is_open = Column(Boolean, nullable=False, default=True, index=True)
+    ft_amount = Column(Float, nullable=False)
+    ft_price = Column(Float, nullable=False)
 
     order_id: str = Column(String(255), nullable=False, index=True)
     status = Column(String(255), nullable=True)
@@ -82,9 +84,13 @@ class Order(_DECL_BASE):
             self.order_filled_date.replace(tzinfo=timezone.utc) if self.order_filled_date else None
         )
 
+    @property
+    def safe_amount(self) -> float:
+        return self.amount or self.ft_amount
+
     @property
     def safe_price(self) -> float:
-        return self.average or self.price or self.stop_price
+        return self.average or self.price or self.stop_price or self.ft_price
 
     @property
     def safe_filled(self) -> float:
@@ -94,7 +100,7 @@ class Order(_DECL_BASE):
     def safe_remaining(self) -> float:
         return (
             self.remaining if self.remaining is not None else
-            self.amount - (self.filled or 0.0)
+            self.safe_amount - (self.filled or 0.0)
         )
 
     @property
@@ -227,11 +233,20 @@ class Order(_DECL_BASE):
             logger.warning(f"Did not find order for {order}.")
 
     @staticmethod
-    def parse_from_ccxt_object(order: Dict[str, Any], pair: str, side: str) -> 'Order':
+    def parse_from_ccxt_object(
+            order: Dict[str, Any], pair: str, side: str,
+            amount: Optional[float] = None, price: Optional[float] = None) -> 'Order':
         """
         Parse an order from a ccxt object and return a new order Object.
+        Optional support for overriding amount and price is only used for test simplification.
         """
-        o = Order(order_id=str(order['id']), ft_order_side=side, ft_pair=pair)
+        o = Order(
+            order_id=str(order['id']),
+            ft_order_side=side,
+            ft_pair=pair,
+            ft_amount=amount if amount else order['amount'],
+            ft_price=price if price else order['price'],
+            )
 
         o.update_from_ccxt_object(order)
         return o
@@ -293,6 +308,7 @@ class LocalTrade():
     close_profit: Optional[float] = None
     close_profit_abs: Optional[float] = None
     stake_amount: float = 0.0
+    max_stake_amount: float = 0.0
     amount: float = 0.0
     amount_requested: Optional[float] = None
     open_date: datetime
@@ -397,12 +413,6 @@ class LocalTrade():
     def close_date_utc(self):
         return self.close_date.replace(tzinfo=timezone.utc)
 
-    @property
-    def enter_side(self) -> str:
-        """ DEPRECATED, please use entry_side instead"""
-        # TODO: Please remove me after 2022.5
-        return self.entry_side
-
     @property
     def entry_side(self) -> str:
         if self.is_short:
@@ -475,8 +485,8 @@ class LocalTrade():
             'amount': round(self.amount, 8),
             'amount_requested': round(self.amount_requested, 8) if self.amount_requested else None,
             'stake_amount': round(self.stake_amount, 8),
+            'max_stake_amount': round(self.max_stake_amount, 8) if self.max_stake_amount else None,
             'strategy': self.strategy,
-            'buy_tag': self.enter_tag,
             'enter_tag': self.enter_tag,
             'timeframe': self.timeframe,
 
@@ -513,7 +523,6 @@ class LocalTrade():
             'profit_pct': round(self.close_profit * 100, 2) if self.close_profit else None,
             'profit_abs': self.close_profit_abs,
 
-            'sell_reason': self.exit_reason,  # Deprecated
             'exit_reason': self.exit_reason,
             'exit_order_status': self.exit_order_status,
             'stop_loss_abs': self.stop_loss,
@@ -882,6 +891,7 @@ class LocalTrade():
         ZERO = FtPrecise(0.0)
         current_amount = FtPrecise(0.0)
         current_stake = FtPrecise(0.0)
+        max_stake_amount = FtPrecise(0.0)
         total_stake = 0.0  # Total stake after all buy orders (does not subtract!)
         avg_price = FtPrecise(0.0)
         close_profit = 0.0
@@ -923,7 +933,9 @@ class LocalTrade():
                     exit_rate, amount=exit_amount, open_rate=avg_price)
             else:
                 total_stake = total_stake + self._calc_open_trade_value(tmp_amount, price)
+                max_stake_amount += (tmp_amount * price)
         self.funding_fees = funding_fees
+        self.max_stake_amount = float(max_stake_amount)
 
         if close_profit:
             self.close_profit = close_profit
@@ -959,11 +971,12 @@ class LocalTrade():
         return None
 
     def select_order(self, order_side: Optional[str] = None,
-                     is_open: Optional[bool] = None) -> Optional[Order]:
+                     is_open: Optional[bool] = None, only_filled: bool = False) -> Optional[Order]:
         """
         Finds latest order for this orderside and status
         :param order_side: ft_order_side of the order (either 'buy', 'sell' or 'stoploss')
         :param is_open: Only search for open orders?
+        :param only_filled: Only search for Filled orders (only valid with is_open=False).
         :return: latest Order object if it exists, else None
         """
         orders = self.orders
@@ -971,6 +984,8 @@ class LocalTrade():
             orders = [o for o in orders if o.ft_order_side == order_side]
         if is_open is not None:
             orders = [o for o in orders if o.ft_is_open == is_open]
+        if is_open is False and only_filled:
+            orders = [o for o in orders if o.filled and o.status in NON_OPEN_EXCHANGE_STATES]
         if len(orders) > 0:
             return orders[-1]
         else:
@@ -1175,6 +1190,7 @@ class Trade(_DECL_BASE, LocalTrade):
     close_profit = Column(Float)
     close_profit_abs = Column(Float)
     stake_amount = Column(Float, nullable=False)
+    max_stake_amount = Column(Float)
     amount = Column(Float)
     amount_requested = Column(Float)
     open_date = Column(DateTime, nullable=False, default=datetime.utcnow)

freqtrade/plugins/pairlist/RemotePairList.py

@@ -0,0 +1,206 @@
+"""
+Remote PairList provider
+
+Provides pair list fetched from a remote source
+"""
+import json
+import logging
+from pathlib import Path
+from typing import Any, Dict, List, Tuple
+
+import requests
+from cachetools import TTLCache
+
+from freqtrade import __version__
+from freqtrade.constants import Config
+from freqtrade.exceptions import OperationalException
+from freqtrade.exchange.types import Tickers
+from freqtrade.plugins.pairlist.IPairList import IPairList
+
+
+logger = logging.getLogger(__name__)
+
+
+class RemotePairList(IPairList):
+
+    def __init__(self, exchange, pairlistmanager,
+                 config: Config, pairlistconfig: Dict[str, Any],
+                 pairlist_pos: int) -> None:
+        super().__init__(exchange, pairlistmanager, config, pairlistconfig, pairlist_pos)
+
+        if 'number_assets' not in self._pairlistconfig:
+            raise OperationalException(
+                '`number_assets` not specified. Please check your configuration '
+                'for "pairlist.config.number_assets"')
+
+        if 'pairlist_url' not in self._pairlistconfig:
+            raise OperationalException(
+                '`pairlist_url` not specified. Please check your configuration '
+                'for "pairlist.config.pairlist_url"')
+
+        self._number_pairs = self._pairlistconfig['number_assets']
+        self._refresh_period: int = self._pairlistconfig.get('refresh_period', 1800)
+        self._keep_pairlist_on_failure = self._pairlistconfig.get('keep_pairlist_on_failure', True)
+        self._pair_cache: TTLCache = TTLCache(maxsize=1, ttl=self._refresh_period)
+        self._pairlist_url = self._pairlistconfig.get('pairlist_url', '')
+        self._read_timeout = self._pairlistconfig.get('read_timeout', 60)
+        self._bearer_token = self._pairlistconfig.get('bearer_token', '')
+        self._init_done = False
+        self._last_pairlist: List[Any] = list()
+
+    @property
+    def needstickers(self) -> bool:
+        """
+        Boolean property defining if tickers are necessary.
+        If no Pairlist requires tickers, an empty Dict is passed
+        as tickers argument to filter_pairlist
+        """
+        return False
+
+    def short_desc(self) -> str:
+        """
+        Short whitelist method description - used for startup-messages
+        """
+        return f"{self.name} - {self._pairlistconfig['number_assets']} pairs from RemotePairlist."
+
+    def process_json(self, jsonparse) -> List[str]:
+
+        pairlist = jsonparse.get('pairs', [])
+        remote_refresh_period = int(jsonparse.get('refresh_period', self._refresh_period))
+
+        if self._refresh_period < remote_refresh_period:
+            self.log_once(f'Refresh Period has been increased from {self._refresh_period}'
+                          f' to minimum allowed: {remote_refresh_period} from Remote.', logger.info)
+
+            self._refresh_period = remote_refresh_period
+            self._pair_cache = TTLCache(maxsize=1, ttl=remote_refresh_period)
+
+        self._init_done = True
+
+        return pairlist
+
+    def return_last_pairlist(self) -> List[str]:
+        if self._keep_pairlist_on_failure:
+            pairlist = self._last_pairlist
+            self.log_once('Keeping last fetched pairlist', logger.info)
+        else:
+            pairlist = []
+
+        return pairlist
+
+    def fetch_pairlist(self) -> Tuple[List[str], float]:
+
+        headers = {
+            'User-Agent': 'Freqtrade/' + __version__ + ' Remotepairlist'
+        }
+
+        if self._bearer_token:
+            headers['Authorization'] = f'Bearer {self._bearer_token}'
+
+        try:
+            response = requests.get(self._pairlist_url, headers=headers,
+                                    timeout=self._read_timeout)
+            content_type = response.headers.get('content-type')
+            time_elapsed = response.elapsed.total_seconds()
+
+            if "application/json" in str(content_type):
+                jsonparse = response.json()
+
+                try:
+                    pairlist = self.process_json(jsonparse)
+                except Exception as e:
+
+                    if self._init_done:
+                        pairlist = self.return_last_pairlist()
+                        logger.warning(f'Error while processing JSON data: {type(e)}')
+                    else:
+                        raise OperationalException(f'Error while processing JSON data: {type(e)}')
+
+            else:
+                if self._init_done:
+                    self.log_once(f'Error: RemotePairList is not of type JSON: '
+                                  f' {self._pairlist_url}', logger.info)
+                    pairlist = self.return_last_pairlist()
+                else:
+                    raise OperationalException('RemotePairList is not of type JSON, abort.')
+
+        except requests.exceptions.RequestException:
+            self.log_once(f'Was not able to fetch pairlist from:'
+                          f' {self._pairlist_url}', logger.info)
+
+            pairlist = self.return_last_pairlist()
+
+            time_elapsed = 0
+
+        return pairlist, time_elapsed
+
+    def gen_pairlist(self, tickers: Tickers) -> List[str]:
+        """
+        Generate the pairlist
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: List of pairs
+        """
+
+        if self._init_done:
+            pairlist = self._pair_cache.get('pairlist')
+        else:
+            pairlist = []
+
+        time_elapsed = 0.0
+
+        if pairlist:
+            # Item found - no refresh necessary
+            return pairlist.copy()
+        else:
+            if self._pairlist_url.startswith("file:///"):
+                filename = self._pairlist_url.split("file:///", 1)[1]
+                file_path = Path(filename)
+
+                if file_path.exists():
+                    with open(filename) as json_file:
+                        # Load the JSON data into a dictionary
+                        jsonparse = json.load(json_file)
+
+                        try:
+                            pairlist = self.process_json(jsonparse)
+                        except Exception as e:
+                            if self._init_done:
+                                pairlist = self.return_last_pairlist()
+                                logger.warning(f'Error while processing JSON data: {type(e)}')
+                            else:
+                                raise OperationalException('Error while processing'
+                                                           f'JSON data: {type(e)}')
+                else:
+                    raise ValueError(f"{self._pairlist_url} does not exist.")
+            else:
+                # Fetch Pairlist from Remote URL
+                pairlist, time_elapsed = self.fetch_pairlist()
+
+        self.log_once(f"Fetched pairs: {pairlist}", logger.debug)
+
+        pairlist = self._whitelist_for_active_markets(pairlist)
+        pairlist = pairlist[:self._number_pairs]
+
+        self._pair_cache['pairlist'] = pairlist.copy()
+
+        if time_elapsed != 0.0:
+            self.log_once(f'Pairlist Fetched in {time_elapsed} seconds.', logger.info)
+        else:
+            self.log_once('Fetched Pairlist.', logger.info)
+
+        self._last_pairlist = list(pairlist)
+
+        return pairlist
+
+    def filter_pairlist(self, pairlist: List[str], tickers: Dict) -> List[str]:
+        """
+        Filters and sorts pairlist and returns the whitelist again.
+        Called on each bot iteration - please use internal caching if necessary
+        :param pairlist: pairlist to filter or sort
+        :param tickers: Tickers (from exchange.get_tickers). May be cached.
+        :return: new whitelist
+        """
+        rpl_pairlist = self.gen_pairlist(tickers)
+        merged_list = pairlist + rpl_pairlist
+        merged_list = sorted(set(merged_list), key=merged_list.index)
+        return merged_list

freqtrade/plugins/pairlist/VolumePairList.py

@@ -135,7 +135,7 @@ class VolumePairList(IPairList):
                 filtered_tickers = [
                     v for k, v in tickers.items()
                     if (self._exchange.get_pair_quote_currency(k) == self._stake_currency
-                        and (self._use_range or v[self._sort_key] is not None)
+                        and (self._use_range or v.get(self._sort_key) is not None)
                         and v['symbol'] in _pairlist)]
                 pairlist = [s['symbol'] for s in filtered_tickers]
             else:
@@ -218,7 +218,7 @@ class VolumePairList(IPairList):
                 else:
                     filtered_tickers[i]['quoteVolume'] = 0
         else:
-            # Tickers mode - filter based on incomming pairlist.
+            # Tickers mode - filter based on incoming pairlist.
             filtered_tickers = [v for k, v in tickers.items() if k in pairlist]
 
         if self._min_value > 0:

freqtrade/rpc/api_server/api_backtest.py

@@ -11,6 +11,7 @@ from freqtrade.configuration.config_validation import validate_config_consistenc
 from freqtrade.data.btanalysis import get_backtest_resultlist, load_and_merge_backtest_result
 from freqtrade.enums import BacktestState
 from freqtrade.exceptions import DependencyException
+from freqtrade.misc import deep_merge_dicts
 from freqtrade.rpc.api_server.api_schemas import (BacktestHistoryEntry, BacktestRequest,
                                                   BacktestResponse)
 from freqtrade.rpc.api_server.deps import get_config, is_webserver_mode
@@ -37,10 +38,11 @@ async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: Bac
 
     btconfig = deepcopy(config)
     settings = dict(bt_settings)
+    if settings.get('freqai', None) is not None:
+        settings['freqai'] = dict(settings['freqai'])
     # Pydantic models will contain all keys, but non-provided ones are None
-    for setting in settings.keys():
-        if settings[setting] is not None:
-            btconfig[setting] = settings[setting]
+
+    btconfig = deep_merge_dicts(settings, btconfig, allow_null_overrides=False)
     try:
         btconfig['stake_amount'] = float(btconfig['stake_amount'])
     except ValueError:

freqtrade/rpc/api_server/api_schemas.py

@@ -217,8 +217,8 @@ class TradeSchema(BaseModel):
     amount: float
     amount_requested: float
     stake_amount: float
+    max_stake_amount: Optional[float]
     strategy: str
-    buy_tag: Optional[str]  # Deprecated
     enter_tag: Optional[str]
     timeframe: int
     fee_open: Optional[float]
@@ -243,7 +243,6 @@ class TradeSchema(BaseModel):
     profit_pct: Optional[float]
     profit_abs: Optional[float]
     profit_fiat: Optional[float]
-    sell_reason: Optional[str]  # Deprecated
     exit_reason: Optional[str]
     exit_order_status: Optional[str]
     stop_loss_abs: Optional[float]
@@ -372,6 +371,10 @@ class StrategyListResponse(BaseModel):
     strategies: List[str]
 
 
+class FreqAIModelListResponse(BaseModel):
+    freqaimodels: List[str]
+
+
 class StrategyResponse(BaseModel):
     strategy: str
     code: str
@@ -410,6 +413,10 @@ class PairHistory(BaseModel):
         }
 
 
+class BacktestFreqAIInputs(BaseModel):
+    identifier: str
+
+
 class BacktestRequest(BaseModel):
     strategy: str
     timeframe: Optional[str]
@@ -419,6 +426,9 @@ class BacktestRequest(BaseModel):
     stake_amount: Optional[str]
     enable_protections: bool
     dry_run_wallet: Optional[float]
+    backtest_cache: Optional[str]
+    freqaimodel: Optional[str]
+    freqai: Optional[BacktestFreqAIInputs]
 
 
 class BacktestResponse(BaseModel):

freqtrade/rpc/api_server/api_v1.py

@@ -13,12 +13,13 @@ from freqtrade.rpc import RPC
 from freqtrade.rpc.api_server.api_schemas import (AvailablePairs, Balances, BlacklistPayload,
                                                   BlacklistResponse, Count, Daily,
                                                   DeleteLockRequest, DeleteTrade, ForceEnterPayload,
-                                                  ForceEnterResponse, ForceExitPayload, Health,
-                                                  Locks, Logs, OpenTradeSchema, PairHistory,
-                                                  PerformanceEntry, Ping, PlotConfig, Profit,
-                                                  ResultMsg, ShowConfig, Stats, StatusMsg,
-                                                  StrategyListResponse, StrategyResponse, SysInfo,
-                                                  Version, WhitelistResponse)
+                                                  ForceEnterResponse, ForceExitPayload,
+                                                  FreqAIModelListResponse, Health, Locks, Logs,
+                                                  OpenTradeSchema, PairHistory, PerformanceEntry,
+                                                  Ping, PlotConfig, Profit, ResultMsg, ShowConfig,
+                                                  Stats, StatusMsg, StrategyListResponse,
+                                                  StrategyResponse, SysInfo, Version,
+                                                  WhitelistResponse)
 from freqtrade.rpc.api_server.deps import get_config, get_exchange, get_rpc, get_rpc_optional
 from freqtrade.rpc.rpc import RPCException
 
@@ -38,7 +39,8 @@ logger = logging.getLogger(__name__)
 # 2.17: Forceentry - leverage, partial force_exit
 # 2.20: Add websocket endpoints
 # 2.21: Add new_candle messagetype
-API_VERSION = 2.21
+# 2.22: Add FreqAI to backtesting
+API_VERSION = 2.22
 
 # Public API, requires no auth.
 router_public = APIRouter()
@@ -279,6 +281,16 @@ def get_strategy(strategy: str, config=Depends(get_config)):
     }
 
 
+@router.get('/freqaimodels', response_model=FreqAIModelListResponse, tags=['freqai'])
+def list_freqaimodels(config=Depends(get_config)):
+    from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
+    strategies = FreqaiModelResolver.search_all_objects(
+        config, False)
+    strategies = sorted(strategies, key=lambda x: x['name'])
+
+    return {'freqaimodels': [x['name'] for x in strategies]}
+
+
 @router.get('/available_pairs', response_model=AvailablePairs, tags=['candle data'])
 def list_available_pairs(timeframe: Optional[str] = None, stake_currency: Optional[str] = None,
                          candletype: Optional[CandleType] = None, config=Depends(get_config)):

freqtrade/rpc/api_server/api_ws.py

@@ -91,9 +91,10 @@ async def _process_consumer_request(
     elif type == RPCRequestType.ANALYZED_DF:
         # Limit the amount of candles per dataframe to 'limit' or 1500
         limit = min(data.get('limit', 1500), 1500) if data else None
+        pair = data.get('pair', None) if data else None
 
         # For every pair in the generator, send a separate message
-        for message in rpc._ws_request_analyzed_df(limit):
+        for message in rpc._ws_request_analyzed_df(limit, pair):
             # Format response
             response = WSAnalyzedDFMessage(data=message)
             await channel.send(response.dict(exclude_none=True))

freqtrade/rpc/api_server/ws/channel.py

@@ -27,7 +27,8 @@ class WebSocketChannel:
         self,
         websocket: WebSocketType,
         channel_id: Optional[str] = None,
-        serializer_cls: Type[WebSocketSerializer] = HybridJSONWebSocketSerializer
+        serializer_cls: Type[WebSocketSerializer] = HybridJSONWebSocketSerializer,
+        send_throttle: float = 0.01
     ):
         self.channel_id = channel_id if channel_id else uuid4().hex[:8]
         self._websocket = WebSocketProxy(websocket)
@@ -41,6 +42,7 @@ class WebSocketChannel:
         self._send_times: Deque[float] = deque([], maxlen=10)
         # High limit defaults to 3 to start
         self._send_high_limit = 3
+        self._send_throttle = send_throttle
 
         # The subscribed message types
         self._subscriptions: List[str] = []
@@ -106,7 +108,8 @@ class WebSocketChannel:
 
         # Explicitly give control back to event loop as
         # websockets.send does not
-        await asyncio.sleep(0.01)
+        # Also throttles how fast we send
+        await asyncio.sleep(self._send_throttle)
 
     async def recv(self):
         """

freqtrade/rpc/api_server/ws_schemas.py

@@ -47,7 +47,7 @@ class WSWhitelistRequest(WSRequestSchema):
 
 class WSAnalyzedDFRequest(WSRequestSchema):
     type: RPCRequestType = RPCRequestType.ANALYZED_DF
-    data: Dict[str, Any] = {"limit": 1500}
+    data: Dict[str, Any] = {"limit": 1500, "pair": None}
 
 
 # ------------------------------ MESSAGE SCHEMAS ----------------------------

freqtrade/rpc/external_message_consumer.py

@@ -8,15 +8,17 @@ import asyncio
 import logging
 import socket
 from threading import Thread
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, TypedDict
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, TypedDict, Union
 
 import websockets
 from pydantic import ValidationError
 
+from freqtrade.constants import FULL_DATAFRAME_THRESHOLD
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.enums import RPCMessageType
 from freqtrade.misc import remove_entry_exit_signals
-from freqtrade.rpc.api_server.ws import WebSocketChannel
+from freqtrade.rpc.api_server.ws.channel import WebSocketChannel, create_channel
+from freqtrade.rpc.api_server.ws.message_stream import MessageStream
 from freqtrade.rpc.api_server.ws_schemas import (WSAnalyzedDFMessage, WSAnalyzedDFRequest,
                                                  WSMessageSchema, WSRequestSchema,
                                                  WSSubscribeRequest, WSWhitelistMessage,
@@ -38,6 +40,10 @@ class Producer(TypedDict):
 logger = logging.getLogger(__name__)
 
 
+def schema_to_dict(schema: Union[WSMessageSchema, WSRequestSchema]):
+    return schema.dict(exclude_none=True)
+
+
 class ExternalMessageConsumer:
     """
     The main controller class for consuming external messages from
@@ -92,6 +98,8 @@ class ExternalMessageConsumer:
             RPCMessageType.ANALYZED_DF: self._consume_analyzed_df_message,
         }
 
+        self._channel_streams: Dict[str, MessageStream] = {}
+
         self.start()
 
     def start(self):
@@ -118,6 +126,8 @@ class ExternalMessageConsumer:
             logger.info("Stopping ExternalMessageConsumer")
             self._running = False
 
+            self._channel_streams = {}
+
             if self._sub_tasks:
                 # Cancel sub tasks
                 for task in self._sub_tasks:
@@ -175,7 +185,6 @@ class ExternalMessageConsumer:
         :param producer: Dictionary containing producer info
         :param lock: An asyncio Lock
         """
-        channel = None
         while self._running:
             try:
                 host, port = producer['host'], producer['port']
@@ -190,19 +199,21 @@ class ExternalMessageConsumer:
                     max_size=self.message_size_limit,
                     ping_interval=None
                 ) as ws:
-                    channel = WebSocketChannel(ws, channel_id=name)
+                    async with create_channel(
+                        ws,
+                        channel_id=name,
+                        send_throttle=0.5
+                    ) as channel:
 
-                    logger.info(f"Producer connection success - {channel}")
+                        # Create the message stream for this channel
+                        self._channel_streams[name] = MessageStream()
 
-                    # Now request the initial data from this Producer
-                    for request in self._initial_requests:
-                        await channel.send(
-                            request.dict(exclude_none=True)
+                        # Run the channel tasks while connected
+                        await channel.run_channel_tasks(
+                            self._receive_messages(channel, producer, lock),
+                            self._send_requests(channel, self._channel_streams[name])
                         )
 
-                    # Now receive data, if none is within the time limit, ping
-                    await self._receive_messages(channel, producer, lock)
-
             except (websockets.exceptions.InvalidURI, ValueError) as e:
                 logger.error(f"{ws_url} is an invalid WebSocket URL - {e}")
                 break
@@ -229,11 +240,19 @@ class ExternalMessageConsumer:
                 # An unforseen error has occurred, log and continue
                 logger.error("Unexpected error has occurred:")
                 logger.exception(e)
+                await asyncio.sleep(self.sleep_time)
                 continue
 
-            finally:
-                if channel:
-                    await channel.close()
+    async def _send_requests(self, channel: WebSocketChannel, channel_stream: MessageStream):
+        # Send the initial requests
+        for init_request in self._initial_requests:
+            await channel.send(schema_to_dict(init_request))
+
+        # Now send any subsequent requests published to
+        # this channel's stream
+        async for request, _ in channel_stream:
+            logger.debug(f"Sending request to channel - {channel} - {request}")
+            await channel.send(request)
 
     async def _receive_messages(
         self,
@@ -270,19 +289,31 @@ class ExternalMessageConsumer:
                     latency = (await asyncio.wait_for(pong, timeout=self.ping_timeout) * 1000)
 
                     logger.info(f"Connection to {channel} still alive, latency: {latency}ms")
-
                     continue
-                except (websockets.exceptions.ConnectionClosed):
-                    # Just eat the error and continue reconnecting
-                    logger.warning(f"Disconnection in {channel} - retrying in {self.sleep_time}s")
-                    await asyncio.sleep(self.sleep_time)
-                    break
+
                 except Exception as e:
+                    # Just eat the error and continue reconnecting
                     logger.warning(f"Ping error {channel} - {e} - retrying in {self.sleep_time}s")
                     logger.debug(e, exc_info=e)
-                    await asyncio.sleep(self.sleep_time)
+                    raise
 
-                    break
+    def send_producer_request(
+        self,
+        producer_name: str,
+        request: Union[WSRequestSchema, Dict[str, Any]]
+    ):
+        """
+        Publish a message to the producer's message stream to be
+        sent by the channel task.
+
+        :param producer_name: The name of the producer to publish the message to
+        :param request: The request to send to the producer
+        """
+        if isinstance(request, WSRequestSchema):
+            request = schema_to_dict(request)
+
+        if channel_stream := self._channel_streams.get(producer_name):
+            channel_stream.publish(request)
 
     def handle_producer_message(self, producer: Producer, message: Dict[str, Any]):
         """
@@ -336,16 +367,45 @@ class ExternalMessageConsumer:
 
         pair, timeframe, candle_type = key
 
+        if df.empty:
+            logger.debug(f"Received Empty Dataframe for {key}")
+            return
+
         # If set, remove the Entry and Exit signals from the Producer
         if self._emc_config.get('remove_entry_exit_signals', False):
             df = remove_entry_exit_signals(df)
 
-        # Add the dataframe to the dataprovider
-        self._dp._add_external_df(pair, df,
-                                  last_analyzed=la,
-                                  timeframe=timeframe,
-                                  candle_type=candle_type,
-                                  producer_name=producer_name)
+        logger.debug(f"Received {len(df)} candle(s) for {key}")
+
+        did_append, n_missing = self._dp._add_external_df(
+            pair,
+            df,
+            last_analyzed=la,
+            timeframe=timeframe,
+            candle_type=candle_type,
+            producer_name=producer_name
+            )
+
+        if not did_append:
+            # We want an overlap in candles incase some data has changed
+            n_missing += 1
+            # Set to None for all candles if we missed a full df's worth of candles
+            n_missing = n_missing if n_missing < FULL_DATAFRAME_THRESHOLD else 1500
+
+            logger.warning(f"Holes in data or no existing df, requesting {n_missing} candles "
+                           f"for {key} from `{producer_name}`")
+
+            self.send_producer_request(
+                producer_name,
+                WSAnalyzedDFRequest(
+                    data={
+                        "limit": n_missing,
+                        "pair": pair
+                    }
+                )
+            )
+            return
 
         logger.debug(
-            f"Consumed message from `{producer_name}` of type `RPCMessageType.ANALYZED_DF`")
+            f"Consumed message from `{producer_name}` "
+            f"of type `RPCMessageType.ANALYZED_DF` for {key}")

freqtrade/rpc/rpc.py

@@ -167,6 +167,7 @@ class RPC:
             results = []
             for trade in trades:
                 order: Optional[Order] = None
+                current_profit_fiat: Optional[float] = None
                 if trade.open_order_id:
                     order = trade.select_order_by_order_id(trade.open_order_id)
                 # calculate profit and send message to user
@@ -176,23 +177,26 @@ class RPC:
                             trade.pair, side='exit', is_short=trade.is_short, refresh=False)
                     except (ExchangeError, PricingError):
                         current_rate = NAN
+                    if len(trade.select_filled_orders(trade.entry_side)) > 0:
+                        current_profit = trade.calc_profit_ratio(
+                            current_rate) if not isnan(current_rate) else NAN
+                        current_profit_abs = trade.calc_profit(
+                            current_rate) if not isnan(current_rate) else NAN
+                    else:
+                        current_profit = current_profit_abs = current_profit_fiat = 0.0
                 else:
+                    # Closed trade ...
                     current_rate = trade.close_rate
-                if len(trade.select_filled_orders(trade.entry_side)) > 0:
-                    current_profit = trade.calc_profit_ratio(
-                        current_rate) if not isnan(current_rate) else NAN
-                    current_profit_abs = trade.calc_profit(
-                        current_rate) if not isnan(current_rate) else NAN
-                    current_profit_fiat: Optional[float] = None
-                    # Calculate fiat profit
-                    if self._fiat_converter:
-                        current_profit_fiat = self._fiat_converter.convert_amount(
-                            current_profit_abs,
-                            self._freqtrade.config['stake_currency'],
-                            self._freqtrade.config['fiat_display_currency']
-                        )
-                else:
-                    current_profit = current_profit_abs = current_profit_fiat = 0.0
+                    current_profit = trade.close_profit
+                    current_profit_abs = trade.close_profit_abs
+
+                # Calculate fiat profit
+                if not isnan(current_profit_abs) and self._fiat_converter:
+                    current_profit_fiat = self._fiat_converter.convert_amount(
+                        current_profit_abs,
+                        self._freqtrade.config['stake_currency'],
+                        self._freqtrade.config['fiat_display_currency']
+                    )
 
                 # Calculate guaranteed profit (in case of trailing stop)
                 stoploss_entry_dist = trade.calc_profit(trade.stop_loss)
@@ -1058,15 +1062,26 @@ class RPC:
         return self._convert_dataframe_to_dict(self._freqtrade.config['strategy'],
                                                pair, timeframe, _data, last_analyzed)
 
-    def __rpc_analysed_dataframe_raw(self, pair: str, timeframe: str,
-                                     limit: Optional[int]) -> Tuple[DataFrame, datetime]:
-        """ Get the dataframe and last analyze from the dataprovider """
+    def __rpc_analysed_dataframe_raw(
+        self,
+        pair: str,
+        timeframe: str,
+        limit: Optional[int]
+    ) -> Tuple[DataFrame, datetime]:
+        """
+        Get the dataframe and last analyze from the dataprovider
+
+        :param pair: The pair to get
+        :param timeframe: The timeframe of data to get
+        :param limit: The amount of candles in the dataframe
+        """
         _data, last_analyzed = self._freqtrade.dataprovider.get_analyzed_dataframe(
             pair, timeframe)
         _data = _data.copy()
 
         if limit:
             _data = _data.iloc[-limit:]
+
         return _data, last_analyzed
 
     def _ws_all_analysed_dataframes(
@@ -1074,7 +1089,16 @@ class RPC:
         pairlist: List[str],
         limit: Optional[int]
     ) -> Generator[Dict[str, Any], None, None]:
-        """ Get the analysed dataframes of each pair in the pairlist """
+        """
+        Get the analysed dataframes of each pair in the pairlist.
+        If specified, only return the most recent `limit` candles for
+        each dataframe.
+
+        :param pairlist: A list of pairs to get
+        :param limit: If an integer, limits the size of dataframe
+                      If a list of string date times, only returns those candles
+        :returns: A generator of dictionaries with the key, dataframe, and last analyzed timestamp
+        """
         timeframe = self._freqtrade.config['timeframe']
         candle_type = self._freqtrade.config.get('candle_type_def', CandleType.SPOT)
 
@@ -1087,10 +1111,15 @@ class RPC:
                 "la": last_analyzed
             }
 
-    def _ws_request_analyzed_df(self, limit: Optional[int]):
+    def _ws_request_analyzed_df(
+        self,
+        limit: Optional[int] = None,
+        pair: Optional[str] = None
+    ):
         """ Historical Analyzed Dataframes for WebSocket """
-        whitelist = self._freqtrade.active_pair_whitelist
-        return self._ws_all_analysed_dataframes(whitelist, limit)
+        pairlist = [pair] if pair else self._freqtrade.active_pair_whitelist
+
+        return self._ws_all_analysed_dataframes(pairlist, limit)
 
     def _ws_request_whitelist(self):
         """ Whitelist data for WebSocket """

freqtrade/strategy/interface.py

@@ -598,6 +598,7 @@ class IStrategy(ABC, HyperStrategyMixin):
                                 informative: DataFrame = None,
                                 set_generalized_indicators: bool = False) -> DataFrame:
         """
+        DEPRECATED - USE FEATURE ENGINEERING FUNCTIONS INSTEAD
         Function designed to automatically generate, name and merge features
         from user indicated timeframes in the configuration file. User can add
         additional features here, but must follow the naming convention.
@@ -610,6 +611,98 @@ class IStrategy(ABC, HyperStrategyMixin):
         """
         return df
 
+    def feature_engineering_expand_all(self, dataframe: DataFrame,
+                                       period: int, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        :param period: period of the indicator - usage example:
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
+        """
+        return dataframe
+
+    def feature_engineering_expand_basic(self, dataframe: DataFrame, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
+
+        Features defined here will *not* be automatically duplicated on user defined
+        `indicator_periods_candles`
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
+        """
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe: DataFrame, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This optional function will be called once with the dataframe of the base timeframe.
+        This is the final function to be called, which means that the dataframe entering this
+        function will contain all the features and columns created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        This function is a good place for any feature that should not be auto-expanded upon
+        (e.g. day of the week).
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the features
+        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        """
+        return dataframe
+
+    def set_freqai_targets(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        Required function to set the targets for the model.
+        All targets must be prepended with `&` to be recognized by the FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the targets
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        return dataframe
+
 ###
 # END - Intended to be overridden by strategy
 ###

freqtrade/templates/FreqaiExampleHybridStrategy.py

@@ -95,65 +95,132 @@ class FreqaiExampleHybridStrategy(IStrategy):
     short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
     exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
 
-    # FreqAI required function, user can add or remove indicators, but general structure
-    # must stay the same.
-    def populate_any_indicators(
-        self, pair, df, tf, informative=None, set_generalized_indicators=False
-    ):
+    def feature_engineering_expand_all(self, dataframe, period, **kwargs):
         """
-        User feeds these indicators to FreqAI to train a classifier to decide
-        if the market will go up or down.
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
 
-        :param pair: pair to be used as informative
-        :param df: strategy dataframe which will receive merges from informatives
-        :param tf: timeframe of the dataframe which will modify the feature names
-        :param informative: the dataframe associated with the informative pair
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        :param period: period of the indicator - usage example:
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
         """
 
-        if informative is None:
-            informative = self.dp.get_pair_dataframe(pair, tf)
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
 
-        # first loop is automatically duplicating indicators for time periods
-        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
+        bollinger = qtpylib.bollinger_bands(
+            qtpylib.typical_price(dataframe), window=period, stds=2.2
+        )
+        dataframe["bb_lowerband-period"] = bollinger["lower"]
+        dataframe["bb_middleband-period"] = bollinger["mid"]
+        dataframe["bb_upperband-period"] = bollinger["upper"]
 
-            t = int(t)
-            informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
-            informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
-            informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
-            informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
-            informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
-            informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
-            informative[f"%-{pair}relative_volume-period_{t}"] = (
-                informative["volume"] / informative["volume"].rolling(t).mean()
-            )
+        dataframe["%-bb_width-period"] = (
+            dataframe["bb_upperband-period"]
+            - dataframe["bb_lowerband-period"]
+        ) / dataframe["bb_middleband-period"]
+        dataframe["%-close-bb_lower-period"] = (
+            dataframe["close"] / dataframe["bb_lowerband-period"]
+        )
 
-        # FreqAI needs the following lines in order to detect features and automatically
-        # expand upon them.
-        indicators = [col for col in informative if col.startswith("%")]
-        # This loop duplicates and shifts all indicators to add a sense of recency to data
-        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
-            if n == 0:
-                continue
-            informative_shift = informative[indicators].shift(n)
-            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
-            informative = pd.concat((informative, informative_shift), axis=1)
+        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
 
-        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
-        skip_columns = [
-            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
-        ]
-        df = df.drop(columns=skip_columns)
+        dataframe["%-relative_volume-period"] = (
+            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
+        )
 
-        # User can set the "target" here (in present case it is the
-        # "up" or "down")
-        if set_generalized_indicators:
-            # User "looks into the future" here to figure out if the future
-            # will be "up" or "down". This same column name is available to
-            # the user
-            df['&s-up_or_down'] = np.where(df["close"].shift(-50) >
-                                           df["close"], 'up', 'down')
+        return dataframe
 
-        return df
+    def feature_engineering_expand_basic(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
+
+        Features defined here will *not* be automatically duplicated on user defined
+        `indicator_periods_candles`
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
+        """
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This optional function will be called once with the dataframe of the base timeframe.
+        This is the final function to be called, which means that the dataframe entering this
+        function will contain all the features and columns created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        This function is a good place for any feature that should not be auto-expanded upon
+        (e.g. day of the week).
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the features
+        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        """
+        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
+        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
+        return dataframe
+
+    def set_freqai_targets(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        Required function to set the targets for the model.
+        All targets must be prepended with `&` to be recognized by the FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the targets
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-50) >
+                                        dataframe["close"], 'up', 'down')
+
+        return dataframe
 
     # flake8: noqa: C901
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

freqtrade/templates/FreqaiExampleStrategy.py

@@ -1,12 +1,11 @@
 import logging
 from functools import reduce
 
-import pandas as pd
 import talib.abstract as ta
 from pandas import DataFrame
 from technical import qtpylib
 
-from freqtrade.strategy import CategoricalParameter, IStrategy, merge_informative_pair
+from freqtrade.strategy import CategoricalParameter, IStrategy
 
 
 logger = logging.getLogger(__name__)
@@ -18,8 +17,8 @@ class FreqaiExampleStrategy(IStrategy):
     IFreqaiModel to the strategy. Namely, the user uses:
     self.freqai.start(dataframe, metadata)
 
-    to make predictions on their data. populate_any_indicators() automatically
-    generates the variety of features indicated by the user in the
+    to make predictions on their data. feature_engineering_*() automatically
+    generate the variety of features indicated by the user in the
     canonical freqtrade configuration file under config['freqai'].
     """
 
@@ -28,7 +27,7 @@ class FreqaiExampleStrategy(IStrategy):
     plot_config = {
         "main_plot": {},
         "subplots": {
-            "prediction": {"prediction": {"color": "blue"}},
+            "&-s_close": {"prediction": {"color": "blue"}},
             "do_predict": {
                 "do_predict": {"color": "brown"},
             },
@@ -40,133 +39,179 @@ class FreqaiExampleStrategy(IStrategy):
     use_exit_signal = True
     # this is the maximum period fed to talib (timeframe independent)
     startup_candle_count: int = 40
-    can_short = False
+    can_short = True
 
     std_dev_multiplier_buy = CategoricalParameter(
         [0.75, 1, 1.25, 1.5, 1.75], default=1.25, space="buy", optimize=True)
     std_dev_multiplier_sell = CategoricalParameter(
         [0.75, 1, 1.25, 1.5, 1.75], space="sell", default=1.25, optimize=True)
 
-    def populate_any_indicators(
-        self, pair, df, tf, informative=None, set_generalized_indicators=False
-    ):
+    def feature_engineering_expand_all(self, dataframe, period, **kwargs):
         """
-        Function designed to automatically generate, name and merge features
-        from user indicated timeframes in the configuration file. User controls the indicators
-        passed to the training/prediction by prepending indicators with `f'%-{pair}`
-        (see convention below). I.e. user should not prepend any supporting metrics
-        (e.g. bb_lowerband below) with % unless they explicitly want to pass that metric to the
-        model.
-        :param pair: pair to be used as informative
-        :param df: strategy dataframe which will receive merges from informatives
-        :param tf: timeframe of the dataframe which will modify the feature names
-        :param informative: the dataframe associated with the informative pair
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and
+        `include_corr_pairs`. In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` *
+        `include_corr_pairs` numbers of features added to the model.
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        :param period: period of the indicator - usage example:
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
         """
 
-        if informative is None:
-            informative = self.dp.get_pair_dataframe(pair, tf)
+        dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period)
+        dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period)
+        dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period)
+        dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period)
+        dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period)
 
-        # first loop is automatically duplicating indicators for time periods
-        for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
+        bollinger = qtpylib.bollinger_bands(
+            qtpylib.typical_price(dataframe), window=period, stds=2.2
+        )
+        dataframe["bb_lowerband-period"] = bollinger["lower"]
+        dataframe["bb_middleband-period"] = bollinger["mid"]
+        dataframe["bb_upperband-period"] = bollinger["upper"]
 
-            t = int(t)
-            informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
-            informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
-            informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, timeperiod=t)
-            informative[f"%-{pair}sma-period_{t}"] = ta.SMA(informative, timeperiod=t)
-            informative[f"%-{pair}ema-period_{t}"] = ta.EMA(informative, timeperiod=t)
+        dataframe["%-bb_width-period"] = (
+            dataframe["bb_upperband-period"]
+            - dataframe["bb_lowerband-period"]
+        ) / dataframe["bb_middleband-period"]
+        dataframe["%-close-bb_lower-period"] = (
+            dataframe["close"] / dataframe["bb_lowerband-period"]
+        )
 
-            bollinger = qtpylib.bollinger_bands(
-                qtpylib.typical_price(informative), window=t, stds=2.2
-            )
-            informative[f"{pair}bb_lowerband-period_{t}"] = bollinger["lower"]
-            informative[f"{pair}bb_middleband-period_{t}"] = bollinger["mid"]
-            informative[f"{pair}bb_upperband-period_{t}"] = bollinger["upper"]
+        dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
 
-            informative[f"%-{pair}bb_width-period_{t}"] = (
-                informative[f"{pair}bb_upperband-period_{t}"]
-                - informative[f"{pair}bb_lowerband-period_{t}"]
-            ) / informative[f"{pair}bb_middleband-period_{t}"]
-            informative[f"%-{pair}close-bb_lower-period_{t}"] = (
-                informative["close"] / informative[f"{pair}bb_lowerband-period_{t}"]
+        dataframe["%-relative_volume-period"] = (
+            dataframe["volume"] / dataframe["volume"].rolling(period).mean()
+        )
+
+        return dataframe
+
+    def feature_engineering_expand_basic(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This function will automatically expand the defined features on the config defined
+        `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`.
+        In other words, a single feature defined in this function
+        will automatically expand to a total of
+        `include_timeframes` * `include_shifted_candles` * `include_corr_pairs`
+        numbers of features added to the model.
+
+        Features defined here will *not* be automatically duplicated on user defined
+        `indicator_periods_candles`
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details on how these config defined parameters accelerate feature engineering
+        in the documentation at:
+
+        https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features
+
+        :param df: strategy dataframe which will receive the features
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-ema-200"] = ta.EMA(dataframe, timeperiod=200)
+        """
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        This optional function will be called once with the dataframe of the base timeframe.
+        This is the final function to be called, which means that the dataframe entering this
+        function will contain all the features and columns created by all other
+        freqai_feature_engineering_* functions.
+
+        This function is a good place to do custom exotic feature extractions (e.g. tsfresh).
+        This function is a good place for any feature that should not be auto-expanded upon
+        (e.g. day of the week).
+
+        All features must be prepended with `%` to be recognized by FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the features
+        usage example: dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7
+        """
+        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
+        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
+        return dataframe
+
+    def set_freqai_targets(self, dataframe, **kwargs):
+        """
+        *Only functional with FreqAI enabled strategies*
+        Required function to set the targets for the model.
+        All targets must be prepended with `&` to be recognized by the FreqAI internals.
+
+        More details about feature engineering available:
+
+        https://www.freqtrade.io/en/latest/freqai-feature-engineering
+
+        :param df: strategy dataframe which will receive the targets
+        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
+        """
+        dataframe["&-s_close"] = (
+            dataframe["close"]
+            .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+            .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+            .mean()
+            / dataframe["close"]
+            - 1
             )
 
-            informative[f"%-{pair}roc-period_{t}"] = ta.ROC(informative, timeperiod=t)
+        # Classifiers are typically set up with strings as targets:
+        # df['&s-up_or_down'] = np.where( df["close"].shift(-100) >
+        #                                 df["close"], 'up', 'down')
 
-            informative[f"%-{pair}relative_volume-period_{t}"] = (
-                informative["volume"] / informative["volume"].rolling(t).mean()
-            )
+        # If user wishes to use multiple targets, they can add more by
+        # appending more columns with '&'. User should keep in mind that multi targets
+        # requires a multioutput prediction model such as
+        # freqai/prediction_models/CatboostRegressorMultiTarget.py,
+        # freqtrade trade --freqaimodel CatboostRegressorMultiTarget
 
-        informative[f"%-{pair}pct-change"] = informative["close"].pct_change()
-        informative[f"%-{pair}raw_volume"] = informative["volume"]
-        informative[f"%-{pair}raw_price"] = informative["close"]
+        # df["&-s_range"] = (
+        #     df["close"]
+        #     .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+        #     .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+        #     .max()
+        #     -
+        #     df["close"]
+        #     .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
+        #     .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
+        #     .min()
+        # )
 
-        indicators = [col for col in informative if col.startswith("%")]
-        # This loop duplicates and shifts all indicators to add a sense of recency to data
-        for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
-            if n == 0:
-                continue
-            informative_shift = informative[indicators].shift(n)
-            informative_shift = informative_shift.add_suffix("_shift-" + str(n))
-            informative = pd.concat((informative, informative_shift), axis=1)
-
-        df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
-        skip_columns = [
-            (s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
-        ]
-        df = df.drop(columns=skip_columns)
-
-        # Add generalized indicators here (because in live, it will call this
-        # function to populate indicators during training). Notice how we ensure not to
-        # add them multiple times
-        if set_generalized_indicators:
-            df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
-            df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
-
-            # user adds targets here by prepending them with &- (see convention below)
-            df["&-s_close"] = (
-                df["close"]
-                .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
-                .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
-                .mean()
-                / df["close"]
-                - 1
-            )
-
-            # Classifiers are typically set up with strings as targets:
-            # df['&s-up_or_down'] = np.where( df["close"].shift(-100) >
-            #                                 df["close"], 'up', 'down')
-
-            # If user wishes to use multiple targets, they can add more by
-            # appending more columns with '&'. User should keep in mind that multi targets
-            # requires a multioutput prediction model such as
-            # templates/CatboostPredictionMultiModel.py,
-
-            # df["&-s_range"] = (
-            #     df["close"]
-            #     .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
-            #     .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
-            #     .max()
-            #     -
-            #     df["close"]
-            #     .shift(-self.freqai_info["feature_parameters"]["label_period_candles"])
-            #     .rolling(self.freqai_info["feature_parameters"]["label_period_candles"])
-            #     .min()
-            # )
-
-        return df
+        return dataframe
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
 
-        # All indicators must be populated by populate_any_indicators() for live functionality
-        # to work correctly.
+        # All indicators must be populated by feature_engineering_*() functions
 
-        # the model will return all labels created by user in `populate_any_indicators`
+        # the model will return all labels created by user in `feature_engineering_*`
         # (& appended targets), an indication of whether or not the prediction should be accepted,
         # the target mean/std values for each of the labels created by user in
-        # `populate_any_indicators()` for each training period.
+        # `set_freqai_targets()` for each training period.
 
         dataframe = self.freqai.start(dataframe, metadata, self)
+
         for val in self.std_dev_multiplier_buy.range:
             dataframe[f'target_roi_{val}'] = (
                 dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * val

freqtrade/templates/strategy_analysis_example.ipynb

@@ -7,14 +7,17 @@
     "# Strategy analysis example\n",
     "\n",
     "Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data.\n",
-    "The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location."
+    "The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location.\n",
+    "Please follow the [documentation](https://www.freqtrade.io/en/stable/data-download/) for more details."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setup"
+    "## Setup\n",
+    "\n",
+    "### Change Working directory to repository root"
    ]
   },
   {
@@ -23,7 +26,38 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import os\n",
     "from pathlib import Path\n",
+    "\n",
+    "# Change directory\n",
+    "# Modify this cell to insure that the output shows the correct path.\n",
+    "# Define all paths relative to the project root shown in the cell output\n",
+    "project_root = \"somedir/freqtrade\"\n",
+    "i=0\n",
+    "try:\n",
+    "    os.chdirdir(project_root)\n",
+    "    assert Path('LICENSE').is_file()\n",
+    "except:\n",
+    "    while i<4 and (not Path('LICENSE').is_file()):\n",
+    "        os.chdir(Path(Path.cwd(), '../'))\n",
+    "        i+=1\n",
+    "    project_root = Path.cwd()\n",
+    "print(Path.cwd())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Configure Freqtrade environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "from freqtrade.configuration import Configuration\n",
     "\n",
     "# Customize these according to your needs.\n",
@@ -31,14 +65,14 @@
     "# Initialize empty configuration object\n",
     "config = Configuration.from_files([])\n",
     "# Optionally (recommended), use existing configuration file\n",
-    "# config = Configuration.from_files([\"config.json\"])\n",
+    "# config = Configuration.from_files([\"user_data/config.json\"])\n",
     "\n",
     "# Define some constants\n",
     "config[\"timeframe\"] = \"5m\"\n",
     "# Name of the strategy class\n",
     "config[\"strategy\"] = \"SampleStrategy\"\n",
     "# Location of the data\n",
-    "data_location = config['datadir']\n",
+    "data_location = config[\"datadir\"]\n",
     "# Pair to analyze - Only use one pair here\n",
     "pair = \"BTC/USDT\""
    ]
@@ -56,12 +90,12 @@
     "candles = load_pair_history(datadir=data_location,\n",
     "                            timeframe=config[\"timeframe\"],\n",
     "                            pair=pair,\n",
-    "                            data_format = \"hdf5\",\n",
+    "                            data_format = \"json\",  # Make sure to update this to your data\n",
     "                            candle_type=CandleType.SPOT,\n",
     "                            )\n",
     "\n",
     "# Confirm success\n",
-    "print(\"Loaded \" + str(len(candles)) + f\" rows of data for {pair} from {data_location}\")\n",
+    "print(f\"Loaded {len(candles)} rows of data for {pair} from {data_location}\")\n",
     "candles.head()"
    ]
   },
@@ -365,7 +399,7 @@
  "metadata": {
   "file_extension": ".py",
   "kernelspec": {
-   "display_name": "Python 3.9.7 64-bit ('trade_397')",
+   "display_name": "Python 3.9.7 64-bit",
    "language": "python",
    "name": "python3"
   },

freqtrade/wallets.py

@@ -291,12 +291,17 @@ class Wallets:
         return self._check_available_stake_amount(stake_amount, available_amount)
 
     def validate_stake_amount(self, pair: str, stake_amount: Optional[float],
-                              min_stake_amount: Optional[float], max_stake_amount: float):
+                              min_stake_amount: Optional[float], max_stake_amount: float,
+                              trade_amount: Optional[float]):
         if not stake_amount:
             logger.debug(f"Stake amount is {stake_amount}, ignoring possible trade for {pair}.")
             return 0
 
         max_stake_amount = min(max_stake_amount, self.get_available_stake_amount())
+        if trade_amount:
+            # if in a trade, then the resulting trade size cannot go beyond the max stake
+            # Otherwise we could no longer exit.
+            max_stake_amount = min(max_stake_amount, max_stake_amount - trade_amount)
 
         if min_stake_amount is not None and min_stake_amount > max_stake_amount:
             if self._log: