Merge branch 'freqtrade:develop' into develop

docs/configuration.md

@@ -11,7 +11,7 @@ Per default, the bot loads the configuration from the `config.json` file, locate
 
 You can specify a different configuration file used by the bot with the `-c/--config` command-line option.
 
-If you used the [Quick start](installation.md/#quick-start) method for installing
+If you used the [Quick start](docker_quickstart.md#docker-quick-start) method for installing
 the bot, the installation script should have already created the default configuration file (`config.json`) for you.
 
 If the default configuration file is not created we recommend to use `freqtrade new-config --config config.json` to generate a basic configuration file.

docs/freqai-reinforcement-learning.md

@@ -58,11 +58,12 @@ where `ReinforcementLearner` will use the templated `ReinforcementLearner` from
 Most of the function remains the same as for typical Regressors, however, the function above shows how the strategy must pass the raw price data to the agent so that it has access to raw OHLCV in the training environment:
 
 ```python
+    def feature_engineering_standard():
         # The following features are necessary for RL models
-        informative[f"%-{pair}raw_close"] = informative["close"]
-        informative[f"%-{pair}raw_open"] = informative["open"]
-        informative[f"%-{pair}raw_high"] = informative["high"]
-        informative[f"%-{pair}raw_low"] = informative["low"]
+        informative[f"%-raw_close"] = informative["close"]
+        informative[f"%-raw_open"] = informative["open"]
+        informative[f"%-raw_high"] = informative["high"]
+        informative[f"%-raw_low"] = informative["low"]
 ```
 
 Finally, there is no explicit "label" to make - instead it is necessary to assign the `&-action` column which will contain the agent's actions when accessed in `populate_entry/exit_trends()`. In the present example, the neutral action to 0. This value should align with the environment used. FreqAI provides two environments, both use 0 as the neutral action.

freqtrade/__init__.py

@@ -2,18 +2,19 @@
 __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/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')

tests/exchange/test_ccxt_compat.py

@@ -77,8 +77,8 @@ EXCHANGES = {
         'leverage_in_spot_market': True,
     },
     'huobi': {
-        'pair': 'BTC/USDT',
-        'stake_currency': 'USDT',
+        'pair': 'ETH/BTC',
+        'stake_currency': 'BTC',
         'hasQuoteVolume': True,
         'timeframe': '5m',
         'futures': False,

tests/test_misc.py

@@ -5,6 +5,7 @@ from copy import deepcopy
 from pathlib import Path
 from unittest.mock import MagicMock
 
+import pandas as pd
 import pytest
 
 from freqtrade.misc import (dataframe_to_json, decimals_per_coin, deep_merge_dicts, file_dump_json,
@@ -231,3 +232,7 @@ def test_dataframe_json(ohlcv_history):
     assert len(ohlcv_history) == len(dataframe)
 
     assert_frame_equal(ohlcv_history, dataframe)
+    ohlcv_history.at[1, 'date'] = pd.NaT
+    json = dataframe_to_json(ohlcv_history)
+
+    dataframe = json_to_dataframe(json)