Merge branch 'develop' into pr/richardjozsa/8336

docs/freqai-parameter-table.md

@@ -85,6 +85,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `net_arch` | Network architecture which is well described in [`stable_baselines3` doc](https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html#examples). In summary: `[<shared layers>, dict(vf=[<non-shared value network layers>], pi=[<non-shared policy network layers>])]`. By default this is set to `[128, 128]`, which defines 2 shared hidden layers with 128 units each.
 | `randomize_starting_position` | Randomize the starting point of each episode to avoid overfitting. <br> **Datatype:** bool. <br> Default: `False`.
 | `drop_ohlc_from_features` | Do not include the normalized ohlc data in the feature set passed to the agent during training (ohlc will still be used for driving the environment in all cases) <br> **Datatype:** Boolean. <br> **Default:** `False`
+| `progress_bar` | Display a progress bar with the current progress, elapsed time and estimated remaining time. <br> **Datatype:** Boolean. <br> Default: `False`.
 
 ### PyTorch parameters
 

freqtrade/constants.py

@@ -600,6 +600,7 @@ CONF_SCHEMA = {
                         "policy_type": {"type": "string", "default": "MlpPolicy"},
                         "net_arch": {"type": "array", "default": [128, 128]},
                         "randomize_starting_position": {"type": "boolean", "default": False},
+                        "progress_bar": {"type": "boolean", "default": True},
                         "model_reward_parameters": {
                             "type": "object",
                             "properties": {

freqtrade/exchange/__init__.py

@@ -6,6 +6,7 @@ from freqtrade.exchange.exchange import Exchange
 from freqtrade.exchange.binance import Binance
 from freqtrade.exchange.bitpanda import Bitpanda
 from freqtrade.exchange.bittrex import Bittrex
+from freqtrade.exchange.bitvavo import Bitvavo
 from freqtrade.exchange.bybit import Bybit
 from freqtrade.exchange.coinbasepro import Coinbasepro
 from freqtrade.exchange.exchange_utils import (ROUND_DOWN, ROUND_UP, amount_to_contract_precision,

freqtrade/exchange/bitvavo.py

@@ -0,0 +1,23 @@
+"""Kucoin exchange subclass."""
+import logging
+from typing import Dict
+
+from freqtrade.exchange import Exchange
+
+
+logger = logging.getLogger(__name__)
+
+
+class Bitvavo(Exchange):
+    """Bitvavo 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.
+    """
+
+    _ft_has: Dict = {
+        "ohlcv_candle_limit": 1440,
+    }

freqtrade/exchange/exchange.py

@@ -2371,12 +2371,12 @@ class Exchange:
                 # Must fetch the leverage tiers for each market separately
                 # * This is slow(~45s) on Okx, makes ~90 api calls to load all linear swap markets
                 markets = self.markets
-                symbols = []
 
-                for symbol, market in markets.items():
+                symbols = [
+                    symbol for symbol, market in markets.items()
                     if (self.market_is_future(market)
-                            and market['quote'] == self._config['stake_currency']):
-                        symbols.append(symbol)
+                        and market['quote'] == self._config['stake_currency'])
+                ]
 
                 tiers: Dict[str, List[Dict]] = {}
 
@@ -2396,25 +2396,26 @@ class Exchange:
                 else:
                     logger.info("Using cached leverage_tiers.")
 
-                async def gather_results():
+                async def gather_results(input_coro):
                     return await asyncio.gather(*input_coro, return_exceptions=True)
 
                 for input_coro in chunks(coros, 100):
 
                     with self._loop_lock:
-                        results = self.loop.run_until_complete(gather_results())
+                        results = self.loop.run_until_complete(gather_results(input_coro))
 
-                    for symbol, res in results:
-                        tiers[symbol] = res
+                    for res in results:
+                        if isinstance(res, Exception):
+                            logger.warning(f"Leverage tier exception: {repr(res)}")
+                            continue
+                        symbol, tier = res
+                        tiers[symbol] = tier
                 if len(coros) > 0:
                     self.cache_leverage_tiers(tiers, self._config['stake_currency'])
                 logger.info(f"Done initializing {len(symbols)} markets.")
 
                 return tiers
-            else:
-                return {}
-        else:
-            return {}
+        return {}
 
     def cache_leverage_tiers(self, tiers: Dict[str, List[Dict]], stake_currency: str) -> None:
 
@@ -2430,14 +2431,17 @@ class Exchange:
     def load_cached_leverage_tiers(self, stake_currency: str) -> Optional[Dict[str, List[Dict]]]:
         filename = self._config['datadir'] / "futures" / f"leverage_tiers_{stake_currency}.json"
         if filename.is_file():
-            tiers = file_load_json(filename)
-            updated = tiers.get('updated')
-            if updated:
-                updated_dt = parser.parse(updated)
-                if updated_dt < datetime.now(timezone.utc) - timedelta(weeks=4):
-                    logger.info("Cached leverage tiers are outdated. Will update.")
-                    return None
-            return tiers['data']
+            try:
+                tiers = file_load_json(filename)
+                updated = tiers.get('updated')
+                if updated:
+                    updated_dt = parser.parse(updated)
+                    if updated_dt < datetime.now(timezone.utc) - timedelta(weeks=4):
+                        logger.info("Cached leverage tiers are outdated. Will update.")
+                        return None
+                return tiers['data']
+            except Exception:
+                logger.exception("Error loading cached leverage tiers. Refreshing.")
         return None
 
     def fill_leverage_tiers(self) -> None:

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -71,7 +71,8 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
 
         model.learn(
             total_timesteps=int(total_timesteps),
-            callback=[self.eval_callback, self.tensorboard_callback]
+            callback=[self.eval_callback, self.tensorboard_callback],
+            progress_bar=self.rl_config.get('progress_bar', False)
         )
 
         if Path(dk.data_path / "best_model.zip").is_file():

freqtrade/optimize/optimize_reports.py

@@ -273,7 +273,8 @@ def _get_resample_from_period(period: str) -> str:
     if period == 'day':
         return '1d'
     if period == 'week':
-        return '1w'
+        # Weekly defaulting to Monday.
+        return '1W-MON'
     if period == 'month':
         return '1M'
     raise ValueError(f"Period {period} is not supported.")

freqtrade/rpc/api_server/api_v1.py

@@ -303,11 +303,11 @@ 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(
+    models = FreqaiModelResolver.search_all_objects(
         config, False)
-    strategies = sorted(strategies, key=lambda x: x['name'])
+    models = sorted(models, key=lambda x: x['name'])
 
-    return {'freqaimodels': [x['name'] for x in strategies]}
+    return {'freqaimodels': [x['name'] for x in models]}
 
 
 @router.get('/available_pairs', response_model=AvailablePairs, tags=['candle data'])

pyproject.toml

@@ -68,6 +68,9 @@ target-version = "py38"
 extend-select = [
   "C90",    # mccabe
   # "N",    # pep8-naming
+  "F",      # pyflakes
+  "E",      # pycodestyle
+  "W",      # pycodestyle
   "UP",     # pyupgrade
   "TID",    # flake8-tidy-imports
   # "EXE",  # flake8-executable

requirements-dev.txt

@@ -10,7 +10,7 @@ coveralls==3.3.1
 ruff==0.0.261
 mypy==1.2.0
 pre-commit==3.2.2
-pytest==7.3.0
+pytest==7.3.1
 pytest-asyncio==0.21.0
 pytest-cov==4.0.0
 pytest-mock==3.10.0
@@ -19,7 +19,7 @@ isort==5.12.0
 # For datetime mocking
 time-machine==2.9.0
 # fastapi testing
-httpx==0.23.3
+httpx==0.24.0
 
 # Convert jupyter notebooks to markdown documents
 nbconvert==7.3.1

requirements-freqai-rl.txt

@@ -5,8 +5,9 @@
 torch==1.13.1; python_version < '3.11'
 #until these branches will be released we can use this
 gymnasium==0.28.1
-stable_baselines3>=2.0.0a1
-sb3_contrib>=2.0.0a1 
+stable_baselines3==2.0.0a5
+sb3_contrib>=2.0.0a4
 # Gym is forced to this version by stable-baselines3.
 setuptools==65.5.1 # Should be removed when gym is fixed.
-
+# Progress bar for stable-baselines3 and sb3-contrib
+tqdm==4.65.0; python_version < '3.11'

requirements-freqai.txt

@@ -8,4 +8,4 @@ joblib==1.2.0
 catboost==1.1.1; platform_machine != 'aarch64' and 'arm' not in platform_machine and python_version < '3.11'
 lightgbm==3.3.5
 xgboost==1.7.5
-tensorboard==2.12.1
+tensorboard==2.12.2

requirements.txt

@@ -2,8 +2,8 @@ numpy==1.24.2
 pandas==1.5.3
 pandas-ta==0.3.14b
 
-ccxt==3.0.59
-cryptography==40.0.1
+ccxt==3.0.69
+cryptography==40.0.2
 aiohttp==3.8.4
 SQLAlchemy==2.0.9
 python-telegram-bot==13.15
@@ -20,7 +20,7 @@ jinja2==3.1.2
 tables==3.8.0
 blosc==1.11.1
 joblib==1.2.0
-rich==13.3.3
+rich==13.3.4
 pyarrow==11.0.0; platform_machine != 'armv7l'
 
 # find first, C search in arrays
@@ -35,7 +35,7 @@ orjson==3.8.10
 sdnotify==0.3.2
 
 # API Server
-fastapi==0.95.0
+fastapi==0.95.1
 pydantic==1.10.7
 uvicorn==0.21.1
 pyjwt==2.6.0

tests/exchange/test_ccxt_compat.py

@@ -528,9 +528,11 @@ class TestCCXTExchange():
             assert res[1] == timeframe
             assert res[2] == candle_type
             candles = res[3]
-            candle_count = exchange.ohlcv_candle_limit(timeframe, candle_type, since_ms) * 0.9
-            candle_count1 = (now.timestamp() * 1000 - since_ms) // timeframe_ms
-            assert len(candles) >= min(candle_count, candle_count1)
+            factor = 0.9
+            candle_count = exchange.ohlcv_candle_limit(timeframe, candle_type, since_ms) * factor
+            candle_count1 = (now.timestamp() * 1000 - since_ms) // timeframe_ms * factor
+            assert len(candles) >= min(candle_count, candle_count1), \
+                f"{len(candles)} < {candle_count} in {timeframe}, Offset: {offset} {factor}"
             assert candles[0][0] == since_ms or (since_ms + timeframe_ms)
 
     def test_ccxt__async_get_candle_history(self, exchange: EXCHANGE_FIXTURE_TYPE):

tests/exchange/test_okx.py

@@ -1,14 +1,14 @@
 from datetime import datetime, timedelta, timezone
 from pathlib import Path
-from unittest.mock import MagicMock, PropertyMock
+from unittest.mock import AsyncMock, MagicMock, PropertyMock
 
 import ccxt
 import pytest
 
 from freqtrade.enums import CandleType, MarginMode, TradingMode
-from freqtrade.exceptions import RetryableOrderError
+from freqtrade.exceptions import RetryableOrderError, TemporaryError
 from freqtrade.exchange.exchange import timeframe_to_minutes
-from tests.conftest import EXMS, get_mock_coro, get_patched_exchange, log_has
+from tests.conftest import EXMS, get_patched_exchange, log_has
 from tests.exchange.test_exchange import ccxt_exceptionhandlers
 
 
@@ -278,7 +278,7 @@ def test_load_leverage_tiers_okx(default_conf, mocker, markets, tmpdir, caplog,
         'fetchLeverageTiers': False,
         'fetchMarketLeverageTiers': True,
     })
-    api_mock.fetch_market_leverage_tiers = get_mock_coro(side_effect=[
+    api_mock.fetch_market_leverage_tiers = AsyncMock(side_effect=[
         [
             {
                 'tier': 1,
@@ -341,6 +341,7 @@ def test_load_leverage_tiers_okx(default_conf, mocker, markets, tmpdir, caplog,
                 }
             },
         ],
+        TemporaryError("this Failed"),
         [
             {
                 'tier': 1,

tests/optimize/test_optimize_reports.py

@@ -465,7 +465,7 @@ def test_generate_periodic_breakdown_stats(testdatadir):
 def test__get_resample_from_period():
 
     assert _get_resample_from_period('day') == '1d'
-    assert _get_resample_from_period('week') == '1w'
+    assert _get_resample_from_period('week') == '1W-MON'
     assert _get_resample_from_period('month') == '1M'
     with pytest.raises(ValueError, match=r"Period noooo is not supported."):
         _get_resample_from_period('noooo')