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Merge branch 'freqtrade:develop' into backtest_bias_tester

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This commit is contained in:
hippocritical
2023-03-10 12:41:41 +01:00
committed by GitHub
parent 4411ab112a 5b2a291109
commit e0d8ce877a
30 changed files with 150 additions and 86 deletions
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1
freqtrade/constants.py
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@@ -588,6 +588,7 @@ CONF_SCHEMA = {
"rl_config": {
"type": "object",
"properties": {
"drop_ohlc_from_features": {"type": "boolean", "default": False},
"train_cycles": {"type": "integer"},
"max_trade_duration_candles": {"type": "integer"},
"add_state_info": {"type": "boolean", "default": False},

1
freqtrade/exchange/exchange.py
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@@ -69,6 +69,7 @@ class Exchange:
# Check https://github.com/ccxt/ccxt/issues/10767 for removal of ohlcv_volume_currency
"ohlcv_volume_currency": "base", # "base" or "quote"
"tickers_have_quoteVolume": True,
"tickers_have_bid_ask": True, # bid / ask empty for fetch_tickers
"tickers_have_price": True,
"trades_pagination": "time", # Possible are "time" or "id"
"trades_pagination_arg": "since",

1
freqtrade/exchange/gate.py
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@@ -32,6 +32,7 @@ class Gate(Exchange):
_ft_has_futures: Dict = {
"needs_trading_fees": True,
"tickers_have_bid_ask": False,
"fee_cost_in_contracts": False, # Set explicitly to false for clarity
"order_props_in_contracts": ['amount', 'filled', 'remaining'],
"stop_price_type_field": "price_type",

29
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
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@@ -114,6 +114,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
# normalize all data based on train_dataset only
prices_train, prices_test = self.build_ohlc_price_dataframes(dk.data_dictionary, pair, dk)
data_dictionary = dk.normalize_data(data_dictionary)
# data cleaning/analysis
@@ -148,12 +149,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
env_info = self.pack_env_dict(dk.pair)
self.train_env = self.MyRLEnv(df=train_df,
prices=prices_train,
**env_info)
self.eval_env = Monitor(self.MyRLEnv(df=test_df,
prices=prices_test,
**env_info))
self.train_env = self.MyRLEnv(df=train_df, prices=prices_train, **env_info)
self.eval_env = Monitor(self.MyRLEnv(df=test_df, prices=prices_test, **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))
@@ -238,6 +235,9 @@ class BaseReinforcementLearningModel(IFreqaiModel):
filtered_dataframe, _ = dk.filter_features(
unfiltered_df, dk.training_features_list, training_filter=False
)
filtered_dataframe = self.drop_ohlc_from_df(filtered_dataframe, dk)
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe
@@ -285,7 +285,6 @@ 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']
rename_dict = {'%-raw_open': 'open', '%-raw_low': 'low',
@@ -318,8 +317,24 @@ class BaseReinforcementLearningModel(IFreqaiModel):
prices_test.rename(columns=rename_dict, inplace=True)
prices_test.reset_index(drop=True)
train_df = self.drop_ohlc_from_df(train_df, dk)
test_df = self.drop_ohlc_from_df(test_df, dk)
return prices_train, prices_test
def drop_ohlc_from_df(self, df: DataFrame, dk: FreqaiDataKitchen):
"""
Given a dataframe, drop the ohlc data
"""
drop_list = ['%-raw_open', '%-raw_low', '%-raw_high', '%-raw_close']
if self.rl_config["drop_ohlc_from_features"]:
df.drop(drop_list, axis=1, inplace=True)
feature_list = dk.training_features_list
dk.training_features_list = [e for e in feature_list if e not in drop_list]
return df
def load_model_from_disk(self, dk: FreqaiDataKitchen) -> Any:
"""
Can be used by user if they are trying to limit_ram_usage *and*

5
freqtrade/persistence/pairlock.py
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@@ -2,8 +2,7 @@ from datetime import datetime, timezone
from typing import Any, ClassVar, Dict, Optional
from sqlalchemy import String, or_
from sqlalchemy.orm import Mapped, Query, mapped_column
from sqlalchemy.orm.scoping import _QueryDescriptorType
from sqlalchemy.orm import Mapped, Query, QueryPropertyDescriptor, mapped_column
from freqtrade.constants import DATETIME_PRINT_FORMAT
from freqtrade.persistence.base import ModelBase, SessionType
@@ -14,7 +13,7 @@ class PairLock(ModelBase):
Pair Locks database model.
"""
__tablename__ = 'pairlocks'
query: ClassVar[_QueryDescriptorType]
query: ClassVar[QueryPropertyDescriptor]
_session: ClassVar[SessionType]
id: Mapped[int] = mapped_column(primary_key=True)

8
freqtrade/persistence/trade_model.py
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@@ -8,8 +8,8 @@ from math import isclose
from typing import Any, ClassVar, Dict, List, Optional, cast
from sqlalchemy import Enum, Float, ForeignKey, Integer, String, UniqueConstraint, desc, func
from sqlalchemy.orm import Mapped, Query, lazyload, mapped_column, relationship
from sqlalchemy.orm.scoping import _QueryDescriptorType
from sqlalchemy.orm import (Mapped, Query, QueryPropertyDescriptor, lazyload, mapped_column,
relationship)
from freqtrade.constants import (DATETIME_PRINT_FORMAT, MATH_CLOSE_PREC, NON_OPEN_EXCHANGE_STATES,
BuySell, LongShort)
@@ -36,7 +36,7 @@ class Order(ModelBase):
Mirrors CCXT Order structure
"""
__tablename__ = 'orders'
query: ClassVar[_QueryDescriptorType]
query: ClassVar[QueryPropertyDescriptor]
_session: ClassVar[SessionType]
# Uniqueness should be ensured over pair, order_id
@@ -1181,7 +1181,7 @@ class Trade(ModelBase, LocalTrade):
Note: Fields must be aligned with LocalTrade class
"""
__tablename__ = 'trades'
query: ClassVar[_QueryDescriptorType]
query: ClassVar[QueryPropertyDescriptor]
_session: ClassVar[SessionType]
use_db: bool = True

7
freqtrade/plugins/pairlist/SpreadFilter.py
View File

@@ -5,6 +5,7 @@ import logging
from typing import Any, Dict, Optional
from freqtrade.constants import Config
from freqtrade.exceptions import OperationalException
from freqtrade.exchange.types import Ticker
from freqtrade.plugins.pairlist.IPairList import IPairList
@@ -22,6 +23,12 @@ class SpreadFilter(IPairList):
self._max_spread_ratio = pairlistconfig.get('max_spread_ratio', 0.005)
self._enabled = self._max_spread_ratio != 0
if not self._exchange.get_option('tickers_have_bid_ask'):
raise OperationalException(
f"{self.name} requires exchange to have bid/ask data for tickers, "
"which is not available for the selected exchange / trading mode."
)
@property
def needstickers(self) -> bool:
"""

1
freqtrade/rpc/api_server/api_schemas.py
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@@ -286,6 +286,7 @@ class OpenTradeSchema(TradeSchema):
current_rate: float
total_profit_abs: float
total_profit_fiat: Optional[float]
total_profit_ratio: Optional[float]
open_order: Optional[str]

3
freqtrade/rpc/api_server/api_v1.py
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@@ -42,7 +42,8 @@ logger = logging.getLogger(__name__)
# 2.22: Add FreqAI to backtesting
# 2.23: Allow plot config request in webserver mode
# 2.24: Add cancel_open_order endpoint
API_VERSION = 2.24
# 2.25: Add several profit values to /status endpoint
API_VERSION = 2.25
# Public API, requires no auth.
router_public = APIRouter()

6
freqtrade/rpc/rpc.py
View File

@@ -192,6 +192,11 @@ class RPC:
current_profit = trade.close_profit or 0.0
current_profit_abs = trade.close_profit_abs or 0.0
total_profit_abs = trade.realized_profit + current_profit_abs
total_profit_ratio: Optional[float] = None
if trade.max_stake_amount:
total_profit_ratio = (
(total_profit_abs / trade.max_stake_amount) * trade.leverage
)
# Calculate fiat profit
if not isnan(current_profit_abs) and self._fiat_converter:
@@ -224,6 +229,7 @@ class RPC:
total_profit_abs=total_profit_abs,
total_profit_fiat=total_profit_fiat,
total_profit_ratio=total_profit_ratio,
stoploss_current_dist=stoploss_current_dist,
stoploss_current_dist_ratio=round(stoploss_current_dist_ratio, 8),
stoploss_current_dist_pct=round(stoploss_current_dist_ratio * 100, 2),

24
freqtrade/rpc/telegram.py
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@@ -510,14 +510,14 @@ class Telegram(RPCHandler):
if prev_avg_price:
minus_on_entry = (cur_entry_average - prev_avg_price) / prev_avg_price
lines.append(f"*{wording} #{order_nr}:* at {minus_on_entry:.2%} avg profit")
lines.append(f"*{wording} #{order_nr}:* at {minus_on_entry:.2%} avg Profit")
if is_open:
lines.append("({})".format(cur_entry_datetime
.humanize(granularity=["day", "hour", "minute"])))
lines.append(f"*Amount:* {cur_entry_amount} "
f"({round_coin_value(order['cost'], quote_currency)})")
lines.append(f"*Average {wording} Price:* {cur_entry_average} "
f"({price_to_1st_entry:.2%} from 1st entry rate)")
f"({price_to_1st_entry:.2%} from 1st entry Rate)")
lines.append(f"*Order filled:* {order['order_filled_date']}")
# TODO: is this really useful?
@@ -569,6 +569,8 @@ class Telegram(RPCHandler):
and not o['ft_order_side'] == 'stoploss'])
r['exit_reason'] = r.get('exit_reason', "")
r['stake_amount_r'] = round_coin_value(r['stake_amount'], r['quote_currency'])
r['max_stake_amount_r'] = round_coin_value(
r['max_stake_amount'] or r['stake_amount'], r['quote_currency'])
r['profit_abs_r'] = round_coin_value(r['profit_abs'], r['quote_currency'])
r['realized_profit_r'] = round_coin_value(r['realized_profit'], r['quote_currency'])
r['total_profit_abs_r'] = round_coin_value(
@@ -580,31 +582,37 @@ class Telegram(RPCHandler):
f"*Direction:* {'`Short`' if r.get('is_short') else '`Long`'}"
+ " ` ({leverage}x)`" if r.get('leverage') else "",
"*Amount:* `{amount} ({stake_amount_r})`",
"*Total invested:* `{max_stake_amount_r}`" if position_adjust else "",
"*Enter Tag:* `{enter_tag}`" if r['enter_tag'] else "",
"*Exit Reason:* `{exit_reason}`" if r['exit_reason'] else "",
]
if position_adjust:
max_buy_str = (f"/{max_entries + 1}" if (max_entries > 0) else "")
lines.append("*Number of Entries:* `{num_entries}" + max_buy_str + "`")
lines.append("*Number of Exits:* `{num_exits}`")
lines.extend([
"*Number of Entries:* `{num_entries}" + max_buy_str + "`",
"*Number of Exits:* `{num_exits}`"
])
lines.extend([
"*Open Rate:* `{open_rate:.8f}`",
"*Close Rate:* `{close_rate:.8f}`" if r['close_rate'] else "",
"*Open Date:* `{open_date}`",
"*Close Date:* `{close_date}`" if r['close_date'] else "",
"*Current Rate:* `{current_rate:.8f}`" if r['is_open'] else "",
" \n*Current Rate:* `{current_rate:.8f}`" if r['is_open'] else "",
("*Unrealized Profit:* " if r['is_open'] else "*Close Profit: *")
+ "`{profit_ratio:.2%}` `({profit_abs_r})`",
])
if r['is_open']:
if r.get('realized_profit'):
lines.append(
"*Realized Profit:* `{realized_profit_r} {realized_profit_ratio:.2%}`")
lines.append("*Total Profit:* `{total_profit_abs_r}` ")
lines.extend([
"*Realized Profit:* `{realized_profit_ratio:.2%} ({realized_profit_r})`",
"*Total Profit:* `{total_profit_ratio:.2%} ({total_profit_abs_r})`"
])
# Append empty line to improve readability
lines.append(" ")
if (r['stop_loss_abs'] != r['initial_stop_loss_abs']
and r['initial_stop_loss_ratio'] is not None):
# Adding initial stoploss only if it is different from stoploss

20
freqtrade/strategy/strategy_helper.py
View File

@@ -86,37 +86,41 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
def stoploss_from_open(
open_relative_stop: float,
current_profit: float,
is_short: bool = False
is_short: bool = False,
leverage: float = 1.0
) -> float:
"""
Given the current profit, and a desired stop loss value relative to the open price,
Given the current profit, and a desired stop loss value relative to the trade entry price,
return a stop loss value that is relative to the current price, and which can be
returned from `custom_stoploss`.
The requested stop can be positive for a stop above the open price, or negative for
a stop below the open price. The return value is always >= 0.
`open_relative_stop` will be considered as adjusted for leverage if leverage is provided..
Returns 0 if the resulting stop price would be above/below (longs/shorts) the current price
:param open_relative_stop: Desired stop loss percentage relative to open price
:param open_relative_stop: Desired stop loss percentage, relative to the open price,
adjusted for leverage
:param current_profit: The current profit percentage
:param is_short: When true, perform the calculation for short instead of long
:param leverage: Leverage to use for the calculation
:return: Stop loss value relative to current price
"""
# formula is undefined for current_profit -1 (longs) or 1 (shorts), return maximum value
if (current_profit == -1 and not is_short) or (is_short and current_profit == 1):
_current_profit = current_profit / leverage
if (_current_profit == -1 and not is_short) or (is_short and _current_profit == 1):
return 1
if is_short is True:
stoploss = -1 + ((1 - open_relative_stop) / (1 - current_profit))
stoploss = -1 + ((1 - open_relative_stop / leverage) / (1 - _current_profit))
else:
stoploss = 1 - ((1 + open_relative_stop) / (1 + current_profit))
stoploss = 1 - ((1 + open_relative_stop / leverage) / (1 + _current_profit))
# negative stoploss values indicate the requested stop price is higher/lower
# (long/short) than the current price
return max(stoploss, 0.0)
return max(stoploss * leverage, 0.0)
def stoploss_from_absolute(stop_rate: float, current_rate: float, is_short: bool = False) -> float: