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add live rate, add trade duration

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This commit is contained in:
robcaulk 2022-08-23 09:44:44 +02:00
parent f9a49744e6
commit 280a1dc3f8
2 changed files with 24 additions and 11 deletions
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13
freqtrade/freqai/RL/Base5ActionRLEnv.py
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@ -62,7 +62,7 @@ class Base5ActionRLEnv(gym.Env):
self.fee = 0.0015
# # spaces
self.shape = (window_size, self.signal_features.shape[1] + 2)
self.shape = (window_size, self.signal_features.shape[1] + 3)
self.action_space = spaces.Discrete(len(Actions))
self.observation_space = spaces.Box(
low=-np.inf, high=np.inf, shape=self.shape, dtype=np.float32)
@ -184,15 +184,22 @@ class Base5ActionRLEnv(gym.Env):
def _get_observation(self):
features_window = self.signal_features[(
self._current_tick - self.window_size):self._current_tick]
features_and_state = DataFrame(np.zeros((len(features_window), 2)),
columns=['current_profit_pct', 'position'],
features_and_state = DataFrame(np.zeros((len(features_window), 3)),
columns=['current_profit_pct', 'position', 'trade_duration'],
index=features_window.index)
features_and_state['current_profit_pct'] = self.get_unrealized_profit()
features_and_state['position'] = self._position.value
features_and_state['trade_duration'] = self.get_trade_duration()
features_and_state = pd.concat([features_window, features_and_state], axis=1)
return features_and_state
def get_trade_duration(self):
if self._last_trade_tick is None:
return 0
else:
return self._current_tick - self._last_trade_tick
def get_unrealized_profit(self):
if self._last_trade_tick is None:

22
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
View File

@ -16,6 +16,7 @@ from stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.monitor import Monitor
import torch as th
from typing import Callable
from datetime import datetime, timezone
from stable_baselines3.common.utils import set_random_seed
import gym
logger = logging.getLogger(__name__)
@ -140,23 +141,27 @@ class BaseReinforcementLearningModel(IFreqaiModel):
open_trades = Trade.get_trades_proxy(is_open=True)
market_side = 0.5
current_profit = 0
trade_duration = 0
for trade in open_trades:
if trade.pair == pair:
current_value = self.strategy.dp._exchange.get_rate(pair, refresh=False) #, side="buy", is_short=True)
current_value = self.strategy.dp._exchange.get_rate(
pair, refresh=False, side="exit", is_short=trade.is_short)
openrate = trade.open_rate
now = datetime.now(timezone.utc).timestamp()
trade_duration = (now - trade.open_date.timestamp()) / self.base_tf_seconds
if 'long' in trade.enter_tag:
market_side = 1
current_profit = (current_value - openrate) / openrate
else:
market_side = 0
current_profit = (openrate - current_value ) / openrate
current_profit = (openrate - current_value) / openrate
total_profit = 0
closed_trades = Trade.get_trades_proxy(pair=pair, is_open=False)
for trade in closed_trades:
total_profit += trade.close_profit
# total_profit = 0
# closed_trades = Trade.get_trades_proxy(pair=pair, is_open=False)
# for trade in closed_trades:
# total_profit += trade.close_profit
return market_side, current_profit, total_profit
return market_side, current_profit, int(trade_duration)
def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first: bool = False
@ -192,10 +197,11 @@ class BaseReinforcementLearningModel(IFreqaiModel):
output = pd.DataFrame(np.zeros(len(dataframe)), columns=dk.label_list)
def _predict(window):
market_side, current_profit, total_profit = self.get_state_info(dk.pair)
market_side, current_profit, trade_duration = self.get_state_info(dk.pair)
observations = dataframe.iloc[window.index]
observations['current_profit'] = current_profit
observations['position'] = market_side
observations['trade_duration'] = trade_duration
res, _ = model.predict(observations, deterministic=True)
return res