callback function and TDQN model added
This commit is contained in:
MukavaValkku
robcaulk
225
freqtrade/freqai/prediction_models/RL/RLPrediction_agent_v2.py
Normal file
225
freqtrade/freqai/prediction_models/RL/RLPrediction_agent_v2.py
Normal file
@@ -0,0 +1,225 @@
|
||||
import torch as th
|
||||
from torch import nn
|
||||
from typing import Dict, List, Tuple, Type, Optional, Any, Union
|
||||
import gym
|
||||
from stable_baselines3.common.type_aliases import GymEnv, Schedule
|
||||
from stable_baselines3.common.torch_layers import (
|
||||
BaseFeaturesExtractor,
|
||||
FlattenExtractor,
|
||||
CombinedExtractor
|
||||
)
|
||||
from stable_baselines3.common.buffers import ReplayBuffer
|
||||
from stable_baselines3 import DQN
|
||||
|
||||
|
||||
from stable_baselines3.common.policies import BasePolicy
|
||||
#from stable_baselines3.common.policies import register_policy
|
||||
from stable_baselines3.dqn.policies import (
|
||||
QNetwork, DQNPolicy, MultiInputPolicy,
|
||||
CnnPolicy, DQNPolicy, MlpPolicy)
|
||||
import torch
|
||||
|
||||
|
||||
def create_mlp_(
|
||||
input_dim: int,
|
||||
output_dim: int,
|
||||
net_arch: List[int],
|
||||
activation_fn: Type[nn.Module] = nn.ReLU,
|
||||
squash_output: bool = False,
|
||||
) -> List[nn.Module]:
|
||||
dropout = 0.2
|
||||
if len(net_arch) > 0:
|
||||
number_of_neural = net_arch[0]
|
||||
|
||||
modules = [
|
||||
nn.Linear(input_dim, number_of_neural),
|
||||
nn.BatchNorm1d(number_of_neural),
|
||||
nn.LeakyReLU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(number_of_neural, number_of_neural),
|
||||
nn.BatchNorm1d(number_of_neural),
|
||||
nn.LeakyReLU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(number_of_neural, number_of_neural),
|
||||
nn.BatchNorm1d(number_of_neural),
|
||||
nn.LeakyReLU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(number_of_neural, number_of_neural),
|
||||
nn.BatchNorm1d(number_of_neural),
|
||||
nn.LeakyReLU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(number_of_neural, output_dim)
|
||||
]
|
||||
return modules
|
||||
|
||||
class TDQNetwork(QNetwork):
|
||||
def __init__(self,
|
||||
observation_space: gym.spaces.Space,
|
||||
action_space: gym.spaces.Space,
|
||||
features_extractor: nn.Module,
|
||||
features_dim: int,
|
||||
net_arch: Optional[List[int]] = None,
|
||||
activation_fn: Type[nn.Module] = nn.ReLU,
|
||||
normalize_images: bool = True
|
||||
):
|
||||
super().__init__(
|
||||
observation_space=observation_space,
|
||||
action_space=action_space,
|
||||
features_extractor=features_extractor,
|
||||
features_dim=features_dim,
|
||||
net_arch=net_arch,
|
||||
activation_fn=activation_fn,
|
||||
normalize_images=normalize_images
|
||||
)
|
||||
action_dim = self.action_space.n
|
||||
q_net = create_mlp_(self.features_dim, action_dim, self.net_arch, self.activation_fn)
|
||||
self.q_net = nn.Sequential(*q_net).apply(self.init_weights)
|
||||
|
||||
def init_weights(self, m):
|
||||
if type(m) == nn.Linear:
|
||||
torch.nn.init.kaiming_uniform_(m.weight)
|
||||
|
||||
|
||||
class TDQNPolicy(DQNPolicy):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
observation_space: gym.spaces.Space,
|
||||
action_space: gym.spaces.Space,
|
||||
lr_schedule: Schedule,
|
||||
net_arch: Optional[List[int]] = None,
|
||||
activation_fn: Type[nn.Module] = nn.ReLU,
|
||||
features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
|
||||
features_extractor_kwargs: Optional[Dict[str, Any]] = None,
|
||||
normalize_images: bool = True,
|
||||
optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
|
||||
optimizer_kwargs: Optional[Dict[str, Any]] = None,
|
||||
):
|
||||
super().__init__(
|
||||
observation_space=observation_space,
|
||||
action_space=action_space,
|
||||
lr_schedule=lr_schedule,
|
||||
net_arch=net_arch,
|
||||
activation_fn=activation_fn,
|
||||
features_extractor_class=features_extractor_class,
|
||||
features_extractor_kwargs=features_extractor_kwargs,
|
||||
normalize_images=normalize_images,
|
||||
optimizer_class=optimizer_class,
|
||||
optimizer_kwargs=optimizer_kwargs
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def init_weights(module: nn.Module, gain: float = 1) -> None:
|
||||
"""
|
||||
Orthogonal initialization (used in PPO and A2C)
|
||||
"""
|
||||
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
||||
nn.init.kaiming_uniform_(module.weight)
|
||||
if module.bias is not None:
|
||||
module.bias.data.fill_(0.0)
|
||||
|
||||
def make_q_net(self) -> TDQNetwork:
|
||||
# Make sure we always have separate networks for features extractors etc
|
||||
net_args = self._update_features_extractor(self.net_args, features_extractor=None)
|
||||
return TDQNetwork(**net_args).to(self.device)
|
||||
|
||||
|
||||
class TMultiInputPolicy(TDQNPolicy):
|
||||
def __init__(
|
||||
self,
|
||||
observation_space: gym.spaces.Space,
|
||||
action_space: gym.spaces.Space,
|
||||
lr_schedule: Schedule,
|
||||
net_arch: Optional[List[int]] = None,
|
||||
activation_fn: Type[nn.Module] = nn.ReLU,
|
||||
features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
|
||||
features_extractor_kwargs: Optional[Dict[str, Any]] = None,
|
||||
normalize_images: bool = True,
|
||||
optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
|
||||
optimizer_kwargs: Optional[Dict[str, Any]] = None,
|
||||
):
|
||||
super().__init__(
|
||||
observation_space,
|
||||
action_space,
|
||||
lr_schedule,
|
||||
net_arch,
|
||||
activation_fn,
|
||||
features_extractor_class,
|
||||
features_extractor_kwargs,
|
||||
normalize_images,
|
||||
optimizer_class,
|
||||
optimizer_kwargs,
|
||||
)
|
||||
|
||||
|
||||
class TDQN(DQN):
|
||||
|
||||
policy_aliases: Dict[str, Type[BasePolicy]] = {
|
||||
"MlpPolicy": MlpPolicy,
|
||||
"CnnPolicy": CnnPolicy,
|
||||
"TMultiInputPolicy": TMultiInputPolicy,
|
||||
}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
policy: Union[str, Type[TDQNPolicy]],
|
||||
env: Union[GymEnv, str],
|
||||
learning_rate: Union[float, Schedule] = 1e-4,
|
||||
buffer_size: int = 1000000, # 1e6
|
||||
learning_starts: int = 50000,
|
||||
batch_size: int = 32,
|
||||
tau: float = 1.0,
|
||||
gamma: float = 0.99,
|
||||
train_freq: Union[int, Tuple[int, str]] = 4,
|
||||
gradient_steps: int = 1,
|
||||
replay_buffer_class: Optional[ReplayBuffer] = None,
|
||||
replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
|
||||
optimize_memory_usage: bool = False,
|
||||
target_update_interval: int = 10000,
|
||||
exploration_fraction: float = 0.1,
|
||||
exploration_initial_eps: float = 1.0,
|
||||
exploration_final_eps: float = 0.05,
|
||||
max_grad_norm: float = 10,
|
||||
tensorboard_log: Optional[str] = None,
|
||||
create_eval_env: bool = False,
|
||||
policy_kwargs: Optional[Dict[str, Any]] = None,
|
||||
verbose: int = 1,
|
||||
seed: Optional[int] = None,
|
||||
device: Union[th.device, str] = "auto",
|
||||
_init_setup_model: bool = True,
|
||||
):
|
||||
|
||||
super().__init__(
|
||||
policy=policy,
|
||||
env=env,
|
||||
learning_rate=learning_rate,
|
||||
buffer_size=buffer_size,
|
||||
learning_starts=learning_starts,
|
||||
batch_size=batch_size,
|
||||
tau=tau,
|
||||
gamma=gamma,
|
||||
train_freq=train_freq,
|
||||
gradient_steps=gradient_steps,
|
||||
replay_buffer_class=replay_buffer_class, # No action noise
|
||||
replay_buffer_kwargs=replay_buffer_kwargs,
|
||||
optimize_memory_usage=optimize_memory_usage,
|
||||
target_update_interval=target_update_interval,
|
||||
exploration_fraction=exploration_fraction,
|
||||
exploration_initial_eps=exploration_initial_eps,
|
||||
exploration_final_eps=exploration_final_eps,
|
||||
max_grad_norm=max_grad_norm,
|
||||
tensorboard_log=tensorboard_log,
|
||||
create_eval_env=create_eval_env,
|
||||
policy_kwargs=policy_kwargs,
|
||||
verbose=verbose,
|
||||
seed=seed,
|
||||
device=device,
|
||||
_init_setup_model=_init_setup_model
|
||||
)
|
||||
|
||||
|
||||
|
||||
# try:
|
||||
# register_policy("TMultiInputPolicy", TMultiInputPolicy)
|
||||
# except:
|
||||
# print("already registered")
|
||||
645
freqtrade/freqai/prediction_models/RL/RLPrediction_env_v2.py
Normal file
645
freqtrade/freqai/prediction_models/RL/RLPrediction_env_v2.py
Normal file
@@ -0,0 +1,645 @@
|
||||
import gym
|
||||
from gym import spaces
|
||||
from gym.utils import seeding
|
||||
from enum import Enum
|
||||
from sklearn.decomposition import PCA, KernelPCA
|
||||
import random
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from collections import deque
|
||||
import matplotlib.pylab as plt
|
||||
from typing import Dict, List, Tuple, Type, Optional, Any, Union, Callable
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# from bokeh.io import output_notebook
|
||||
# from bokeh.plotting import figure, show
|
||||
# from bokeh.models import (
|
||||
# CustomJS,
|
||||
# ColumnDataSource,
|
||||
# NumeralTickFormatter,
|
||||
# Span,
|
||||
# HoverTool,
|
||||
# Range1d,
|
||||
# DatetimeTickFormatter,
|
||||
# Scatter,
|
||||
# Label, LabelSet
|
||||
# )
|
||||
|
||||
class Actions(Enum):
|
||||
Short = 0
|
||||
Long = 1
|
||||
Neutral = 2
|
||||
|
||||
class Actions_v2(Enum):
|
||||
Neutral = 0
|
||||
Long_buy = 1
|
||||
Long_sell = 2
|
||||
Short_buy = 3
|
||||
Short_sell = 4
|
||||
|
||||
|
||||
class Positions(Enum):
|
||||
Short = 0
|
||||
Long = 1
|
||||
Neutral = 0.5
|
||||
|
||||
def opposite(self):
|
||||
return Positions.Short if self == Positions.Long else Positions.Long
|
||||
|
||||
def mean_over_std(x):
|
||||
std = np.std(x, ddof=1)
|
||||
mean = np.mean(x)
|
||||
return mean / std if std > 0 else 0
|
||||
|
||||
class DEnv(gym.Env):
|
||||
|
||||
metadata = {'render.modes': ['human']}
|
||||
|
||||
def __init__(self, df, prices, reward_kwargs, window_size=10, starting_point=True, ):
|
||||
assert df.ndim == 2
|
||||
|
||||
self.seed()
|
||||
self.df = df
|
||||
self.signal_features = self.df
|
||||
self.prices = prices
|
||||
self.window_size = window_size
|
||||
self.starting_point = starting_point
|
||||
self.rr = reward_kwargs["rr"]
|
||||
self.profit_aim = reward_kwargs["profit_aim"]
|
||||
|
||||
self.fee=0.0015
|
||||
|
||||
# # spaces
|
||||
self.shape = (window_size, self.signal_features.shape[1])
|
||||
self.action_space = spaces.Discrete(len(Actions_v2))
|
||||
self.observation_space = spaces.Box(low=-np.inf, high=np.inf, shape=self.shape, dtype=np.float32)
|
||||
|
||||
# episode
|
||||
self._start_tick = self.window_size
|
||||
self._end_tick = len(self.prices) - 1
|
||||
self._done = None
|
||||
self._current_tick = None
|
||||
self._last_trade_tick = None
|
||||
self._position = Positions.Neutral
|
||||
self._position_history = None
|
||||
self.total_reward = None
|
||||
self._total_profit = None
|
||||
self._first_rendering = None
|
||||
self.history = None
|
||||
self.trade_history = []
|
||||
|
||||
# self.A_t, self.B_t = 0.000639, 0.00001954
|
||||
self.r_t_change = 0.
|
||||
|
||||
self.returns_report = []
|
||||
|
||||
|
||||
def seed(self, seed=None):
|
||||
self.np_random, seed = seeding.np_random(seed)
|
||||
return [seed]
|
||||
|
||||
|
||||
def reset(self):
|
||||
|
||||
self._done = False
|
||||
|
||||
if self.starting_point == True:
|
||||
self._position_history = (self._start_tick* [None]) + [self._position]
|
||||
else:
|
||||
self._position_history = (self.window_size * [None]) + [self._position]
|
||||
|
||||
self._current_tick = self._start_tick
|
||||
self._last_trade_tick = None
|
||||
#self._last_trade_tick = self._current_tick - 1
|
||||
self._position = Positions.Neutral
|
||||
|
||||
self.total_reward = 0.
|
||||
self._total_profit = 1. # unit
|
||||
self._first_rendering = True
|
||||
self.history = {}
|
||||
self.trade_history = []
|
||||
self.portfolio_log_returns = np.zeros(len(self.prices))
|
||||
|
||||
|
||||
self._profits = [(self._start_tick, 1)]
|
||||
self.close_trade_profit = []
|
||||
self.r_t_change = 0.
|
||||
|
||||
self.returns_report = []
|
||||
|
||||
return self._get_observation()
|
||||
|
||||
|
||||
def step(self, action):
|
||||
self._done = False
|
||||
self._current_tick += 1
|
||||
|
||||
if self._current_tick == self._end_tick:
|
||||
self._done = True
|
||||
|
||||
self.update_portfolio_log_returns(action)
|
||||
|
||||
self._update_profit(action)
|
||||
step_reward = self._calculate_reward(action)
|
||||
self.total_reward += step_reward
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
trade_type = None
|
||||
if self.is_tradesignal_v2(action): # exclude 3 case not trade
|
||||
# Update position
|
||||
"""
|
||||
Action: Neutral, position: Long -> Close Long
|
||||
Action: Neutral, position: Short -> Close Short
|
||||
|
||||
Action: Long, position: Neutral -> Open Long
|
||||
Action: Long, position: Short -> Close Short and Open Long
|
||||
|
||||
Action: Short, position: Neutral -> Open Short
|
||||
Action: Short, position: Long -> Close Long and Open Short
|
||||
"""
|
||||
|
||||
|
||||
temp_position = self._position
|
||||
if action == Actions_v2.Neutral.value:
|
||||
self._position = Positions.Neutral
|
||||
trade_type = "neutral"
|
||||
elif action == Actions_v2.Long_buy.value:
|
||||
self._position = Positions.Long
|
||||
trade_type = "long"
|
||||
elif action == Actions_v2.Short_buy.value:
|
||||
self._position = Positions.Short
|
||||
trade_type = "short"
|
||||
elif action == Actions_v2.Long_sell.value:
|
||||
self._position = Positions.Neutral
|
||||
trade_type = "neutral"
|
||||
elif action == Actions_v2.Short_sell.value:
|
||||
self._position = Positions.Neutral
|
||||
trade_type = "neutral"
|
||||
else:
|
||||
print("case not defined")
|
||||
|
||||
# Update last trade tick
|
||||
self._last_trade_tick = self._current_tick
|
||||
|
||||
if trade_type != None:
|
||||
self.trade_history.append(
|
||||
{'price': self.current_price(), 'index': self._current_tick, 'type': trade_type})
|
||||
|
||||
if self._total_profit < 0.2:
|
||||
self._done = True
|
||||
|
||||
self._position_history.append(self._position)
|
||||
observation = self._get_observation()
|
||||
info = dict(
|
||||
tick = self._current_tick,
|
||||
total_reward = self.total_reward,
|
||||
total_profit = self._total_profit,
|
||||
position = self._position.value
|
||||
)
|
||||
self._update_history(info)
|
||||
|
||||
return observation, step_reward, self._done, info
|
||||
|
||||
|
||||
def processState(self, state):
|
||||
return state.to_numpy()
|
||||
|
||||
def convert_mlp_Policy(self, obs_):
|
||||
pass
|
||||
|
||||
def _get_observation(self):
|
||||
return self.signal_features[(self._current_tick - self.window_size):self._current_tick]
|
||||
|
||||
|
||||
def get_unrealized_profit(self):
|
||||
|
||||
if self._last_trade_tick == None:
|
||||
return 0.
|
||||
|
||||
if self._position == Positions.Neutral:
|
||||
return 0.
|
||||
elif self._position == Positions.Short:
|
||||
current_price = self.add_buy_fee(self.prices.iloc[self._current_tick].open)
|
||||
last_trade_price = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
|
||||
return (last_trade_price - current_price)/last_trade_price
|
||||
elif self._position == Positions.Long:
|
||||
current_price = self.add_sell_fee(self.prices.iloc[self._current_tick].open)
|
||||
last_trade_price = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
|
||||
return (current_price - last_trade_price)/last_trade_price
|
||||
else:
|
||||
return 0.
|
||||
|
||||
|
||||
def is_tradesignal(self, action):
|
||||
# trade signal
|
||||
"""
|
||||
not trade signal is :
|
||||
Action: Neutral, position: Neutral -> Nothing
|
||||
Action: Long, position: Long -> Hold Long
|
||||
Action: Short, position: Short -> Hold Short
|
||||
"""
|
||||
return not ((action == Actions.Neutral.value and self._position == Positions.Neutral)
|
||||
or (action == Actions.Short.value and self._position == Positions.Short)
|
||||
or (action == Actions.Long.value and self._position == Positions.Long))
|
||||
|
||||
def is_tradesignal_v2(self, action):
|
||||
# trade signal
|
||||
"""
|
||||
not trade signal is :
|
||||
Action: Neutral, position: Neutral -> Nothing
|
||||
Action: Long, position: Long -> Hold Long
|
||||
Action: Short, position: Short -> Hold Short
|
||||
"""
|
||||
return not ((action == Actions_v2.Neutral.value and self._position == Positions.Neutral) or
|
||||
(action == Actions_v2.Short_buy.value and self._position == Positions.Short) or
|
||||
(action == Actions_v2.Short_sell.value and self._position == Positions.Short) or
|
||||
(action == Actions_v2.Short_buy.value and self._position == Positions.Long) or
|
||||
(action == Actions_v2.Short_sell.value and self._position == Positions.Long) or
|
||||
|
||||
(action == Actions_v2.Long_buy.value and self._position == Positions.Long) or
|
||||
(action == Actions_v2.Long_sell.value and self._position == Positions.Long) or
|
||||
(action == Actions_v2.Long_buy.value and self._position == Positions.Short) or
|
||||
(action == Actions_v2.Long_sell.value and self._position == Positions.Short))
|
||||
|
||||
|
||||
|
||||
def _is_trade(self, action: Actions):
|
||||
return ((action == Actions.Long.value and self._position == Positions.Short) or
|
||||
(action == Actions.Short.value and self._position == Positions.Long) or
|
||||
(action == Actions.Neutral.value and self._position == Positions.Long) or
|
||||
(action == Actions.Neutral.value and self._position == Positions.Short)
|
||||
)
|
||||
|
||||
def _is_trade_v2(self, action: Actions_v2):
|
||||
return ((action == Actions_v2.Long_buy.value and self._position == Positions.Short) or
|
||||
(action == Actions_v2.Short_buy.value and self._position == Positions.Long) or
|
||||
(action == Actions_v2.Neutral.value and self._position == Positions.Long) or
|
||||
(action == Actions_v2.Neutral.value and self._position == Positions.Short) or
|
||||
|
||||
(action == Actions_v2.Neutral.Short_sell and self._position == Positions.Long) or
|
||||
(action == Actions_v2.Neutral.Long_sell and self._position == Positions.Short)
|
||||
)
|
||||
|
||||
|
||||
def is_hold(self, action):
|
||||
return ((action == Actions.Short.value and self._position == Positions.Short)
|
||||
or (action == Actions.Long.value and self._position == Positions.Long))
|
||||
|
||||
def is_hold_v2(self, action):
|
||||
return ((action == Actions_v2.Short_buy.value and self._position == Positions.Short)
|
||||
or (action == Actions_v2.Long_buy.value and self._position == Positions.Long))
|
||||
|
||||
|
||||
def add_buy_fee(self, price):
|
||||
return price * (1 + self.fee)
|
||||
|
||||
def add_sell_fee(self, price):
|
||||
return price / (1 + self.fee)
|
||||
|
||||
def _update_history(self, info):
|
||||
if not self.history:
|
||||
self.history = {key: [] for key in info.keys()}
|
||||
|
||||
for key, value in info.items():
|
||||
self.history[key].append(value)
|
||||
|
||||
|
||||
def render(self, mode='human'):
|
||||
|
||||
def _plot_position(position, tick):
|
||||
color = None
|
||||
if position == Positions.Short:
|
||||
color = 'red'
|
||||
elif position == Positions.Long:
|
||||
color = 'green'
|
||||
if color:
|
||||
plt.scatter(tick, self.prices.loc[tick].open, color=color)
|
||||
|
||||
if self._first_rendering:
|
||||
self._first_rendering = False
|
||||
plt.cla()
|
||||
plt.plot(self.prices)
|
||||
start_position = self._position_history[self._start_tick]
|
||||
_plot_position(start_position, self._start_tick)
|
||||
|
||||
plt.cla()
|
||||
plt.plot(self.prices)
|
||||
_plot_position(self._position, self._current_tick)
|
||||
|
||||
plt.suptitle("Total Reward: %.6f" % self.total_reward + ' ~ ' + "Total Profit: %.6f" % self._total_profit)
|
||||
plt.pause(0.01)
|
||||
|
||||
|
||||
def render_all(self):
|
||||
plt.figure()
|
||||
window_ticks = np.arange(len(self._position_history))
|
||||
plt.plot(self.prices['open'], alpha=0.5)
|
||||
|
||||
short_ticks = []
|
||||
long_ticks = []
|
||||
neutral_ticks = []
|
||||
for i, tick in enumerate(window_ticks):
|
||||
if self._position_history[i] == Positions.Short:
|
||||
short_ticks.append(tick - 1)
|
||||
elif self._position_history[i] == Positions.Long:
|
||||
long_ticks.append(tick - 1)
|
||||
elif self._position_history[i] == Positions.Neutral:
|
||||
neutral_ticks.append(tick - 1)
|
||||
|
||||
plt.plot(neutral_ticks, self.prices.loc[neutral_ticks].open,
|
||||
'o', color='grey', ms=3, alpha=0.1)
|
||||
plt.plot(short_ticks, self.prices.loc[short_ticks].open,
|
||||
'o', color='r', ms=3, alpha=0.8)
|
||||
plt.plot(long_ticks, self.prices.loc[long_ticks].open,
|
||||
'o', color='g', ms=3, alpha=0.8)
|
||||
|
||||
plt.suptitle("Generalising")
|
||||
fig = plt.gcf()
|
||||
fig.set_size_inches(15, 10)
|
||||
|
||||
|
||||
|
||||
|
||||
def close_trade_report(self):
|
||||
small_trade = 0
|
||||
positive_big_trade = 0
|
||||
negative_big_trade = 0
|
||||
small_profit = 0.003
|
||||
for i in self.close_trade_profit:
|
||||
if i < small_profit and i > -small_profit:
|
||||
small_trade+=1
|
||||
elif i > small_profit:
|
||||
positive_big_trade += 1
|
||||
elif i < -small_profit:
|
||||
negative_big_trade += 1
|
||||
print(f"small trade={small_trade/len(self.close_trade_profit)}; positive_big_trade={positive_big_trade/len(self.close_trade_profit)}; negative_big_trade={negative_big_trade/len(self.close_trade_profit)}")
|
||||
|
||||
|
||||
def report(self):
|
||||
|
||||
# get total trade
|
||||
long_trade = 0
|
||||
short_trade = 0
|
||||
neutral_trade = 0
|
||||
for trade in self.trade_history:
|
||||
if trade['type'] == 'long':
|
||||
long_trade += 1
|
||||
|
||||
elif trade['type'] == 'short':
|
||||
short_trade += 1
|
||||
else:
|
||||
neutral_trade += 1
|
||||
|
||||
negative_trade = 0
|
||||
positive_trade = 0
|
||||
for tr in self.close_trade_profit:
|
||||
if tr < 0.:
|
||||
negative_trade += 1
|
||||
|
||||
if tr > 0.:
|
||||
positive_trade += 1
|
||||
|
||||
total_trade_lr = negative_trade+positive_trade
|
||||
|
||||
|
||||
total_trade = long_trade + short_trade
|
||||
sharp_ratio = self.sharpe_ratio()
|
||||
sharp_log = self.get_sharpe_ratio()
|
||||
|
||||
from tabulate import tabulate
|
||||
|
||||
headers = ["Performance", ""]
|
||||
performanceTable = [["Total Trade", "{0:.2f}".format(total_trade)],
|
||||
["Total reward", "{0:.3f}".format(self.total_reward)],
|
||||
["Start profit(unit)", "{0:.2f}".format(1.)],
|
||||
["End profit(unit)", "{0:.3f}".format(self._total_profit)],
|
||||
["Sharp ratio", "{0:.3f}".format(sharp_ratio)],
|
||||
["Sharp log", "{0:.3f}".format(sharp_log)],
|
||||
# ["Sortino ratio", "{0:.2f}".format(0) + '%'],
|
||||
["winrate", "{0:.2f}".format(positive_trade*100/total_trade_lr) + '%']
|
||||
]
|
||||
tabulation = tabulate(performanceTable, headers, tablefmt="fancy_grid", stralign="center")
|
||||
print(tabulation)
|
||||
|
||||
result = {
|
||||
"Start": "{0:.2f}".format(1.),
|
||||
"End": "{0:.2f}".format(self._total_profit),
|
||||
"Sharp": "{0:.3f}".format(sharp_ratio),
|
||||
"Winrate": "{0:.2f}".format(positive_trade*100/total_trade_lr)
|
||||
}
|
||||
return result
|
||||
|
||||
def close(self):
|
||||
plt.close()
|
||||
|
||||
def get_sharpe_ratio(self):
|
||||
return mean_over_std(self.get_portfolio_log_returns())
|
||||
|
||||
|
||||
def save_rendering(self, filepath):
|
||||
plt.savefig(filepath)
|
||||
|
||||
|
||||
def pause_rendering(self):
|
||||
plt.show()
|
||||
|
||||
|
||||
def _calculate_reward(self, action):
|
||||
# rw = self.transaction_profit_reward(action)
|
||||
#rw = self.reward_rr_profit_config(action)
|
||||
rw = self.reward_rr_profit_config_v2(action)
|
||||
return rw
|
||||
|
||||
|
||||
def _update_profit(self, action):
|
||||
#if self._is_trade(action) or self._done:
|
||||
if self._is_trade_v2(action) or self._done:
|
||||
pnl = self.get_unrealized_profit()
|
||||
|
||||
if self._position == Positions.Long:
|
||||
self._total_profit = self._total_profit + self._total_profit*pnl
|
||||
self._profits.append((self._current_tick, self._total_profit))
|
||||
self.close_trade_profit.append(pnl)
|
||||
|
||||
if self._position == Positions.Short:
|
||||
self._total_profit = self._total_profit + self._total_profit*pnl
|
||||
self._profits.append((self._current_tick, self._total_profit))
|
||||
self.close_trade_profit.append(pnl)
|
||||
|
||||
|
||||
def most_recent_return(self, action):
|
||||
"""
|
||||
We support Long, Neutral and Short positions.
|
||||
Return is generated from rising prices in Long
|
||||
and falling prices in Short positions.
|
||||
The actions Sell/Buy or Hold during a Long position trigger the sell/buy-fee.
|
||||
"""
|
||||
# Long positions
|
||||
if self._position == Positions.Long:
|
||||
current_price = self.prices.iloc[self._current_tick].open
|
||||
#if action == Actions.Short.value or action == Actions.Neutral.value:
|
||||
if action == Actions_v2.Short_buy.value or action == Actions_v2.Neutral.value:
|
||||
current_price = self.add_sell_fee(current_price)
|
||||
|
||||
previous_price = self.prices.iloc[self._current_tick - 1].open
|
||||
|
||||
if (self._position_history[self._current_tick - 1] == Positions.Short
|
||||
or self._position_history[self._current_tick - 1] == Positions.Neutral):
|
||||
previous_price = self.add_buy_fee(previous_price)
|
||||
|
||||
return np.log(current_price) - np.log(previous_price)
|
||||
|
||||
# Short positions
|
||||
if self._position == Positions.Short:
|
||||
current_price = self.prices.iloc[self._current_tick].open
|
||||
#if action == Actions.Long.value or action == Actions.Neutral.value:
|
||||
if action == Actions_v2.Long_buy.value or action == Actions_v2.Neutral.value:
|
||||
current_price = self.add_buy_fee(current_price)
|
||||
|
||||
previous_price = self.prices.iloc[self._current_tick - 1].open
|
||||
if (self._position_history[self._current_tick - 1] == Positions.Long
|
||||
or self._position_history[self._current_tick - 1] == Positions.Neutral):
|
||||
previous_price = self.add_sell_fee(previous_price)
|
||||
|
||||
return np.log(previous_price) - np.log(current_price)
|
||||
|
||||
return 0
|
||||
|
||||
def get_portfolio_log_returns(self):
|
||||
return self.portfolio_log_returns[1:self._current_tick + 1]
|
||||
|
||||
|
||||
def get_trading_log_return(self):
|
||||
return self.portfolio_log_returns[self._start_tick:]
|
||||
|
||||
def update_portfolio_log_returns(self, action):
|
||||
self.portfolio_log_returns[self._current_tick] = self.most_recent_return(action)
|
||||
|
||||
def current_price(self) -> float:
|
||||
return self.prices.iloc[self._current_tick].open
|
||||
|
||||
def prev_price(self) -> float:
|
||||
return self.prices.iloc[self._current_tick-1].open
|
||||
|
||||
|
||||
|
||||
def sharpe_ratio(self):
|
||||
if len(self.close_trade_profit) == 0:
|
||||
return 0.
|
||||
returns = np.array(self.close_trade_profit)
|
||||
reward = (np.mean(returns) - 0. + 1e-9) / (np.std(returns) + 1e-9)
|
||||
return reward
|
||||
|
||||
def get_bnh_log_return(self):
|
||||
return np.diff(np.log(self.prices['open'][self._start_tick:]))
|
||||
|
||||
|
||||
def transaction_profit_reward(self, action):
|
||||
rw = 0.
|
||||
|
||||
pt = self.prev_price()
|
||||
pt_1 = self.current_price()
|
||||
|
||||
|
||||
if self._position == Positions.Long:
|
||||
a_t = 1
|
||||
elif self._position == Positions.Short:
|
||||
a_t = -1
|
||||
else:
|
||||
a_t = 0
|
||||
|
||||
# close long
|
||||
if (action == Actions.Short.value or action == Actions.Neutral.value) and self._position == Positions.Long:
|
||||
pt_1 = self.add_sell_fee(self.current_price())
|
||||
po = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
|
||||
|
||||
rw = a_t*(pt_1 - po)/po
|
||||
#rw = rw*2
|
||||
# close short
|
||||
elif (action == Actions.Long.value or action == Actions.Neutral.value) and self._position == Positions.Short:
|
||||
pt_1 = self.add_buy_fee(self.current_price())
|
||||
po = self.add_sell_fee(self.prices.iloc[self._last_trade_tick].open)
|
||||
rw = a_t*(pt_1 - po)/po
|
||||
#rw = rw*2
|
||||
else:
|
||||
rw = a_t*(pt_1 - pt)/pt
|
||||
|
||||
return np.clip(rw, 0, 1)
|
||||
|
||||
|
||||
|
||||
def reward_rr_profit_config_v2(self, action):
|
||||
rw = 0.
|
||||
|
||||
pt_1 = self.current_price()
|
||||
|
||||
|
||||
if len(self.close_trade_profit) > 0:
|
||||
# long
|
||||
if self._position == Positions.Long:
|
||||
pt_1 = self.add_sell_fee(self.current_price())
|
||||
po = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
|
||||
|
||||
if action == Actions_v2.Short_buy.value:
|
||||
if self.close_trade_profit[-1] > self.profit_aim * self.rr:
|
||||
rw = 10 * 2
|
||||
elif self.close_trade_profit[-1] > 0 and self.close_trade_profit[-1] < self.profit_aim * self.rr:
|
||||
rw = 10 * 1 * 1
|
||||
elif self.close_trade_profit[-1] < 0:
|
||||
rw = 10 * -1
|
||||
elif self.close_trade_profit[-1] < (self.profit_aim * -1) * self.rr:
|
||||
rw = 10 * 3 * -1
|
||||
|
||||
if action == Actions_v2.Long_sell.value:
|
||||
if self.close_trade_profit[-1] > self.profit_aim * self.rr:
|
||||
rw = 10 * 5
|
||||
elif self.close_trade_profit[-1] > 0 and self.close_trade_profit[-1] < self.profit_aim * self.rr:
|
||||
rw = 10 * 1 * 3
|
||||
elif self.close_trade_profit[-1] < 0:
|
||||
rw = 10 * -1
|
||||
elif self.close_trade_profit[-1] < (self.profit_aim * -1) * self.rr:
|
||||
rw = 10 * 3 * -1
|
||||
|
||||
if action == Actions_v2.Neutral.value:
|
||||
if self.close_trade_profit[-1] > 0:
|
||||
rw = 2
|
||||
elif self.close_trade_profit[-1] < 0:
|
||||
rw = 2 * -1
|
||||
|
||||
# short
|
||||
if self._position == Positions.Short:
|
||||
pt_1 = self.add_sell_fee(self.current_price())
|
||||
po = self.add_buy_fee(self.prices.iloc[self._last_trade_tick].open)
|
||||
|
||||
if action == Actions_v2.Long_buy.value:
|
||||
if self.close_trade_profit[-1] > self.profit_aim * self.rr:
|
||||
rw = 10 * 2
|
||||
elif self.close_trade_profit[-1] > 0 and self.close_trade_profit[-1] < (self.profit_aim * -1) * self.rr:
|
||||
rw = 10 * 1 * 1
|
||||
elif self.close_trade_profit[-1] < 0:
|
||||
rw = 10 * -1
|
||||
elif self.close_trade_profit[-1] < (self.profit_aim * -1) * self.rr:
|
||||
rw = 10 * 3 * -1
|
||||
|
||||
if action == Actions_v2.Short_sell.value:
|
||||
if self.close_trade_profit[-1] > self.profit_aim * self.rr:
|
||||
rw = 10 * 5
|
||||
elif self.close_trade_profit[-1] > 0 and self.close_trade_profit[-1] < (self.profit_aim * -1) * self.rr:
|
||||
rw = 10 * 1 * 3
|
||||
elif self.close_trade_profit[-1] < 0:
|
||||
rw = 10 * -1
|
||||
elif self.close_trade_profit[-1] < (self.profit_aim * -1) * self.rr:
|
||||
rw = 10 * 3 * -1
|
||||
|
||||
if action == Actions_v2.Neutral.value:
|
||||
if self.close_trade_profit[-1] > 0:
|
||||
rw = 2
|
||||
elif self.close_trade_profit[-1] < 0:
|
||||
rw = 2 * -1
|
||||
|
||||
return np.clip(rw, 0, 1)
|
||||
Reference in New Issue
Block a user