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LeverageSimMarket.py
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LeverageSimMarket.py
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from __future__ import print_function
import pandas as pd
import numpy as np
from gym import spaces
import gym
import time
import json
import pickle
np.random.seed(42)
def unscale(value, scaler, column):
traindf_min = scaler.data_min_[column]
traindf_max = scaler.data_max_[column]
return ((value * (traindf_max - traindf_min)) + traindf_min)
def scale(value, scaler, column):
traindf_min = scaler.data_min_[column]
traindf_max = scaler.data_max_[column]
return (value - traindf_min) / (traindf_max-traindf_min)
class LeverageSimMarket(gym.Env):
def __init__(self, cash = 0, data = []):
super(LeverageSimMarket, self).__init__()
self.cash = cash
self.start = time.time()
self.initcash = self.cash
self.coins = 0
#start with only cash
self.row = 0
self.price = 0
self.data = np.array(data)
self.failstate = False
#we will discretize to -10,10 here
#self.action_space = spaces.Box(high = 1, low = -1, shape = (1,))
self.action_space = spaces.Box(low= 0, high = 1, shape = (1,))
high = np.full(np.shape(self.data[0]), 1)
low = np.full(np.shape(self.data[0]), 0)
#high = np.array([np.finfo(np.float32).max, np.finfo(np.float32).max])
#low = np.array([0,0])
self.observation_space = spaces.Box(np.float32(low),np.float32(high))
self.state = None
self.accountvalue = self.cash
self.nowroi = 1
#per episode log
self.actions = []
self.vals = []
self.roi = []
#whole run log
self.num_episodes = -1 #don't ask, logic really forces your hand sometimes, or laziness
self.valperepisode = []
self.actionperepisode = []
self.roiperepisode = []
self.endingvalperepisode = []
#account value now
def getAccountValue(self):
return (self.price * self.coins) + self.cash
def getState(self):
# if self.getAccountValue() < self.initcash/10:
# self.failState = True
return np.array(self.state), ((self.nowroi - 0) / np.std(self.roi)) if np.std(self.roi) != 0 else np.random.uniform(0,1), self.failState, {}
def getROI(self):
return (self.accountvalue / self.initcash)
# def rewardFunction(self):
# return ((self.nowroi - 0) / np.std(self.nowroi))
def step(self, action):
#price,predprice
self.failState = False
if self.row >= len(self.data):
self.failState = True
# print(self.getAccountValue())
print("Took ", np.round((time.time() - self.start),4) ," to run. Account value at end of run: " , np.round(self.accountvalue,2), "difference from hodl point: ", np.round((self.accountvalue - ((self.initcash / np.min(self.data[:,0])) * np.max(self.data[:,0]))),2), " average action:" + str(np.mean(self.actions)))
return self.getState()
self.state = self.data[self.row]
#price
self.price = self.state[0]
action = action[0]
# coinValue = self.coins * self.price
# totalValue = self.cash + coinValue
# currentLeverage = coinValue / totalValue
# deltaLeverage = action - currentLeverage
# deltaCash = deltaLeverage * totalValue
# deltaCoins = deltaCash / self.price
# self.cash = self.cash - deltaCash
# self.coins = self.coins + deltaCoins
oldcash = self.cash
self.cash = (action - 1) * (-self.cash - (self.price*self.coins))
self.coins = action * (oldcash/self.price + self.coins)
self.accountvalue = self.getAccountValue()
self.nowroi = self.getROI()
self.vals.append(self.accountvalue)
self.roi.append(self.nowroi)
self.actions.append(action)
self.row += 1
return self.getState()
def render(self):
return 1
def reset(self):
self.valperepisode.append(self.vals)
self.actionperepisode.append(self.actions)
self.roiperepisode.append(self.roi)
self.endingvalperepisode.append(self.vals[-1:])
self.num_episodes +=1
self.row =0
self.cash = self.initcash
self.coins = 0
self.price = 0
self.start = time.time()
self.failstate = False
self.state = np.array(self.data[0])
self.vals = []
self.actions = []
self.roi = []
self.accountvalue = self.initcash
self.nowroi = 1
return self.state
from stable_baselines3.common.callbacks import BaseCallback
class TensorboardCallback(BaseCallback):
"""
Custom callback for plotting additional values in tensorboard.
"""
def __init__(self, verbose=0):
super(TensorboardCallback, self).__init__(verbose)
def _init_callback(self) -> None:
self.rowlength = len(self.training_env.get_attr('data')[0])
def _on_rollout_end(self) -> None:
pass
def _on_training_end(self) -> None:
file = open('trainingdata.pkl','wb')
numepisodes = self.training_env.get_attr('num_episodes')
valperepisode = self.training_env.get_attr('valperepisode')
actionperepisode = self.training_env.get_attr('actionperepisode')
roiperepisode = self.training_env.get_attr('roiperepisode')
endingvalperepisode = self.training_env.get_attr('endingvalperepisode')
# jsonobj = {'episodes' : numepisodes, 'valperepisode' : valperepisode, 'actionperepisode' : actionperepisode,
# 'roiperepisode' : roiperepisode, 'endingvalperepisode' : endingvalperepisode}
# with open('training_data.json', 'w') as outfile:
# json.dump(jsonobj, outfile)
pickle.dump(numepisodes, file)
pickle.dump(valperepisode, file)
pickle.dump(actionperepisode, file)
pickle.dump(roiperepisode, file)
pickle.dump(endingvalperepisode, file)
file.close()
pass
def _on_step(self) -> bool:
if(self.rowlength and (self.n_calls % self.rowlength) == 0):
endingvaluelist = self.training_env.get_attr('endingvalperepisode')
endingvaluelist = [endingvaluelist[-1:] for endingvaluelist in endingvaluelist ]
self.logger.record('endingval', np.mean(endingvaluelist))
endingroi = self.training_env.get_attr('roiperepisode')
endingroi = [endingroi[-1:] for endingroi in endingroi ]
self.logger.record('endingroi', np.mean(endingroi))
return True