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deep_evolution.py
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deep_evolution.py
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# Copyright 2020 The fingym Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from fingym import fingym
from collections import deque
import matplotlib.pyplot as plt
import ray
import os
ray.init()
env = fingym.make('SPY-Daily-v0')
CONFIG = {
'env_name': 'SPY-Daily-v0',
# removing time frame, stocks owned and cash in hand
'state_size': env.state_dim - 3,
'max_shares_to_trade_at_once': 100,
'time_frame': 30,
'sigma': 0.1,
'learning_rate': 0.03,
'population_size': 400,
'iterations': 50,
'train': False,
'eval': True,
'log_actions': True
}
def get_state_as_change_percentage(state, next_state):
open = (next_state[2] - state[2]) / next_state[2]
high = (next_state[3] - state[3]) / next_state[3]
low = (next_state[4] - state[4]) / next_state[4]
close = (next_state[5] - state[5]) / next_state[5]
volume = (next_state[6] - state[6]) / next_state[6]
return [open, high, low, close, volume]
@ray.remote
def reward_function(weights):
time_frame = CONFIG['time_frame']
state_size = CONFIG['state_size']
model = Model(time_frame * state_size, 500, 3)
model.set_weights(weights)
agent = Agent(model,state_size, time_frame)
_,_,_,reward = run_agent(agent)
print('reward: ',reward)
return reward
def run_agent(agent):
env = fingym.make(CONFIG['env_name'])
log_actions = CONFIG['log_actions']
state = env.reset()
# Removed time element from state
state = np.delete(state, 2)
next_state, reward, done, info = env.step([0,0])
if len(next_state) > agent.state_size:
next_state = np.delete(next_state, 2)
state_as_percentages = get_state_as_change_percentage(state,next_state)
state = next_state
done = False
states_buy = []
states_sell = []
closes = []
i = 0
while not done:
closes.append(state[5])
action = agent.act(state_as_percentages)
#if log_actions:
#print('action: ',action)
#print('state: ',state)
next_state, reward, done, info = env.step(action)
if len(next_state) > agent.state_size:
next_state = np.delete(next_state, 2)
if action[0] == 1 and action[1] > 0 and state[1] > state[2]:
if log_actions:
print('stocks owned: ',state[0])
print('stocks to buy: ',action[1])
print('stock price: ',state[2])
print('cash in hand: ',state[1])
print('total value: ',info['cur_val'])
states_buy.append(i)
if action[0] == 2 and action[1] > 0 and state[0] > 0:
if log_actions:
print('stocks owned: ',state[0])
print('stocks to sell: ',action[1])
print('stock price: ',state[2])
print('cash in hand: ',state[1])
print('total value: ',info['cur_val'])
states_sell.append(i)
state_as_percentages = get_state_as_change_percentage(state, next_state)
state = next_state
i+=1
return closes, states_buy, states_sell, info['cur_val']
class Deep_Evolution_Strategy:
def __init__(self, weights):
self.weights = weights
self.population_size = CONFIG['population_size']
self.sigma = CONFIG['sigma']
self.learning_rate = CONFIG['learning_rate']
def _get_weight_from_population(self,weights, population):
weights_population = []
for index, i in enumerate(population):
jittered = self.sigma * i
weights_population.append(weights[index] + jittered)
return weights_population
def get_weights(self):
return self.weights
def train(self,epoch = 500, print_every=1):
for i in range(epoch):
population = []
rewards = np.zeros(self.population_size)
for k in range(self.population_size):
x = []
for w in self.weights:
x.append(np.random.randn(*w.shape))
population.append(x)
futures = [reward_function.remote(self._get_weight_from_population(self.weights,population[k])) for k in range(self.population_size)]
rewards = ray.get(futures)
rewards = (rewards - np.mean(rewards)) / np.std(rewards)
for index, w in enumerate(self.weights):
A = np.array([p[index] for p in population])
self.weights[index] = (
w + self.learning_rate / (self.population_size * self.sigma) * np.dot(A.T, rewards).T
)
if (i + 1) % print_every == 0:
print('iter: {}. standard reward: {}'.format(i+1,ray.get(reward_function.remote((self.weights)))))
class Agent:
def __init__(self, model, state_size, time_frame):
self.model = model
self.time_frame = time_frame
self.state_size = state_size
self.state_fifo = deque(maxlen=self.time_frame)
self.max_shares_to_trade_at_once = CONFIG['max_shares_to_trade_at_once']
self.des = Deep_Evolution_Strategy(self.model.get_weights())
def act(self,state):
self.state_fifo.append(state)
# do nothing for the first time frames until we can start the prediction
if len(self.state_fifo) < self.time_frame:
return np.zeros(2)
state = np.array(list(self.state_fifo))
state = np.reshape(state,(self.state_size*self.time_frame,1))
#print(state)
decision, buy = self.model.predict(state.T)
# print('decision: ', decision)
# print('buy: ', buy)
return [np.argmax(decision[0]), min(self.max_shares_to_trade_at_once,max(int(buy[0]),0))]
def fit(self, iterations, checkpoint):
self.des.train(iterations, print_every = checkpoint)
class Model:
def __init__(self, input_size, layer_size, output_size):
self.weights = [
np.random.randn(input_size, layer_size),
np.random.randn(layer_size, output_size),
np.random.randn(layer_size, 1),
np.random.randn(1, layer_size)
]
def predict(self, inputs):
feed = np.dot(inputs, self.weights[0]) + self.weights[-1]
decision = np.dot(feed, self.weights[1])
buy = np.dot(feed, self.weights[2])
return decision, buy
def get_weights(self):
return self.weights
def set_weights(self, weights):
self.weights = weights
if __name__ == '__main__':
train = False
eval = False
time_frame = CONFIG['time_frame']
state_size = CONFIG['state_size']
model = Model(time_frame * state_size, 500, 3)
dirname = os.path.dirname(__file__)
weights_file = os.path.join(dirname,'deep_evo_weights.npy')
if os.path.exists(weights_file):
print('loading weights')
weights = np.load(weights_file,allow_pickle=True)
model.set_weights(weights)
# np.save(weights_file,model.get_weights())
agent = Agent(model,state_size, time_frame)
if CONFIG['train']:
agent.fit(iterations=CONFIG['iterations'], checkpoint=10)
agent.model.set_weights(agent.des.get_weights())
np.save(weights_file, agent.des.get_weights())
if CONFIG['eval']:
closes, states_buy, states_sell, result = run_agent(agent)
print('result: {}'.format(str(result)))
plt.figure(figsize = (20, 10))
plt.plot(closes, label = 'true close', c = 'g')
plt.plot(closes, 'X', label = 'predict buy', markevery = states_buy, c = 'b')
plt.plot(closes, 'o', label = 'predict sell', markevery = states_sell, c = 'r')
plt.legend()
plt.show()