Aug-07-2020, 08:39 PM
Hey Everyone!
I am having multiple errors with no traceback with this Q-Learning Trading Bot. Any help would be really appreciated!
Best Wishes,
Matt
I am having multiple errors with no traceback with this Q-Learning Trading Bot. Any help would be really appreciated!
Best Wishes,
Matt
# Import pandas datareader
import pandas_datareader
pandas_datareader.__version__
import pandas as pd
from pandas_datareader import data
import numpy as np
import random as random
import tensorflow as tf
# Set the start and end date
start_date = '2017-01-01'
end_date = '2019-02-01'
# Set the ticker
ticker = 'AMZN'
# Get the data
data = data.get_data_yahoo(ticker, start_date, end_date)
#print(data.head())
pricesdf = data.Close.to_string(index=False)
prices = (list(pricesdf.split()))[1:]
#print(prices)
class DecisionPolicy:
def select_action(self, current_state, step):
pass
def update_q(self, state, action, reward, next_state):
pass
class RandomDecisionPolicy(DecisionPolicy):
def __init__(self, actions):
self.actions = actions
def select_action(self, current_state, step):
action = self.actions[random.randint(0, len(self.actions) -1)]
#print(action)
return action
class QLearningDecisionPolicy(DecisionPolicy):
def __init__(self, actions, input_dim):
self.epsilon = .5
self.gamma = .001
self.actions = actions
output_dim = len(actions)
h1_dim = 200
self.sess = tf.Session(target='', graph=None, config=None)
self.x = tf.placeholder(tf.float32, [None, input_dim])
self.y = tf.placeholder(tf.float32, [output_dim])
W1 = tf.Variable(tf.random_normal([input_dim,h1_dim]))
b1= tf.Variable(tf.constant(0.1,shape=[h1_dim]))
h1 = tf.nn.relu(tf.matmul(self.x, W1) + b1)
W2 = tf.Variable(tf.random_normal([h1_dim,output_dim]))
b2 = tf.Variable(tf.constant(.1, shape=[output_dim]))
self.q = tf.nn.relu(tf.matmul(h1,W2) +b2)
loss = tf.square(self.y - self.q)
self.train_op = tf.train.GradientDescentOptimizer(.01).minimize(loss)
def select_action(self,current_state, step):
threshold = min(self.epsilon, step / 1000.)
if random.random() < threshold:
action_q_vals = self.sess.run(self.q,feed_dict={self.x: current_state})
action_idx = np.argmax(action_q_vals)
action = self.actions[action_idx]
else:
action = self.actions[random.randint(0,len(self.actions)-1)]
def run_simulation(policy, initial_budget, initial_num_stocks, prices, hist, debug=False):
budget = initial_budget
num_stocks = initial_num_stocks
share_value = 0
transitions = list()
for i in range(len(prices) - hist - 1):
if i % 100 == 0:
#print('progress {:.2f}%'.format(float(100*i) / (len(prices) - hist - 1)))
current_state = np.asmatrix(np.hstack((prices[i:i+hist], budget, num_stocks)))
current_portfolio = budget + num_stocks * share_value
action = policy.select_action(current_state, i)
share_value = float(prices[i + hist + 1])
if action == 'Buy' and budget >= share_value:
budget -= share_value
num_stocks += 1
elif action == 'Sell' and num_stocks > 0:
budget += share_value
num_stocks -= 1
else:
action = 'Hold'
new_portfolio = budget + num_stocks * share_value
reward = new_portfolio - current_portfolio
next_state = np.asmatrix(np.hstack((prices[i+1:i+hist+1],
budget, num_stocks)))
transitions.append((current_state, action, reward, next_state))
policy.update_q(current_state, action, reward, next_state)
portfolio = budget + num_stocks * share_value
if debug:
print('${}t{} shares'.format(budget, num_stocks))
return portfolio
def run_simulations(policy, budget, num_stocks, prices, hist):
num_tries = 100
final_portfolios = list()
for i in range(num_tries):
final_portfolio = run_simulation(policy, budget, num_stocks, prices, hist)
final_portfolios.append(final_portfolio)
avg, std = np.mean(final_portfolios), np.std(final_portfolios)
return avg, std
actions = ['Buy', 'Sell', 'Hold']
hist = 200
policy = QLearningDecisionPolicy(actions, input_dim = 202)
budget = 1000.0
num_stocks = 0
avg,std=run_simulations(policy,budget,num_stocks,prices, hist)
print(avg, std)
