def bt(data, num_features, use_existing_model, model_name):
dqn = Dqn()
agent = Agent(num_features, use_existing_model, model_name)
state = dqn.get_state(data, num_features, num_features)
total_profits = 0
total_holds = 0
total_buys = 1
total_sells = 0
l = len(data) - 1
for t in range(num_features,l):
action = agent.choose_best_action(state)#it will always predict
reward, total_profits, total_holds, total_buys, total_sells = dqn.execute_action (action, data[t], t, total_profits, total_holds, total_buys, total_sells)
done = True if t == l - 1 else False
next_state = dqn.get_state(data, t + 1, num_features)
print(f'row #{t} {agent.actions[action]} @{data[t]}, state1={state}, state2={next_state}, reward={reward}')
state = next_state
if done:
print("-----------------------------------------")
print(f'Total Profit: {formatPrice(total_profits)} , Total hold/buy/exit trades: {total_holds} / {total_buys} / {total_sells}')
print("-----------------------------------------")
def learn(self, data, episodes, num_features, batch_size, use_existing_model, random_action_min=0.1, random_action_decay=0.99995, num_neurons=64, future_reward_importance=0.95):
agent = Agent(num_features, use_existing_model, '', random_action_min, random_action_decay, num_neurons, future_reward_importance)
l = len(data) - 1
rewards_vs_episode = []
profit_vs_episode = []
trades_vs_episode = []
epsilon_vs_episode = []
for episode in range(1,episodes + 1):
#print("Episode " + str(e) + "/" + str(episode_count))
state = self.get_state(data, num_features, num_features)
total_profits = 0
total_holds = 0
total_buys = 1
total_sells = 0
#total_rewards = 0
self.open_orders = [data[0]]
for t in range(num_features,l):
action = agent.choose_best_action(state)#tradeoff bw predict and random
#print(f'state={state}')
reward, total_profits, total_holds, total_buys, total_sells = self.execute_action (action, data[t], t, total_profits, total_holds, total_buys, total_sells)
done = True if t == l - 1 else False
next_state = self.get_state(data, t + 1, num_features)
print(f'row #{t} {agent.actions[action]} @{data[t]}, state1={state}, state2={next_state}, reward={reward}')
agent.remember(state, action, reward, next_state, done)#store contents of memory in buffer for future learning
state = next_state
if done:
eps = np.round(agent.epsilon,3)
print(f'Episode {episode}/{episodes} Total Profit: {formatPrice(total_profits)} , Total trades: {total_buys}, probability of random action: {eps}')
print("---------------------------------------")
#rewards_vs_episode.append(total_rewards)
profit_vs_episode.append(np.round(total_profits,4))
trades_vs_episode.append(total_buys)
epsilon_vs_episode.append(eps)
if len(agent.memory) > batch_size:#if memory of agent gets full:
agent.experience_replay(batch_size)#fit
#clean memory ?
# if episode % 1000 == 0:
# model_name = "files/output/model_ep" + str(episode)
# agent.model.save(model_name)
# print(f'{model_name} saved')
model_name = "files/output/model_ep" + str(episodes)
agent.model.save(model_name)
print(f'{model_name} saved')
return profit_vs_episode, trades_vs_episode, epsilon_vs_episode, model_name, agent.num_trains, agent.epsilon
def bt(data, n_features, use_existing_model, name_model):
dqn = Dqn()
dqn.open_orders = [data[0]]
agent = Agent(n_features, use_existing_model, name_model)
state = dqn.get_state(data, n_features, n_features)
total_profits = 0
total_holds = 0
total_buys = 1
total_sells = 0
total_notvalid = 0
l = len(data) - 1
for t in range(n_features, l):
action = agent.choose_best_action(state) # it will always predict
reward, total_profits, total_holds, total_buys, total_sells, total_notvalid = \
dqn.execute_action(action, data[t], t, total_profits, total_holds, total_buys, total_sells, total_notvalid)
done = True if t == l - 1 else False
next_state = dqn.get_state(data, t + 1, n_features)
#print(f'row #{t} {agent.actions[action]} @{data[t]}, state1={state}, state2={next_state}, reward={reward}')
state = next_state
if done:
# sell position at end of episode
reward, total_profits, total_holds, total_buys, total_sells, total_notvalid = \
dqn.execute_action(2, data[t+1], t+1, total_profits, total_holds, total_buys, total_sells,
total_notvalid)
print("-----------------------------------------")
print(
f'Total Profit: {formatPrice(total_profits*100)} ,'
f' Total hold/buy/sell/notvalid trades: {total_holds} / {total_buys} / {total_sells} / {total_notvalid}'
)
print("-----------------------------------------")
def learn(self,
data,
n_episodes,
n_features,
batch_size,
use_existing_model,
random_action_min=0.1,
random_action_decay=0.99995,
n_neurons=64,
future_reward_importance=0.95):
agent = Agent(n_features, use_existing_model, '', random_action_min,
random_action_decay, n_neurons, future_reward_importance)
l = len(data) - 1
rewards_vs_episode = []
profit_vs_episode = []
trades_vs_episode = []
epsilon_vs_episode = []
for episode in range(1, n_episodes + 1):
state = self.get_state(data, n_features, n_features)
total_profits = 0
total_holds = 0
total_buys = 1
total_sells = 0
total_notvalid = 0 # add-on buys or sells without previous buy
# total_rewards = 0
self.open_orders = [data[0]]
for t in range(n_features, l):
action = agent.choose_best_action(
state) # tradeoff bw predict and random
# print(f'state={state}')
reward, total_profits, total_holds, total_buys, total_sells, total_notvalid = \
self.execute_action(action, data[t], t, total_profits, total_holds, total_buys, total_sells,
total_notvalid)
done = True if t == l - 1 else False
next_state = self.get_state(data, t + 1, n_features)
#if len(self.open_orders) > 0: # if long add next state return as reward
#print(action, agent.actions[action])
if agent.actions[action] == 'buy':
immediate_reward = next_state[0][-1]
elif agent.actions[action] == 'sell':
immediate_reward = -next_state[0][-1]
else:
immediate_reward = 0
#print("Immediate reward:{0:.5f} Reward:{1:.5f} Time:{2} Price:{3} Action:{4}".
# format(immediate_reward, reward, t, data[t], agent.actions[action]))
#reward = reward + immediate_reward
reward = immediate_reward
#print(f'row #{t} {agent.actions[action]} @{data[t]}, state1={state}, state2={next_state}, reward={reward}')
# store contents of memory in buffer for future learning
agent.remember(state, action, reward, next_state, done)
state = next_state
if done:
# sell position at end of episode
reward, total_profits, total_holds, total_buys, total_sells, total_notvalid = \
self.execute_action(2, data[t+1], t+1, total_profits, total_holds, total_buys, total_sells,
total_notvalid)
eps = np.round(agent.epsilon, 3)
print(
f'Episode {episode}/{n_episodes} Total Profit: {formatPrice(total_profits * 100)},'
f' Total hold/buy/sell/notvalid trades: {total_holds} / {total_buys} / {total_sells} / {total_notvalid},'
f' probability of random action: {eps}')
print("---------------------------------------")
# rewards_vs_episode.append(total_rewards)
profit_vs_episode.append(np.round(total_profits, 4))
trades_vs_episode.append(total_buys)
epsilon_vs_episode.append(eps)
if len(agent.memory
) >= batch_size: # if enough recorded memory available
agent.experience_replay(batch_size) # fit
# clean memory ?
model_name = "files/output/model_ep" + str(n_episodes)
agent.model.save(model_name)
print(f'{model_name} saved')
return profit_vs_episode, trades_vs_episode, epsilon_vs_episode, model_name, agent.num_trains, agent.epsilon