def evaluate(model_name):
time_start = dt.now()
model = load_model(model_name) # Load the NN-agent model
state_size = model.layers[0].input.shape.as_list()[
1] # Load the state size from the model
window_size = int(state_size / 2)
env = Environment(ds_path=ds_path,
window_size=window_size,
pip_pos=pip_pos,
stop_loss=stop_loss_value,
trans_cost=trans_cost)
actions = env.get_actions(
) # Getting the available actions of the environment
actions_size = env.get_actions_n(
) # Getting the number of the actions available into the environment
agent = Agent(state_size=state_size,
action_size=actions_size,
is_eval=True,
model_name=model_name)
state, reward = env.step(
"Hold") # Making a first neutral action for get the first state
total_revenue = 0
while not env.done: # Loop until we finish all the instances
action = agent.act(
state) # The agent choose an action based on the current state
next_state, reward = env.step(
actions[action]
) # Getting the next state and reward based on the action choose
#with open(log, "a+") as file:
#file.write(str(actions[action]) + "\n") # Saving the performance on a file
#if env.stop_loss_triggered:
#file.write("Stop Loss Triggered!" + "\n") # Saving the stop loss taken on a file
#file.write(str(reward) + "\n") # Saving the performance on a file
'''print(colored("Observation:", 'blue'), state)
print(colored("Action:", 'yellow'), actions[action])
if env.stop_loss_triggered: # Alert when we got a stop loss from the environment
print(colored('Stop loss triggered!', 'red'))
print(colored("Next Observation:", 'blue'), next_state)
print(colored("Reward:", 'cyan'), reward)'''
total_revenue += reward
#agent.memory.append((state, action, reward, next_state)) # Saving the experience
state = next_state
#if len(agent.memory) > batch_size: # Making an analysis based on our experience
# agent.exp_replay(batch_size)
# ***************************** Showing and Saving the Results over a Single Episode *******************************
#print("-----------------------------------------------------------------------------------------------------------")
if total_revenue > 0:
print(colored("Total Profit: ", 'blue'),
colored(str(round(total_revenue, 1)), 'cyan'), "pips")
else:
print(colored("Total Profit: ", 'blue'),
colored(str(round(total_revenue, 1)), 'red'), "pips")
with open(performance_file_path, "a+") as file:
file.write(str(round(total_revenue, 1)) +
"\n") # Saving the performance on a file
time_stop = dt.now()
print(colored("Execution time for this episode:", 'yellow'),
round((time_stop - time_start).total_seconds(), 0), "seconds")
batch_size = 30 # The number of tuple (state, action, reward, next_state) to save before replay
stop_loss_value = -50 # The maximum loss that we can handle (expressed in pips)
performance_file_path = "performance/train_performance.txt" # Path where to store the training performance log file
log = "performance/train_log.txt" # Path where to store the training log file
models_path = "models/" # Path where are stored the models
n_prev_iterations = len(next(os.walk(
models_path))[2]) # Get the number of existent models in the models_path
setup(seed_value=7)
# ********************************* Creating the Agent Model and the Environment Model *********************************
env = Environment(ds_path=ds_path,
window_size=window_size,
pip_pos=pip_pos,
stop_loss=stop_loss_value,
trans_cost=trans_cost)
actions = env.get_actions() # Getting the available action of the environment
agent = Agent(env.get_state_size(), env.get_actions_n())
if os.path.exists(
performance_file_path
): # Checking if there are previous training performances saved
os.remove(performance_file_path) # Deleting the old train performances
if os.path.exists(
log): # Checking if there are previous training performances saved
os.remove(log) # Deleting the old train performances
print(dt.now())
print("stop loss:", stop_loss_value)
print("pc: BH")
# ********************************************* Looping over all Episodes ***************-******************************
for ep in range(n_episodes - n_prev_iterations):
time_start = dt.now()
