def test_model(episode_count, data_test, data_test_open, start_balance,
model_name):
# Define arrays to store per episode values
Act_datasize = len(data_test)
Act_Bench_Stock1_Bal = int(
np.floor((start_balance / 2) / data_test_open[0]))
Act_Bench_Open_cash = start_balance / 2
model = load_model("models/" + model_name)
# Actual run
episode_count = 0
# Define arrays to store per episode values
total_Prof = []
total_stock1bal = []
total_open_cash = []
total_port_value = []
total_days_played = []
Act_total_Prof = []
Act_total_stock1bal = []
Act_total_open_cash = []
Act_total_port_value = []
Act_total_days_played = []
actions_done_perday = []
portfolio_value = []
for e in range(1): # here we run only for 1 episode, as it is Test run
Bal_stock1_t2 = Act_Bench_Stock1_Bal
done = False
open_cash_t2 = Act_Bench_Open_cash
total_profit = 0
reward = 0
# Initialize Agent
agent_test = Agent(8, is_eval=True, model_name=model_name)
# agent = Agent(8)
agent_test.inventory1 = []
for i in range(Bal_stock1_t2):
agent_test.inventory1.append(data_test_open[0])
# Timestep delta to make sure that with time reward increases for taking action
timestep_delta = 0
# Running episode over all days in the datasize
for t in range(Act_datasize):
print("..........")
print(data_test.iloc[t, 0])
state_class_obj = State(data_test_open, Bal_stock1_t2,
open_cash_t2, t)
state_array_obj = state_class_obj.getState()
action = agent_test.act(state_array_obj)
print("Total portfolio value: " +
str(state_class_obj.portfolio_value) + " stock 1 number: " +
str(len(agent_test.inventory1)) + " open cash" +
str(state_class_obj.open_cash))
# reward should be more as time goes further. We will remove reward_timedelta from actual reward
# reward_timedelta=(datasize-t)*timestep_delta
change_percent_stock1 = (state_class_obj.Stock1Price -
state_class_obj.fiveday_stock1
) / state_class_obj.fiveday_stock1 * 100
# print("change_percent_stock1: "+str(change_percent_stock1))
# print("change_percent_stock2: "+str(change_percent_stock2))
if action == 0: # buy stock 1
if state_class_obj.Stock1Price > state_class_obj.open_cash:
'''
print("Buy stock 1 when it did not have cash, so bankrupt, end of episode")
reward=-reward_timedelta*10
done = True
'''
done = True
# end episode
else:
# print("In Buy stock 1")
agent_test.inventory1.append(data_test_open[t])
Bal_stock1_t2 = len(agent_test.inventory1)
open_cash_t2 = state_class_obj.open_cash - state_class_obj.Stock1Price # Here we are buying 1 stock
if action == 1: # sell stock 1
if state_class_obj.Stock1Blnc < 1:
# print("sold stock 2 when it did not have stock 2, so bankrupt, end of episode")
done = True
# end episode
else:
# print("In sell stock 1")
agent_test.inventory1.pop(0)
Bal_stock1_t2 = len(agent_test.inventory1)
# Bal_stock2_t2 = len(agent_test.inventory2)
open_cash_t2 = state_class_obj.open_cash + state_class_obj.Stock1Price # State[0] is the price of stock 1. Here we are buying 1 stoc
if action == 2: # Do nothing action
Bal_stock1_t2 = len(agent_test.inventory1)
# Bal_stock2_t2 = len(agent_test.inventory2)
# print("Do nothing")
if t == Act_datasize - 1:
# print("t==datasize")
done = True
next_state_class_obj = State(data_test_open, Bal_stock1_t2,
open_cash_t2, t)
next_state_array_obj = next_state_class_obj.getState()
else:
# print("t!=datasize"+str(open_cash_t2))
next_state_class_obj = State(data_test_open, Bal_stock1_t2,
open_cash_t2, t + 1)
next_state_array_obj = next_state_class_obj.getState()
# print("Action is "+str(action)+" reward is" + str(reward))
actions_done_perday.append(action)
portfolio_value.append(next_state_class_obj.portfolio_value)
if done == True:
print("--------------------------------")
print("Total Profit: " +
formatPrice(next_state_class_obj.portfolio_value -
start_balance))
print("Total No. of days played: " + str(t) +
" out of overall days: " + str(Act_datasize))
print("Total portfolio value: " +
str(next_state_class_obj.portfolio_value) +
" stock 1 number: " + str(len(agent_test.inventory1)) +
" open cash" + str(next_state_class_obj.open_cash))
# + " stock 2 number: " + str(len(agent_test.inventory2))
Act_total_Prof.append(total_profit)
Act_total_stock1bal.append(len(agent_test.inventory1))
# Act_total_stock2bal.append(len(agent_test.inventory2))
Act_total_open_cash.append(state_class_obj.open_cash)
Act_total_port_value.append(state_class_obj.portfolio_value)
Act_total_days_played.append(t)
print("--------------------------------")
state_class_obj.reset()
break
opencash = state_class_obj.open_cash
return total_profit, portfolio_value, opencash, Act_total_days_played
def train_model(episode_count, start_balance, data_train, training, date):
from os import path
# Define arrays to store per episode values
total_Prof = []
total_stock1bal = []
total_open_cash = []
total_port_value = []
total_days_played = []
batch_size = 64
# Training run
for e in range(episode_count + 1):
print("..........")
print("Episode " + str(e) + "/" + str(episode_count))
Bal_stock1 = int(np.floor((start_balance / 2) / data_train[0]))
open_cash = start_balance / 2
datasize = training
done = False
total_profit = 0
reward = 0
max = 0
# Initialize Agent
agent = Agent(5)
agent.inventory1 = []
for i in range(Bal_stock1):
agent.inventory1.append(data_train[0])
# Timestep delta to make sure that with time reward increases for taking action
# timestep_delta=0
# Running episode over all days in the datasize
for t in range(datasize):
# print("..........")
# print(pd_data1_train.iloc[t,0])
state_class_obj = State(data_train, Bal_stock1, open_cash, t)
state_array_obj = state_class_obj.getState()
action = agent.act(state_array_obj)
change_percent_stock1 = (state_class_obj.Stock1Price -
state_class_obj.fiveday_stock1
) / state_class_obj.fiveday_stock1 * 100
# profit=data1_train[t]-agent.inventory1(-1)
# print("change_percent_stock1: "+str(change_percent_stock1))
# if action not in [0,1,2]:
# reward= reward-1000
# decide_reward(action,data_train)
if action == 0: # buy stock 1
if state_class_obj.Stock1Price > state_class_obj.open_cash:
'''
print("Buy stock 1 when it did not have cash, so bankrupt, end of episode")
reward=-reward_timedelta*10
done = True
'''
reward = reward - 4000
# done = True
# end episode
else:
# print("In Buy stock 1")
agent.inventory1.append(data_train[t])
Bal_stock1_t1 = len(agent.inventory1)
# Bal_stock2_t1 = len(agent.inventory2)
open_cash_t1 = state_class_obj.open_cash - state_class_obj.Stock1Price # Here we are buying 1 stock
# needs to be reviewed
if (state_class_obj.open_cash < 500):
reward = reward - 2000
elif (0.1 * Bal_stock1_t1 > Bal_stock1):
reward = reward - (1000 * Bal_stock1_t1)
# elif (abs(change_percent_stock1) <= 2):
# reward = reward-2000
else:
reward = reward - (change_percent_stock1 * 1000)
if action == 1: # sell stock 1
if state_class_obj.Stock1Blnc < 1:
# print("sold stock 2 when it did not have stock 2, so bankrupt, end of episode")
reward = reward - 4000
# done = True
# end episode
else:
# print("In sell stock 1")
bought_price1 = agent.inventory1.pop(0)
Bal_stock1_t1 = len(agent.inventory1)
total_profit += data_train[t] - bought_price1
# Bal_stock2_t1 = len(agent.inventory2)
open_cash_t1 = state_class_obj.open_cash + state_class_obj.Stock1Price # State[0] is the price of stock 1. Here we are selling 1 stoc
if (0.1 * Bal_stock1_t1 > Bal_stock1):
reward = reward - (1000 * Bal_stock1_t1)
# elif (abs(change_percent_stock1) <= 2):
# reward = -1000
elif total_profit > 200:
reward = reward + (2000 * total_profit)
else:
reward = reward + (
change_percent_stock1 * 100
) # State[0] is the price of stock 1. Here we are selling 1 stock
# total_profit += data1_train[t] - bought_price1
# print("reward for sell stock1 " + str(reward))
if action == 2: # Do nothing action
# if (abs(change_percent_stock1) <= 2):
# reward = 100
if (state_class_obj.open_cash < 0.05 * start_balance):
reward += 2000
else:
reward = reward - 2000
Bal_stock1_t1 = len(agent.inventory1)
# Bal_stock2_t1 = len(agent.inventory2)
open_cash_t1 = open_cash
# print("Do nothing")
if t == datasize - 1:
# print("t==datasize")
done = True
next_state_class_obj = State(data_train, Bal_stock1_t1,
open_cash_t1, t)
next_state_array_obj = next_state_class_obj.getState()
else:
next_state_class_obj = State(data_train, Bal_stock1_t1,
open_cash_t1, t + 1)
next_state_array_obj = next_state_class_obj.getState()
agent.memory.append(
(state_array_obj, action, reward, next_state_array_obj, done))
# print("Action is "+str(action)+" reward is" + str(reward))
Bal_stock1 = Bal_stock1_t1
# Bal_stock2 = Bal_stock2_t1
open_cash = open_cash_t1
if done == True:
total_Prof.append(total_profit)
total_stock1bal.append(len(agent.inventory1))
# total_stock2bal.append(len(agent.inventory2))
total_open_cash.append(state_class_obj.open_cash)
total_port_value.append(state_class_obj.portfolio_value)
total_days_played.append(t)
print("--------------------------------")
state_class_obj.reset()
break
if len(agent.memory) > batch_size:
agent.expReplay(batch_size)
print(reward)
if reward > max:
max = reward
agent.model.save("models/model_" + date + "-max")
if e % 30 == 0:
agent.model.save("models/model_" + date + "-" + str(e))
if path.exists("models/model_" + date + "-max"):
model_name = "model_" + date + "-max"
else:
model_name = "model_" + date + "-" + str(episode_count)
return model_name
