def main(stock_name, model_name):
# if len(sys.argv) != 3:
# print("Usage: python evaluate.py [stock] [model]")
# exit()
# stock_name, model_name = sys.argv[1], sys.argv[2]
model = load_model("models/" + model_name)
window_size = model.layers[0].input.shape.as_list()[1]
agent = Agent(window_size, True, model_name)
data = getStockDataVec(stock_name)
l = len(data) - 1
batch_size = 32
state = getState(data, 0, window_size + 1)
total_profit = 0
agent.inventory = []
for t in range(l):
action = agent.act(state)
# sit
next_state = getState(data, t + 1, window_size + 1)
reward = 0
if action == 1: # buy
agent.inventory.append(data[t])
print("Buy: " + formatPrice(data[t]))
elif action == 2 and len(agent.inventory) > 0: # sell
bought_price = agent.inventory.pop(0)
reward = max(data[t] - bought_price, 0)
total_profit += data[t] - bought_price
print("Sell: " + formatPrice(data[t]) + " | Profit: " +
formatPrice(data[t] - bought_price))
done = True if t == l - 1 else False
agent.memory.append((state, action, reward, next_state, done))
state = next_state
if done:
print("--------------------------------")
print(stock_name + " Total Profit: " + formatPrice(total_profit))
print("--------------------------------")
def Trainer(stock_name, window_size, episode_count):
agent = Agent(window_size)
data = getStockDataVec(stock_name)
l = len(data) - 1
batch_size = 32
for e in range(episode_count + 1):
print("Episode " + str(e) + "/" + str(episode_count))
state = getState(data, 0, window_size + 1)
total_profit = 0
agent.inventory = []
for t in range(l):
action = agent.act(state)
# sit
next_state = getState(data, t + 1, window_size + 1)
reward = 0
if action == 1: # buy
agent.inventory.append(data[t])
print("Buy: " + formatPrice(data[t]))
elif action == 2 and len(agent.inventory) > 0: # sell
bought_price = agent.inventory.pop(0)
reward = max(data[t] - bought_price, 0)
total_profit += data[t] - bought_price
print("Sell: " + formatPrice(data[t]) + " | Profit: " + formatPrice(data[t] - bought_price))
done = True if t == l - 1 else False
agent.memory.append((state, action, reward, next_state, done))
state = next_state
if done:
print("--------------------------------")
print("Total Profit: " + formatPrice(total_profit))
print("--------------------------------")
if len(agent.memory) > batch_size:
agent.expReplay(batch_size)
if e % 10 == 0:
agent.model.save("models/model_ep" + str(e))
print("Usage: python train.py [stock] [window] [episodes]")
exit()
stock_name, window_size, episode_count = sys.argv[1], int(sys.argv[2]), int(
sys.argv[3])
agent = Agent(window_size)
data = getStockDataVec(stock_name)
l = len(data) - 1
for e in range(episode_count + 1):
print("Episode " + str(e) + "/" + str(episode_count))
state = getState(data, 0, window_size + 1)
total_profit = 0
agent.inventory = []
for t in range(l):
action = agent.act(state)
# sit
next_state = getState(data, t + 1, window_size + 1)
reward = 0
if action == 1: # buy
agent.inventory.append(data[t])
# print("Buy: " + formatPrice(data[t]))
elif action == 2 and len(agent.inventory) > 0: # sell
bought_price = agent.inventory.pop(0)
reward = max(data[t] - bought_price, 0)
if len(sys.argv) != 3:
print "Usage: python evaluate.py [stock] [model]"
exit()
stock_name, model_name = sys.argv[1], sys.argv[2]
model = load_model("models/" + model_name)
window_size = model.layers[0].input.shape.as_list()[1]
agent = Agent(window_size, True, model_name)
data = getStockDataVec(stock_name)
l = len(data) - 1
batch_size = 32
state = getState(data, 0, window_size + 1)
total_profit = 0
agent.inventory = []
for t in xrange(l):
action = agent.act(state)
# sit
next_state = getState(data, t + 1, window_size + 1)
reward = 0
if action == 1: # buy
agent.inventory.append(data[t])
print "Buy: " + formatPrice(data[t])
elif action == 2 and len(agent.inventory) > 0: # sell
bought_price = agent.inventory.pop(0)
reward = max(data[t] - bought_price, 0)
stock_name, model_name = sys.argv[1], sys.argv[2]
model = load_model("models/" + model_name)
window_size = model.layers[0].input.shape.as_list()[1]
print("loading agent ...")
agent = {}
total_profit = 0
total_profit_array = np.zeros((1259))
buy_and_hold_array = np.zeros((1259))
#buy_and_hold = 0
#buy_and_hold_array = np.zeros((1259))
stocks_held = 0
for xxx, name in enumerate(data):
print(name)
agent[name] = Agent(window_size, True, model_name)
agent[name].inventory = []
print("starting model")
for day in range(days): #looping through days
print("Day:", day, "Money:", avaliable_money, "TP:", total_profit, "SH:",
stocks_held)
buy_and_hold = 0
for xxx, name in enumerate(data): #looping through each stock
buy_and_hold += (initial_avaliable_money /
len(data)) * (data[name][day] / data[name][0])
state = getState(data[name], day, window_size + 1)
action = agent[name].act(state)
#next_state = getState(data[name], day, window_size + 1)
reward = 0
if action == 1: # buy
if avaliable_money - data[name][day] > 0:
