def test_order_target_value(self):
feature_df, price_s = create_toy_feature(order_book_ids_number=3,
feature_number=3,
random_seed=111)
data_source = DataSource(feature_df=feature_df, price_s=price_s)
all_order_book_ids = data_source.get_available_order_book_ids()
print(all_order_book_ids)
STOCK_INIT_CASH = 1000000
env = TradingEnv(data_source=data_source,
look_backward_window=2,
mode="non-rl",
starting_cash={"STOCK": STOCK_INIT_CASH},
commission_multiplier=0,
min_commission=0,
tax_multiplier=0)
state = env.reset()
order_book_id = "000001.XSHE"
print(
"-----------------------current trading dt: {}-----------------------------------"
.format(Context.get_instance().trading_dt))
last_price = Context.get_instance().get_last_price(order_book_id)
to_submit_orders1 = order_target_value(order_book_id, 500000)
expect_quantity = int((500000 / last_price) / 100) * 100
assert expect_quantity == to_submit_orders1.quantity
self.assertEqual(first=expect_quantity,
second=to_submit_orders1.quantity)
state, reward, is_done, info = env.step(action=[to_submit_orders1])
current_positions0 = get_position(all_order_book_ids[0],
POSITION_DIRECTION.LONG)
print(
"-----------------------current trading dt: {}-----------------------------------"
.format(Context.get_instance().trading_dt))
last_price = Context.get_instance().get_last_price(order_book_id)
print(last_price)
#current_positions2 = get_position(all_order_book_ids[0], POSITION_DIRECTION.LONG)
to_submit_orders2 = order_target_value(order_book_id, 600000)
gap_value = 600000 - current_positions0.market_value
expect_quantity = int((gap_value / last_price) / 100) * 100
assert expect_quantity == to_submit_orders2.quantity
self.assertEqual(first=expect_quantity,
second=to_submit_orders2.quantity)
state, reward, is_done, info = env.step(action=[to_submit_orders2])
current_positions2 = get_position(all_order_book_ids[0],
POSITION_DIRECTION.LONG)
print(
"-----------------------current trading dt: {}-----------------------------------"
.format(Context.get_instance().trading_dt))
last_price = Context.get_instance().get_last_price(order_book_id)
print("price: {}".format(last_price))
# to_submit_orders3 = order_value(order_book_id, 100000)
to_submit_orders3 = order_target_value(order_book_id, 100000)
gap_value = 100000 - current_positions2.market_value
expect_quantity = abs(int((gap_value / last_price) / 100) * 100)
self.assertEqual(first=expect_quantity,
second=to_submit_orders3.quantity)
self.assertEqual(first=to_submit_orders3.side, second=SIDE.SELL)
self.assertEqual(first=to_submit_orders3.position_effect,
second=POSITION_EFFECT.CLOSE)
def your_strategy(state):
"""
here is a random strategy, only trade the first stock with a random target percent
"""
target_percent_of_postion = round(random.uniform(0, 1), 2)
# call trade API
target_pososition_dict = {the_first_stock_id: target_percent_of_postion}
print("the target portfolio is to be: {}".format(target_pososition_dict))
# call trade API
action = order_target_weights(target_pososition_dict)
return action
state = env.reset()
while True:
print("the current trading_dt is: {}".format(env.trading_dt))
action = your_strategy(state)
print("my action is: \n {}".format(action))
next_state, reward, done, info = env.step(action)
print("the reward of this action: {}".format(reward))
print("the extra info of this action: {}".format(info))
print("the next state is \n {}".format(next_state))
if done:
break
else:
state = next_state
def test_order_target_quantites(self):
feature_df, price_s = create_toy_feature(order_book_ids_number=3,
feature_number=3,
random_seed=111)
data_source = DataSource(feature_df=feature_df, price_s=price_s)
all_order_book_ids = data_source.get_available_order_book_ids()
print(all_order_book_ids)
STOCK_INIT_CASH = 1000000
env = TradingEnv(data_source=data_source,
look_backward_window=2,
mode="non-rl",
starting_cash={"STOCK": STOCK_INIT_CASH},
commission_multiplier=0,
min_commission=0,
tax_multiplier=0)
state = env.reset()
target_quantities1 = {
all_order_book_ids[0]: 500,
all_order_book_ids[1]: 600
}
to_submit_orders1 = order_target_quantities(target_quantities1)
state, reward, is_done, info = env.step(action=to_submit_orders1)
current_positions0 = get_position(all_order_book_ids[0],
POSITION_DIRECTION.LONG)
self.assertEqual(first=500, second=current_positions0.quantity)
current_positions1 = get_position(all_order_book_ids[1],
POSITION_DIRECTION.LONG)
self.assertEqual(first=600, second=current_positions1.quantity)
target_quantities2 = {
all_order_book_ids[0]: 800,
all_order_book_ids[1]: 400
}
to_submit_orders2 = order_target_quantities(target_quantities2)
state, reward, is_done, info = env.step(action=to_submit_orders2)
current_positions0 = get_position(all_order_book_ids[0],
POSITION_DIRECTION.LONG)
self.assertEqual(first=800, second=current_positions0.quantity)
current_positions1 = get_position(all_order_book_ids[1],
POSITION_DIRECTION.LONG)
self.assertEqual(first=400, second=current_positions1.quantity)
target_quantities3 = {
all_order_book_ids[0]: 200,
all_order_book_ids[2]: 300
}
to_submit_orders3 = order_target_quantities(target_quantities3)
state, reward, is_done, info = env.step(action=to_submit_orders3)
current_positions0 = get_position(all_order_book_ids[0],
POSITION_DIRECTION.LONG)
current_positions1 = get_position(all_order_book_ids[1],
POSITION_DIRECTION.LONG)
current_positions2 = get_position(all_order_book_ids[2],
POSITION_DIRECTION.LONG)
self.assertEqual(first=200, second=current_positions0.quantity)
self.assertEqual(first=0, second=current_positions1.quantity)
self.assertEqual(first=300, second=current_positions2.quantity)