def __search_decisive_price(self, side: Side):
percent = ""
if side is Side.BUY:
percent = "longCountPercent"
if side is Side.SELL:
percent = "shortCountPercent"
if not self.current_position_book_is_exist(
) or not self.before_position_book_is_exist():
return None
now_p_buckets = fx_lib.divide_buckets_up_and_down(
self.current_position_book()["buckets"], self.current_price())
before_p_buckets = fx_lib.divide_buckets_up_and_down(
self.before_position_book()["buckets"], self.current_price())
for i in range(self.params["target_range"]):
# low
now = now_p_buckets["low"][i][percent]
before = before_p_buckets["low"][i][percent]
position_growth_rate = self.calculate_position_growth_rate(
now, before)
if position_growth_rate >= self.params["entry_threshold"] \
and before > self.params["min_required_count_percent"]:
return now_p_buckets["low"][i]["price"]
# high
now = now_p_buckets["high"][i][percent]
before = before_p_buckets["high"][i][percent]
position_growth_rate = self.calculate_position_growth_rate(
now, before)
if position_growth_rate >= self.params["entry_threshold"] \
and before > self.params["min_required_count_percent"]:
return now_p_buckets["low"][i]["price"]
return None
def export_csv(self, f_name: str):
date_from = self.date_from
header = ["date_time", "price", "limit_short", "stop_short", "limit_long", "stop_long", "gan_short",
"loss_short", "gan_long", "loss_long"]
records = []
while date_from <= self.date_to:
record = [date_from, -1, -1, -1, -1, -1, -1, -1, -1, -1]
timestamp = date_from.timestamp()
if self.order_book_is_exist(timestamp):
order_book = self.order_book(timestamp)
record[1] = order_book["price"]
buckets = fx_lib.divide_buckets_up_and_down(order_book["buckets"], order_book["price"])
limit_short = 0
stop_short = 0
limit_long = 0
stop_long = 0
for i in range(10):
limit_short += buckets["high"][i]["shortCountPercent"]
stop_short += buckets["low"][i]["shortCountPercent"]
limit_long += buckets["low"][i]["longCountPercent"]
stop_long += buckets["high"][i]["longCountPercent"]
record[2] = limit_short
record[3] = stop_short
record[4] = limit_long
record[5] = stop_long
if self.position_book_is_exist(timestamp):
position_book = self.position_book(timestamp)
record[1] = position_book["price"]
buckets = fx_lib.divide_buckets_up_and_down(position_book["buckets"], position_book["price"])
gan_short = 0
loss_short = 0
gan_long = 0
loss_long = 0
for i in range(10):
gan_short += buckets["high"][i]["shortCountPercent"]
loss_short += buckets["low"][i]["shortCountPercent"]
gan_long += buckets["low"][i]["longCountPercent"]
loss_long += buckets["high"][i]["longCountPercent"]
record[6] = gan_short
record[7] = loss_short
record[8] = gan_long
record[9] = loss_long
records.append(record)
date_from = date_from + timedelta(minutes=20)
export_dir = "../result_data/book_csv/"
os.makedirs(export_dir, exist_ok=True)
pd.DataFrame(records, columns=header).to_csv(export_dir + f_name, index=False)
def create_new_orders_logic_type_b(instrument: Instrument, params: dict,
buckets: list, current_price: float,
target_range: int):
buckets = fx_lib.divide_buckets_up_and_down(buckets, current_price)
long = buckets["long"]
short = buckets["short"]
orders = []
if (len(long) <= target_range + len(params["stopOrders"])) or (
len(short) <= target_range + len(params["stopOrders"])):
return orders
# short order
for i in range(target_range):
activatable = True
for j in range(len(params["stopOrders"])):
so = params["stopOrders"][j]
if short[i + j]["shortCountPercent"] < so:
activatable = False
break
if activatable:
enter_price = short[i]["price"] + pips_to_price(
instrument, params["spreadAllowance"])
limit_price = enter_price - pips_to_price(
instrument, params["profitDistance"])
stop_price = enter_price + pips_to_price(
instrument, params["stopLossDistance"])
order = Order(instrument, Side.SELL, Type.MARKET_IF_TOUCHED, 1,
enter_price, limit_price, stop_price)
order.memo = short[i]
order.memo2 = short[i + 1]
order.memo3 = short[i + 2]
orders.append(order)
break
# long orders
for i in range(target_range):
activatable = True
for j in range(len(params["stopOrders"])):
lo = params["stopOrders"][j]
if long[i + j]["longCountPercent"] < lo:
activatable = False
break
if activatable:
enter_price = long[i]["price"] - pips_to_price(
instrument, params["spreadAllowance"])
limit_price = enter_price + pips_to_price(
instrument, params["profitDistance"])
stop_price = enter_price - pips_to_price(
instrument, params["stopLossDistance"])
order = Order(instrument, Side.BUY, Type.MARKET_IF_TOUCHED, 1,
enter_price, limit_price, stop_price)
order.memo = long[i]
order.memo2 = long[i + 1]
order.memo3 = long[i + 2]
orders.append(order)
break
return orders
def sum_sell_power(self):
if self.high_volatility_ob():
return 0
now_buckets = fx_lib.divide_buckets_up_and_down(
self.current_order_book()["buckets"], self.current_price())
sum_short = 0
for i in range(self.params["power_count_range"]):
sum_short += now_buckets["low"][i]["shortCountPercent"] # / (i+1)
return sum_short
def high_volatility_pb(self):
now_buckets = fx_lib.divide_buckets_up_and_down(
self.current_position_book()["buckets"], self.current_price())
if len(now_buckets["low"]) < 5 or len(now_buckets["high"]) < 5:
return True
low_distance = abs(self.current_price() -
now_buckets["low"][0]["price"])
high_distance = abs(self.current_price() -
now_buckets["high"][0]["price"])
return low_distance + high_distance > pips_to_price(self.instrument, 5)
def sum_sell_fuel(self):
if self.high_volatility_pb():
return 0
now_buckets = fx_lib.divide_buckets_up_and_down(
self.current_position_book()["buckets"], self.current_price())
sum_long = 0
invalid_range = 2
for i in range(invalid_range, self.params["fuel_count_range"]):
sum_long += now_buckets["high"][i]["longCountPercent"] # / (i+1)
return sum_long
def calculate_slope_of_trend(self):
buckets = fx_lib.divide_buckets_up_and_down(
self.before_position_book()["buckets"], self.current_price())
low = buckets["low"]
high = buckets["high"]
short_sum = 0
long_sum = 0
for i in range(self.params["target_range"]):
short_sum += low[i]["shortCountPercent"] + high[i][
"shortCountPercent"]
long_sum += low[i]["longCountPercent"] + high[i]["longCountPercent"]
return (long_sum - short_sum) / (long_sum + short_sum)
