def strategy_combination_a(start_time, end_time, init_balance):
# 写入strategy_log表
new_log = Log(strategy_id=1, start_date=start_time, end_date=end_time, init_balance=init_balance,
coin_category='btc', creator=1)
log_id = insert_2_strategy_log(new_log)
position = Position(start_date=start_time, end_date=end_time, balance=init_balance, strategy_type=1)
dates = position.datas['id']
dates = dates[dates >= start_time]
print(dates.size)
sell_a = SellA(start_time=start_time, end_time=end_time, position=position)
buy_a = BuyA(start_time=start_time, end_time=end_time, position=position)
for t in dates:
status = 0
sell_flag = sell_a.strategy(t, position)
position = sell_a.position
if sell_flag.flag is not 0:
status = 1
# todo signal 策略中的信号放到一个对象返回到这里
account_id_sell = account_insert(position=position, t=t, strategy_log_id=log_id, signal=int(sell_flag.signal),
transaction_status=status)
buy_flag = buy_a.strategy(t, position)
position = buy_a.position
if buy_flag is not 0:
status = 2
# todo signal 策略中的信号放到一个对象返回到这里
account_id_buy = account_insert(position=position, t=t, strategy_log_id=log_id, signal=int(buy_flag.signal),
transaction_status=status)
# 回写策略执行后总资产到strategy_log表
write_back2log(position, log_id)
def place_enemies_in_battlefield(battlefield, enemies):
for i in range(len(enemies)):
battlefield.add_new_character(enemies[i], Position(y=1, x=1 + i))
def place_characters_in_battlefield(battlefield, characters):
for i in range(len(characters)):
battlefield.add_new_character(characters[i], Position(y=3, x=1 + i))
def strategy_poc(strategy_id, start_time, end_time, init_balance, create_time, creator):
print('strategy_id' + str(strategy_id))
print('creator' + str(creator))
new_log = Log(strategy_id=strategy_id, start_date=start_time, end_date=end_time, create_time=create_time,
init_balance=init_balance,
coin_category='btc', creator=creator)
log_id = insert_2_strategy_log(new_log)
balance = init_balance
data = read_datas_1day_test(start_time - 777600, end_time + 86400)
if data.empty:
print('load data error!')
return False
df = get_strategy_conf_list(strategy_id)
if df.empty:
return False
pos = Position(start_date=start_time, end_date=end_time, balance=init_balance, strategy_type=0)
pos.datas = data
df_sell = df[df['5'] == 2]
df_buy = df[df['5'] == 1]
# 转换构造sell和buy的所有条件
sell_dict = create_conditions_dictionary(df_sell)
buy_dict = create_conditions_dictionary(df_buy)
position = 0.000000
# todo 初始化持仓
end_time_close = data[data['id'] == end_time].iat[0, 2]
id_list = data[data['id'] >= start_time]
id_list = id_list[id_list['id'] <= end_time]['id']
strategy_profit = 0.00
benchmark_profit = 0.00
for t in id_list:
data_t = data[data['id'] == t]
next_data_t = data[data['id'] == t + 86400]
close_t = data_t.iat[0, 2]
next_close_t = next_data_t.iat[0, 2]
# 返回价钱和数量signal
signal = sell_signal(t, sell_dict, data)
# 插入account表
pos.current_position = position
# print(str(position))
pos.balance = balance
account_id = account_insert(position=pos, t=t, strategy_log_id=log_id,
signal=int(signal.signal),
transaction_status=signal.signal)
# 最后一次t卖出
if t == end_time:
signal = Poc_signal(signal=2, price=Decimal(end_time_close).quantize(Decimal('0.000000')))
if signal.signal == 2 and position != 0:
# 卖出并返回余额
amount = position
pre_balance = balance
balance = Decimal(str(balance)).quantize(Decimal('0.00'))
balance += Decimal(str(position)) * Decimal(str(close_t))
position = 0
balance = Decimal(str(balance)).quantize(Decimal('0.00'))
# position对象更新
pos.balance = balance
pos.current_position = position
# todo 初始化运算器
calculator = Calculator.Calculator(pos, t, signal=0, strategy_id=strategy_id,
strategy_account_id=account_id)
calculator.transaction(flag=2, cost=close_t, amount=amount, pre_current_position=amount,
pre_balance=pre_balance)
# print('********sell*********')
# print('balance: ' + str(balance))
# print('position: ' + str(position))
# print('current profit: ' + str(
# ((balance + Decimal(str(position)) * Decimal(str(close_t))) - Decimal(str(init_balance))) / Decimal(str(
# init_balance))))
# print('\n')
signal = buy_signal(t, buy_dict, data)
# 最后一t不买
if t == end_time:
signal.signal = 0
if signal.signal == 1 and balance != 0:
# 买入并返回余额，买入数量
amount = (Decimal(str(balance)) / Decimal(str(close_t)))
amount *= 100000000
amount = floor(amount) / 100000000
# .quantize(Decimal('0.00000000'))
position = Decimal(str(amount))
pre_balance = balance
balance = Decimal(str(balance)).quantize(Decimal('0.00'))
balance -= Decimal(str(amount)) * Decimal(str(close_t))
balance = Decimal(str(balance)).quantize(Decimal('0.00'))
# balance = 0
# position对象更新
pos.balance = balance
pos.current_position = position
# todo 初始化运算器
calculator = Calculator.Calculator(pos, t, signal=0, strategy_id=strategy_id,
strategy_account_id=account_id)
calculator.transaction(flag=1, cost=close_t, amount=amount, pre_current_position=0,
pre_balance=pre_balance)
# print('********buy********')
# print('balance: ' + str(balance))
# print('position: ' + str(position))
# print('current profit: ' + str(((balance + position * Decimal(close_t)) - Decimal(init_balance)) / Decimal(
# init_balance)))
# print('\n')
cal_balance = Decimal(str(balance)).quantize(Decimal('0.00'))
cal_balance += Decimal(position) * Decimal(close_t)
# 策略收益率计算
strategy_profit = (cal_balance - Decimal(init_balance)) / Decimal(init_balance)
# print('strategy_profit='+str(strategy_profit))
# 基准收益率
df_start = data[data['id'] == start_time]
df_end = data[data['id'] == end_time]
close_start_t = Decimal(df_start.iat[0, 2])
benchmark_profit = (Decimal(str(close_t)) - close_start_t) / close_start_t
# print('benchmark_profit=' + str(benchmark_profit))
# pos.rate_of_return = strategy_profit
account_update_total_margin(strategy_profit, account_id)
# 回写策略执行后 收益率 到strategy_log表
write_back2log(margin=strategy_profit, benchmark=benchmark_profit, log_id=log_id)
# 计算并回写 最大回撤率 到strategy_log表
write_max_drawdown_back2log(log_id=log_id)
return Poc_response(strategy_profit=Decimal(str(strategy_profit)).quantize(Decimal('0.0000')),
benchmark_profit=Decimal(str(benchmark_profit)).quantize(Decimal('0.0000')),
log_id=log_id)
def update_MA(self, T):
start_timestamp_5T = T - 5 * 86400
start_timestamp_10T = T - 10 * 86400
pre_T = T - 86400
df_5 = self.datas[(self.idt >= start_timestamp_5T)]
df1_5 = df_5[df_5['id'] < pre_T]
df_10 = self.datas[(self.idt >= start_timestamp_10T)]
df1_10 = df_10[df_10['id'] < pre_T]
self.MA5 = df1_5['close'].mean()
self.MA10 = df1_10['close'].mean()
# get MA5(T-1) MA10(T-1）
def update_pre_MA(self, T):
start_timestamp_5T = T - 6 * 86400
start_timestamp_10T = T - 11 * 86400
pre_T = T - 2 * 86400
df_5 = self.datas[(self.idt >= start_timestamp_5T)]
df1_5 = df_5[df_5['id'] < pre_T]
df_10 = self.datas[(self.idt >= start_timestamp_10T)]
df1_10 = df_10[df_10['id'] < pre_T]
self.pre_MA5 = df1_5['close'].mean()
self.pre_MA10 = df1_10['close'].mean()
if __name__ == '__main__':
pos = Position(1509984000, 1515600000, 200, 1)
buy_a = BuyA(1509984000, 1515600000, pos)
buy_a.update_MA(1514995200)