schedule.time == date不能直接比较

是的，您是正确的。在Python中，不能直接比较时间和日期对象，因为它们是不同的数据类型。您需要将它们转换为相同的类型，然后再进行比较。可以使用datetime模块中的函数来进行转换。例如，可以使用datetime.combine()函数将日期对象和时间对象组合成一个datetime对象，然后再进行比较。示例代码如下：

from datetime import datetime, date, time

# 创建日期对象和时间对象
d = date(2022, 10, 1)
t = time(10, 30)

# 将日期对象和时间对象组合成datetime对象
dt = datetime.combine(d, t)

# 比较datetime对象
if dt == datetime.now():
    print("时间匹配")
else:
    print("时间不匹配")

请注意，上面的示例代码中，datetime.now()返回的是当前时间的datetime对象。您可以将其替换为另一个datetime对象，以便与dt进行比较。

相关问题

import numpy as np import pandas as pd import talib def initialize(context): context.symbol = 'BTCUSDT' context.window_size = 5 context.deviation = 1 context.trade_size = 0.01 context.stop_loss = 0.05 context.take_profit = 0.1 schedule_function(rebalance, date_rules.every_day(), time_rules.market_open()) def rebalance(context, data): price = data.history(context.symbol, 'close', context.window_size + 1, '1d') signal = mean_reversion_signal(price, context.window_size, context.deviation) current_position = context.portfolio.positions[context.symbol].amount if signal[-1] == 1 and current_position <= 0: target_position_size = context.trade_size / data.current(context.symbol, 'close') order_target_percent(context.symbol, target_position_size) elif signal[-1] == -1 and current_position >= 0: order_target(context.symbol, 0) elif current_position > 0: current_price = data.current(context.symbol, 'close') stop_loss_price = current_price * (1 - context.stop_loss) take_profit_price = current_price * (1 + context.take_profit) if current_price <= stop_loss_price or current_price >= take_profit_price: order_target(context.symbol, 0) def moving_average(x, n): ma = talib.SMA(x, timeperiod=n) return ma def std_deviation(x, n): std = talib.STDDEV(x, timeperiod=n) return std def mean_reversion_signal(price, window_size, deviation): ma = moving_average(price, window_size) std = std_deviation(price, window_size) upper_band = ma + deviation * std lower_band = ma - deviation * std signal = np.zeros_like(price) signal[price > upper_band] = -1 # 卖出信号 signal[price < lower_band] = 1 # 买入信号 return signal ''' 运行回测 ''' start_date = pd.to_datetime('2019-01-01', utc=True) end_date = pd.to_datetime('2021-01-01', utc=True) results = run_algorithm( start=start_date, end=end_date, initialize=initialize, capital_base=10000, data_frequency='daily', bundle='binance' ) ''' 查看回测结果 ''' print(results.portfolio_value)格式错误

这段代码存在一些格式错误。具体来说，第一行和第二行之间需要有一个空行，否则会提示语法错误。另外，代码中的注释也需要使用 # 符号进行标记。

以下是修改后的代码：

import numpy as np
import pandas as pd
import talib

def initialize(context):
    context.symbol = 'BTCUSDT'
    context.window_size = 5
    context.deviation = 1
    context.trade_size = 0.01
    context.stop_loss = 0.05
    context.take_profit = 0.1
    
    schedule_function(rebalance, date_rules.every_day(), time_rules.market_open())

def rebalance(context, data):
    price = data.history(context.symbol, 'close', context.window_size + 1, '1d')
    signal = mean_reversion_signal(price, context.window_size, context.deviation)
    current_position = context.portfolio.positions[context.symbol].amount
    
    if signal[-1] == 1 and current_position <= 0:
        target_position_size = context.trade_size / data.current(context.symbol, 'close')
        order_target_percent(context.symbol, target_position_size)
    elif signal[-1] == -1 and current_position >= 0:
        order_target(context.symbol, 0)
    elif current_position > 0:
        current_price = data.current(context.symbol, 'close')
        stop_loss_price = current_price * (1 - context.stop_loss)
        take_profit_price = current_price * (1 + context.take_profit)
        
        if current_price <= stop_loss_price or current_price >= take_profit_price:
            order_target(context.symbol, 0)

def moving_average(x, n):
    ma = talib.SMA(x, timeperiod=n)
    return ma

def std_deviation(x, n):
    std = talib.STDDEV(x, timeperiod=n)
    return std

def mean_reversion_signal(price, window_size, deviation):
    ma = moving_average(price, window_size)
    std = std_deviation(price, window_size)
    upper_band = ma + deviation * std
    lower_band = ma - deviation * std
    
    signal = np.zeros_like(price)
    signal[price > upper_band] = -1  # 卖出信号
    signal[price < lower_band] = 1   # 买入信号
    
    return signal

''' 运行回测 '''
start_date = pd.to_datetime('2019-01-01', utc=True)
end_date = pd.to_datetime('2021-01-01', utc=True)

results = run_algorithm(
    start=start_date,
    end=end_date,
    initialize=initialize,
    capital_base=10000,
    data_frequency='daily',
    bundle='binance'
)

''' 查看回测结果 '''
print(results.portfolio_value)

# ！/usr/bin/env python # -*- coding：utf-8 -*- # author: haotian time:2019/9/14 import numpy as np f = open("./data/CD_Flight190914A.csv", "rb") excel = open("./data/time_flight.csv", "w+") # position_exl = open("./data/position_exl.csv", "w+") schedule = np.loadtxt(f, dtype=str, delimiter=",", skiprows=1, usecols=(4,)) # 分隔符 空格 Array = np.zeros(209) count = 1 i = 0 n = 0 while i < (len(schedule)-1): if schedule[i] == schedule[i + 1] : # 如果航班时间重复 创建一个不重复的时间表记录重复次数 count = count + 1 else: Array[n] = count #Array存的重复次数 count = 0 n = n + 1 i = i + 1 new_schedule,a = np.unique(schedule,return_index=True) #去掉相同时间的数据 # for i in range(len(position)): # position_exl.write(str(position[i])+',\n') # position_exl.close() # position_exl = open(("./data/position_exl.csv", "w+")) # position = np.loadtxt(position_exl, dtype=float, delimiter=",", skiprows=0, usecols=(0,)) # new_schedule = [len(position)*''] # n = 0 # numbers = [ int(x) for x in position ] # for i in range(numbers): # new_schedule[n] = schedule[i] # n = n + 1 excel.write("Schedule,PlaneNum"+'\n') for i in range(len(new_schedule)-1): excel.write(str(new_schedule[i])+","+str(Array[i])+",\n") excel.close() ''' 此时的数据time_flight.csv由于排序的原因导致时间的序列不一致， 最终数据用excel降序排列并保存到schedule_PlaneNum.csv中 '''完善并使其运行

这段代码是用来处理航班时间表的，读取 CD_Flight190914A.csv 文件中的航班时间数据，统计每个时间段内的航班数目，然后将数据保存到 time_flight.csv 文件中。其中，new_schedule 保存去重后的时间表，Array 数组保存每个时间段内的航班数目。

代码存在一些问题，比如：

1. 在写入文件时，每行最后多了一个逗号，需要去掉。
2. position_exl 文件的路径错误，应该是 "./data/position_exl.csv"。
3. position_exl 文件没有正确写入，应该是使用 f.write() 方法写入数据。
4. 关于 position_exl 文件的处理，代码中存在一些注释，但是没有被执行，需要根据需要进行修改。

修改后的代码如下：

#!/usr/bin/env python
# -*- coding:utf-8 -*-
# author: haotian time:2019/9/14

import numpy as np

f = open("./data/CD_Flight190914A.csv", "rb")
excel = open("./data/time_flight.csv", "w+")
position_exl = open("./data/position_exl.csv", "w+")

schedule = np.loadtxt(f, dtype=str, delimiter=",", skiprows=1, usecols=(4,))  # 分隔符 空格
Array = np.zeros(209)
count = 1
i = 0
n = 0

while i < (len(schedule)-1):
    if schedule[i] == schedule[i + 1]:
        count = count + 1
    else:
        Array[n] = count
        count = 0
        n = n + 1
    i = i + 1

# 将时间表去重
new_schedule, a = np.unique(schedule, return_index=True)

# 将位置信息写入到 position_exl 文件中
for i in range(len(new_schedule)):
    position_exl.write(str(i+1) + ',\n')
position_exl.close()

# 重新打开 position_exl 文件，并读取数据
position_exl = open("./data/position_exl.csv", "r")
position = np.loadtxt(position_exl, dtype=float, delimiter=",", skiprows=0, usecols=(0,))

# 将航班时间和航班数目写入到 excel 文件中
excel.write("Schedule,PlaneNum\n")
for i in range(len(new_schedule)):
    excel.write(str(new_schedule[i])+","+str(Array[i])+"\n")
excel.close()

# 将 excel 文件降序排列
data = np.loadtxt("./data/time_flight.csv", dtype=str, delimiter=",", skiprows=1)
data = data[data[:, 1].astype(int).argsort()[::-1]]

# 将排好序的数据保存到 schedule_PlaneNum.csv 文件中
np.savetxt("./data/schedule_PlaneNum.csv", data, delimiter=",", fmt="%s")