def algorithm(draw, grid, start, end): count = 0 open_set = PriorityQueue() open_set.put((0, count, start)) came_from = {} g_score = {node: float("inf") for row in grid for node in row} g_score[start] = 0 f_score = {node: float("inf") for row in grid for node in row} f_score[start] = h(start.get_pos(), end.get_pos()) open_set_hash = {start} while not open_set.empty(): for event in pygame.event.get(): if event.type == pygame.QUIT: pygame.quit() current = open_set.get()[2] open_set_hash.remove(current) if current == end: reconstruct_path(came_from, end, draw) end.make_end() return True for neighbour in current.neighbours: temp_g_score = g_score[current] + 1 if temp_g_score < g_score[neighbour]: came_from[neighbour] = current g_score[neighbour] = temp_g_score f_score[neighbour] = temp_g_score + h(neighbour.get_pos(), end.get_pos()) if neighbour not in open_set_hash: count += 1 open_set.put((f_score[neighbour], count, neighbour)) open_set_hash.add(neighbour) neighbour.make_open() draw() if current != start: current.make_closed() return False解释这段代码

这段代码是实现 A* 算法的主要函数，输入参数包括绘图函数 draw、地图 grid、起点 start 和终点 end。在算法过程中，首先初始化一些变量，包括一个优先队列 open_set、一个字典 came_from、两个字典 g_score 和 f_score。open_set 用于保存待扩展节点，came_from 保存每个节点的前驱节点，g_score 和 f_score 分别表示从起点到每个节点的实际代价和估计代价。算法开始时将起点加入 open_set，然后不断从 open_set 中取出代价最小的节点进行扩展，扩展的过程中更新各个节点的代价和前驱节点，并将扩展出的节点加入 open_set，直到找到终点为止。如果找到了终点，则通过 came_from 字典回溯路径并绘制出路径；如果 open_set 已经空了但还没有找到终点，则说明不存在可行路径。其中，h() 函数是计算两个节点间的曼哈顿距离。整个函数的返回值为布尔值，表示是否找到了可行路径。

相关问题

while run: draw(win, grid, ROWS, width) for event in pygame.event.get(): if event.type == pygame.QUIT: run = False # 鼠标左键点击设置起点、终点或障碍物 if pygame.mouse.get_pressed()[0]: pos = pygame.mouse.get_pos() row, col = get_clicked_pos(pos, ROWS, width) node = grid[row][col] if not start and node != end: start = node start.make_start() elif not end and node != start: end = node end.make_end() elif node != end and node != start: node.make_barrier() # 鼠标右键点击删除起点、终点或障碍物 elif pygame.mouse.get_pressed()[2]: pos = pygame.mouse.get_pos() row, col = get_clicked_pos(pos, ROWS, width) node = grid[row][col] node.reset() if node == start: start = None elif node == end: end = None # 按下空格键开始或重新开始路径规划 if event.type == pygame.KEYDOWN: if event.key == pygame.K_SPACE and not started and start and end: for row in grid: for node in row: node.update_neighbours(grid) algorithm(lambda: draw(win, grid, ROWS, width), grid, start, end) # 按下C键清空地图 if event.key == pygame.K_c: start = None end = None grid = make_grid(ROWS, width) pygame.quit()这段代码的运行逻辑

这段代码实现了一个基于Pygame库的路径规划可视化程序。它通过绘制一个网格来表示地图，其中每个格子可以是起点、终点或障碍物。程序通过监听鼠标和键盘事件来实现用户交互，例如点击鼠标左键设置起点、终点或障碍物，点击鼠标右键删除起点、终点或障碍物，按下空格键开始或重新开始路径规划，按下C键清空地图。程序使用了一个算法来计算最短路径，并在可视化界面上展示出来。当算法运行时，程序会不断调用绘图函数来更新可视化界面，直到算法完成路径规划或者用户终止程序。

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)运行有错误

在代码中有一行缺少了冒号，应该在第一行的末尾添加一个冒号。另外，还需要导入run_algorithm函数，可以使用如下语句进行导入：

from zipline import run_algorithm

下面是修改后的代码：

import numpy as np
import pandas as pd
import talib
from zipline import run_algorithm

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)