def target_percent(): trace = go.Pie(labels = ['健康样本','患病样本'], values = data['Outcome'].value_counts(), textfont=dict(size=15), opacity = 0.8, marker=dict(colors=['lightskyblue', 'gold'], line=dict(color='#000000', width=1.5))) layout = dict(title = '样本构成') fig = dict(data = [trace], layout=layout) py.iplot(fig)

这段代码定义了一个函数 `target_percent()`，它使用 Plotly 库中的 `go.Pie()` 函数来绘制一个饼图，该饼图显示数据集中患病样本和健康样本的百分比。`values` 参数使用了 `data['Outcome'].value_counts()`，它返回了一个 Pandas Series，其中包含了 `Outcome` 列中各个取值的计数。`labels` 参数设置了饼图中各个部分的标签，`colors` 参数设置了各个部分的颜色，`textfont` 参数设置了文本的字体大小，`opacity` 参数设置了图形的透明度，`line` 参数设置了饼图中各个部分的边框线的颜色和宽度。最后，函数将饼图及其布局打包成字典，调用 Plotly 的 `iplot()` 函数将其绘制出来。

相关问题

def target_percent(): trace = go.Pie(labels = ['healthy','diabetic'], values = data['Outcome'].value_counts(), textfont=dict(size=15), opacity = 0.8, marker=dict(colors=['lightskyblue', 'gold'], line=dict(color='#000000', width=1.5))) layout = dict(title = 'Distribution of Outcome variable') fig = dict(data = [trace], layout=layout) py.iplot(fig) p=sns.heatmap(data.corr(), annot=True,cmap ='hot')

这段代码看起来像是用Python的Plotly库和Seaborn库对数据进行可视化处理。其中，`go.Pie()`函数生成饼图，`data['Outcome'].value_counts()`是对数据中“Outcome”变量的统计，`sns.heatmap()`函数绘制热力图，`data.corr()`是对数据的相关性进行计算。这段代码的作用可能是展示数据的分布情况和相关性。

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)