升级import talib print(talib.__version__)

要升级 `talib` 库，你可以使用以下步骤：

1. 检查当前安装的 `talib` 版本：

import talib

print(talib.__version__)

2. 使用适合你的包管理工具（如 `pip` 或 `conda`）执行升级命令。

使用 `pip` 升级 `talib`：

   pip install --upgrade TA-Lib

使用 `conda` 升级 `talib`：

   conda install --upgrade ta-lib

3. 完成升级后，再次检查 `talib` 版本以确认升级成功。

希望这个步骤能帮助你升级 `talib` 库！如果还有其他问题，请随时提问。

相关问题

talib.stream_STOCHRSI

根据提供的引用内容，我们可以看出talib是一个用于技术分析的Python库，而STOCHRSI是其中的一个指标。stream_STOCHRSI是talib库中的一个函数，用于计算STOCHRSI指标。具体使用方法如下：

import talib
import numpy as np

# 假设有一组收盘价数据
close = np.random.random(100)

# 计算STOCHRSI指标
stochrsi = talib.stream_STOCHRSI(close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0)

# 输出结果
for s in stochrsi:
    print(s)

其中，close是收盘价数据，timeperiod表示RSI的时间周期，fastk_period表示STOCHRSI的K线时间周期，fastd_period表示STOCHRSI的D线时间周期，fastd_matype表示D线的计算方法。函数返回的是一个生成器对象，可以通过for循环逐个输出结果。

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)