写一个python代码来实现上面的要求

以下是使用Python和`pandas`, `numpy`, 和 `statsmodels` 库实现上述流程的一个简化示例：

import pandas as pd
import numpy as np
from statsmodels.tsa.arima_model import ARIMA

# Step 1: 下载数据
def download_data(symbol):
    # 使用tushare库，这里假设已经安装
    from tushare.pro_api import ProApi
    pro = ProApi(api_key='your_tushare_api_key', token='your_token')
    data = pro.daily(trade_date="2022", symbol=symbol)
    return data

# Step 2: 数据清洗
def clean_data(df):
    df['date'] = pd.to_datetime(df['trade_date'])
    df.set_index('date', inplace=True)
    df.dropna(inplace=True)  # 删除缺失值
    return df

# Step 3: 预处理
def preprocess_data(df):
    close_prices = df['close'].resample('D').ffill()  # 保留最后一天的收盘价
    return close_prices

# Step 4: 构建ARMA模型
def fit_arma(close_prices, p_max=5, q_max=5):
    arma_order = auto_arima(close_prices, start_p=p_max, start_q=q_max, seasonal=False, trace=True)
    arma_model = arma_order.model
    return arma_model

# Step 5: 参数估计
def estimate_params(arma_model):
    params = arma_model.params
    return params

# Step 6: 预测
def forecast(arma_model, n_periods):
    forecast_values = arma_model.forecast(steps=n_periods)
    return forecast_values

# 示例：
symbol = 'SH600000'  # 替换为你想要的股票代码
data = download_data(symbol)
cleaned_df = clean_data(data)
prices = preprocess_data(cleaned_df)
arma_model = fit_arma(prices)
params = estimate_params(arma_model)
forecast_values = forecast(arma_model, n_periods=10)