昨天写了一篇文章 【Python技术】使用akshare、pyecharts绘制K线图 ， 有同学私信我，怎么实现预测股票涨跌。 说实话，之前我没细想过，本能的回答用大数据机器学习就可以搞。 趁着下班，写个demo例子演示下
1、先导入必要的库:
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akshare: 用于获取股票数据
-
pandas: 用于数据处理和分析
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scikit-learn: 用于机器学习模型的训练和预测
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numpy: 用于数值计算
2、简单利用5日和20日移动平均线、RSI指标等指标，使用随机森林分类器模型。
备注：随机森林(Random Forest)是一种基于集成学习的机器学习算法，其目的是通过组合多个决策树模型来进行预测。随机森林通过对训练数据进行随机采样，以及在构建每个决策树节点时对特征进行随机选择，来增加模型的多样性和鲁棒性。
废话不多说，先上具体实现代码例子：
import akshare as ak
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import numpy as np
def fetch_stock_data(stock_code):
# Fetch daily stock data using akshare
# 新浪源 df = ak.stock_zh_a_daily(symbol=stock_code, adjust="qfq")
df = ak.stock_zh_a_hist(symbol=stock_code, adjust="qfq").iloc[:, :6]
df.columns = [
'date',
'open',
'close',
'high',
'low',
'volume',
]
df['date'] = pd.to_datetime(df['date'])
df = df.set_index('date').sort_index()
#print(df)
return df
def prepare_data(df):
# Calculate some basic technical indicators
df['SMA5'] = df['close'].rolling(window=5).mean()
df['SMA20'] = df['close'].rolling(window=20).mean()
df['RSI'] = calculate_rsi(df['close'], window=14)
# Create target variable (1 if price goes up, 0 if it goes down)
df['target'] = (df['close'].shift(-1) > df['close']).astype(int)
# Drop NaN values
df = df.dropna()
# Select features
features = ['open', 'high', 'low', 'close', 'volume', 'SMA5', 'SMA20', 'RSI']
X = df[features]
y = df['target']
return X, y
def calculate_rsi(prices, window=14):
delta = prices.diff()
gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
rs = gain / loss
return 100 - (100 / (1 + rs))
def train_model(X, y):
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Scale the features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# Train a Random Forest classifier
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train_scaled, y_train)
# Make predictions on the test set
y_pred = model.predict(X_test_scaled)
# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f"Model accuracy: {accuracy:.2f}")
return model, scaler
def predict_next_day(model, scaler, last_data):
last_data_scaled = scaler.transform(last_data.reshape(1, -1))
prediction = model.predict(last_data_scaled)
probability = model.predict_proba(last_data_scaled)
return prediction[0], probability[0]
def main(stock_code):
# Fetch stock data
df = fetch_stock_data(stock_code)
# Prepare data
X, y = prepare_data(df)
# Train model
model, scaler = train_model(X, y)
# Predict for the next day
last_data = X.iloc[-1].values
prediction, probability = predict_next_day(model, scaler, last_data)
if prediction == 1:
direction = "up"
else:
direction = "down"
print(f"Prediction for the next day: Stock is likely to go {direction}")
print(f"Probability: Up - {probability[1]:.2f}, Down - {probability[0]:.2f}")
if __name__ == "__main__":
#新浪源 stock_code = input("Enter the stock code (e.g., sh600000): ")
#东方财富源
stock_code = input("Enter the stock code (e.g., 600000): ")
main(stock_code)
运行代码：
Enter the stock code (e.g., 600000): 000001
Model accuracy: 0.53
Prediction for the next day: Stock is likely to go down
Probability: Up - 0.30, Down - 0.70
预测大盘明天下跌0.7的几率，难道大盘明天还要跌？希望这个算法是有问题的。
实际的股票市场预测要复杂得多,受到许多这个简单模型没有考虑到的因素影响。在进行实际投资决策时, 最好自己学习专业的金融知识，咨询专业人士。
原文链接：
【Python技术】使用akshare、scikit-learn预测股票涨跌简单例子
