import pandas as pd
import numpy as np
import talib
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split

def extract_features(df):
    # Calculate RSI
    df['rsi'] = talib.RSI(df['close'].values, timeperiod=14)

    # Calculate Bollinger Bands
    upperband, middleband, lowerband = talib.BBANDS(df['close'].values, timeperiod=14)
    df['bollinger_upper'] = upperband
    df['bollinger_middle'] = middleband
    df['bollinger_lower'] = lowerband

    # Calculate Moving Average
    df['ma50'] = talib.SMA(df['close'].values, timeperiod=50)

    return df

def label_data(df):
    # Mark the rows where the RSI value is above 70 as "divergence" (0)
    # Mark the rows where the RSI value is below 30 as "convergence" (1)
    df['label'] = np.where(df['rsi'] > 70, 0, 1)
    df['label'] = np.where(df['rsi'] < 30, 1, df['label'])

    return df

def load_data(filename):
    df = pd.read_csv(filename)
    df = extract_features(df)
    df = label_data(df)

    return df

def train_model(df):
    # Split data into training and testing sets
    X = df.drop(['label'], axis=1)
    y = df['label']
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

    # Train a decision tree model
    clf = DecisionTreeClassifier(random_state=0)
    clf.fit(X_train, y_train)

    # Test the model on the testing data
    accuracy = clf.score(X_test, y_test)
    print("Accuracy:", accuracy)

    return clf

def predict(clf, df):
    # Use the trained model to predict the labels for the test data
    X = df.drop(['label'], axis=1)
    y_pred = clf.predict(X)

    return y_pred

# Load the data from a CSV file
df = load_data('stock_data.csv')

# Train the model
clf = train_model(df)

# Use the trained model to predict the labels for new data
y_pred = predict(clf, df)







import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
import joblib

def load_data(file_name):
    df = pd.read_csv(file_name)
    return df

def preprocess_data(df):
    df = df.dropna()
    df['rsi'] = calculate_rsi(df)
    df['bolinger_band'] = calculate_bolinger_band(df)
    df['ma50'] = calculate_ma50(df)
    return df

def calculate_rsi(df):
    gain = df['close'].diff().where(df['close'].diff() > 0, 0)
    loss = df['close'].diff().where(df['close'].diff() < 0, 0)
    avg_gain = gain.rolling(14).mean()
    avg_loss = loss.rolling(14).mean().abs()
    rs = avg_gain / avg_loss
    rsi = 100 - (100 / (1 + rs))
    return rsi

def calculate_bolinger_band(df):
    rolling_mean = df['close'].rolling(window=20).mean()
    rolling_std = df['close'].rolling(window=20).std()
    bolinger_high = rolling_mean + (rolling_std * 2)
    bolinger_low = rolling_mean - (rolling_std * 2)
    bolinger_band = (df['close'] - bolinger_low) / (bolinger_high - bolinger_low)
    return bolinger_band

def calculate_ma50(df):
    ma50 = df['close'].rolling(window=50).mean()
    return ma50

def create_features_and_labels(df):
    df['price_pattern'] = np.where(df['close'].diff(1) > 0, 1, 0)
    df['rsi_pattern'] = np.where(df['rsi'].diff(1) > 0, 1, 0)
    X = df[['rsi_pattern', 'bolinger_band', 'ma50']]
    y = df['price_pattern']
    return X, y

def train_model(X_train, y_train):
    model = DecisionTreeClassifier()
    model.fit(X_train, y_train)
    return model

def evaluate_model(model, X_test, y_test):
    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    return accuracy

def save_model(model, file_name):
    joblib.dump(model, file_name)

def load_model(file_name):
    return joblib.load(file_name)

def write_result_to_csv(file_name, stock_name, result):
    with open(file_name, 'a') as f:
        f.write(f'{stock_name},{result}\n')

if __name__ == '__main__':
    stock_








# Get a list of all the CSV files in the directory
files = [f for f in os.listdir() if f.endswith('.csv')]

# Read each file into a separate DataFrame and store them in a list
dfs = [pd.read_csv(f) for f in files]

# Concatenate the DataFrames into a single DataFrame
df = pd.concat(dfs, ignore_index=True)