main.py

from fastapi import FastAPI
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json

# import seaborn as sns
import yfinance as yf
import datetime as dt
import xgboost as xgb
from sklearn.metrics import mean_squared_error

app = FastAPI()


@app.get("/")
def read_root():
    return {
        "message": "Hello, Please type a ticker at the end of the URL to get LAST TRADING HOUR FORCAST.",
        "format": "https://yaakovy-lasthourforcast.hf.space/ticker/[TICKER]",
        "example": "https://yaakovy-lasthourforcast.hf.space/ticker/msft",
    }


def get_data(ticker):
    # Define the ticker symbol
    tickerSymbol = ticker
    days_period = 300

    # Get data on this ticker
    tickerData = yf.Ticker(tickerSymbol)

    start_date = dt.datetime.today() - dt.timedelta(days=days_period)
    end_date = dt.datetime.today()

    df_all = tickerData.history(start=start_date, end=end_date, interval="1h")
    df_all = df_all.drop(columns=["Dividends", "Stock Splits", "Volume"])
    return df_all


def get_last_date_missing_hours(df):
    # Assuming df is your DataFrame with the correct datetime index
    df.index = pd.to_datetime(df.index)  # Ensure datetime format

    # Define the trading hours
    trading_start = "09:30:00"
    trading_end = "16:00:00"

    # Normalize the timezone if necessary, here assuming the data might be timezone aware
    df.index = df.index.tz_localize(None)

    # Find the latest date in your data
    latest_date = df.index.max().date()

    # Generate a full range of expected trading hours for the latest date, ensuring it's timezone-naive
    expected_hours = pd.date_range(
        start=f"{latest_date} {trading_start}",
        end=f"{latest_date} {trading_end}",
        freq="H",
        tz=None,
    )

    # Extract actual timestamps for the latest date, also as timezone-naive
    actual_hours = df[df.index.date == latest_date].index.tz_localize(None)

    # Determine missing hours
    missing_hours = expected_hours.difference(actual_hours)
    # Add missing hours to the DataFrame as empty rows
    for hour in missing_hours:
        if hour not in df.index:
            df.loc[hour] = [pd.NA] * len(df.columns)  # Initialize missing hours with NA

    # Sort the DataFrame after inserting new rows to maintain the chronological order
    df.sort_index(inplace=True)

    # forward filling
    # Ensure the index is in datetime format and normalized
    df.index = pd.to_datetime(df.index)
    df.index = df.index.tz_localize(None)

    # Find the latest date in your data
    latest_date = df.index.max().date()

    # Select only the data for the latest day
    latest_day_data = df[df.index.date == latest_date]

    # Perform forward filling on this latest day data
    latest_day_data_filled = latest_day_data.ffill()

    # Replace the original latest day data in the DataFrame with the filled data
    df.loc[df.index.date == latest_date] = latest_day_data_filled

    # Optionally, ensure the entire DataFrame is sorted by index
    df.sort_index(inplace=True)
    return df


def prepare_df_for_model(df):
    df.index = pd.to_datetime(df.index)  # Ensure the index is datetime

    # Extract date and time from the datetime index
    df["Date"] = df.index.date
    df["Time"] = df.index.time

    # Filter out data for hours from 09:30 to 14:30 and the target at 15:30
    df_hours = df[
        df["Time"].isin(
            [
                pd.to_datetime("09:30:00").time(),
                pd.to_datetime("10:30:00").time(),
                pd.to_datetime("11:30:00").time(),
                pd.to_datetime("12:30:00").time(),
                pd.to_datetime("13:30:00").time(),
                pd.to_datetime("14:30:00").time(),
            ]
        )
    ]
    df_target = df[df["Time"] == pd.to_datetime("15:30:00").time()][["Date", "Close"]]

    # Rename the target close column for clarity
    df_target.rename(columns={"Close": "Close_target"}, inplace=True)

    # Pivot the hours data to have one row per day with all the columns
    df_pivot = df_hours.pivot(
        index="Date", columns="Time", values=["Open", "High", "Low", "Close"]
    )

    # Flatten the columns after pivoting and create a multi-level index
    df_pivot.columns = [
        "{}_{}".format(feature, time.strftime("%H:%M"))
        for feature, time in df_pivot.columns
    ]

    # Join the pivot table with the target data
    df_final = df_pivot.join(df_target.set_index("Date"))

    # Convert the index back to datetime if it got changed to object type
    df_final.index = pd.to_datetime(df_final.index)

    df = df_final.dropna()
    return df


def high_low_columns(df_final):
    # Extract columns for 'High' and 'Low' values
    high_columns = [col for col in df_final.columns if "High_" in col]
    low_columns = [col for col in df_final.columns if "Low_" in col]

    # Calculate 'max high' and 'min low' for each day
    df_final["MAX_high"] = df_final[high_columns].max(axis=1)
    df_final["MIN_low"] = df_final[low_columns].min(axis=1)

    return df_final


def calc_percentage_change(df):
    # Convert index to datetime if necessary (if not already done)
    df.index = pd.to_datetime(df.index)

    # Calculate the percentage change relative to 'Open_09:30' for each column
    for column in df.columns:
        if column != "Open_09:30":
            df[column] = (df[column] - df["Open_09:30"]) / df["Open_09:30"] * 100
    return df


def create_features(df):
    """
    Create time series features based on time series index.
    """
    df = df.copy()
    df["dayofweek"] = df.index.dayofweek
    df["quarter"] = df.index.quarter
    df["month"] = df.index.month
    df["year"] = df.index.year
    df["dayofyear"] = df.index.dayofyear
    df["dayofmonth"] = df.index.day
    df["weekofyear"] = df.index.isocalendar().week
    df["weekofyear"] = df["weekofyear"].astype("Int32")
    return df


def train_test_split(df):
    df.index = pd.to_datetime(df.index)
    # Define the number of test instances (e.g., last 30 days)
    num_test = 30

    # Split data into features and target
    X = df.drop(columns=["Close_target"])
    y = df["Close_target"]

    # Split the data into training and testing sets
    X_train, y_train = X[:-num_test], y[:-num_test]
    X_test, y_test = X[-num_test:], y[-num_test:]

    # Train indices are earlier, and test indices include the last date
    train_indices = df.index < df.index[-num_test]
    test_indices = df.index >= df.index[-num_test]
    return X_train, y_train, X_test, y_test


def run_xgboost(df):
    X_train, y_train, X_test, y_test = train_test_split(df)
    # Define the model
    model = xgb.XGBRegressor(
        n_estimators=100,
        learning_rate=0.1,
        max_depth=3,
        subsample=0.8,
        colsample_bytree=0.8,
        objective="reg:squarederror",
    )

    # Train the model with evaluation
    model.fit(
        X_train,
        y_train,
        eval_metric="rmse",
        eval_set=[(X_train, y_train), (X_test, y_test)],
        verbose=True,
        early_stopping_rounds=10,
    )

    # Making predictions
    predictions = model.predict(X_test)

    # Prediction for the latest date
    latest_prediction = predictions[-1]

    # Calculate and print RMSE for the test set
    rmse = np.sqrt(mean_squared_error(y_test, predictions))
    np_float = np.float32(rmse)
    return {"latest_prediction": latest_prediction, "RMSE": float(np_float)}


@app.get("/ticker/{ticker}")
def prcess_ticker(ticker: str):
    df = get_data(ticker)
    df = get_last_date_missing_hours(df)
    df = prepare_df_for_model(df)
    df = high_low_columns(df)
    df = calc_percentage_change(df)
    df = create_features(df)
    result = run_xgboost(df)

    return json.dumps(result, cls=NumpyEncoder)