import requests
import pandas as pd
import mplfinance as mpf
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
import os
import numpy as np
from PIL import Image
import tensorflow as tf
from tensorflow.keras import layers, models
from sklearn.metrics import accuracy_score, f1_score, recall_score, roc_auc_score, precision_recall_curve, auc
from sklearn.utils.class_weight import compute_class_weight
import argparse
import gc
import time
import shutil

# Use non-interactive backend for matplotlib
plt.switch_backend('Agg')

# Coin configurations
COINS = {
    "BTCUSDT": {"train_month": (2024, 6), "test_months": [(2024, 12), (2024, 3), (2024, 8), (2024, 4), (2024, 1)]},
    "ETHUSDT": {"train_month": (2024, 6), "test_months": [(2024, 8), (2024, 4), (2024, 5), (2024, 3), (2024, 2)]},
    "BNBUSDT": {"train_month": (2024, 10), "test_months": [(2024, 3), (2024, 12), (2024, 8), (2024, 1), (2024, 4)]},
    "XRPUSDT": {"train_month": (2024, 9), "test_months": [(2024, 11), (2024, 12), (2024, 4), (2024, 8), (2024, 1)]},
    "ADAUSDT": {"train_month": (2024, 9), "test_months": [(2024, 4), (2024, 12), (2024, 1), (2024, 3), (2024, 11)]},
    "DOGEUSDT": {"train_month": (2024, 9), "test_months": [(2024, 3), (2024, 4), (2024, 11), (2024, 8), (2024, 12)]}
}

TIME_LENGTHS = [7, 14, 21, 28]  # 1, 2, 3, 4 weeks in days
WINDOW_SIZES = [5, 15, 30]  # Candles per image

# Set BASE_DIR for new output
BASE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "crypto_research_minute_fullimage")
# Old directory for reusing data and images
OLD_BASE_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "crypto_research_minute")

# Binance API data fetcher (fixed to 1m interval) - Skipped since we reuse raw data
def fetch_coin_data(symbol, start_time, end_time):
    url = "https://api.binance.com/api/v3/klines"
    all_data = []
    current_start = int(start_time.timestamp() * 1000)
    end_ms = int(end_time.timestamp() * 1000)
    
    while current_start < end_ms:
        params = {"symbol": symbol, "interval": "1m", "startTime": current_start, "endTime": end_ms, "limit": 1000}
        response = requests.get(url, params=params)
        data = response.json()
        if not data:
            break
        all_data.extend(data)
        current_start = int(data[-1][0]) + 60000  # 1 minute in milliseconds
    
    df = pd.DataFrame(all_data, columns=["timestamp", "open", "high", "low", "close", "volume", "close_time", "quote_asset_volume", "trades", "taker_buy_base", "taker_buy_quote", "ignore"])
    df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
    df[["open", "high", "low", "close"]] = df[["open", "high", "low", "close"]].astype(float)
    return df[["timestamp", "open", "high", "low", "close"]]

# Generate candlestick images and labels with variable window size - Modified to reuse images
def generate_images(df, window_size, output_dir, period_name, month_str):
    os.makedirs(output_dir, exist_ok=True)
    labels_file = os.path.join(output_dir, f"labels_{month_str}_1m_{period_name}_w{window_size}.csv")
    old_images_dir = os.path.join(OLD_BASE_DIR, df.name, "images", f"{month_str}_1m_{period_name}_w{window_size}")
    
    # Check if images already exist in the old directory
    if os.path.exists(old_images_dir):
        print(f"Images already exist at {old_images_dir}, copying to {output_dir}")
        # Copy images to new directory
        if os.path.exists(output_dir) and os.path.samefile(old_images_dir, output_dir):
            print(f"Output directory {output_dir} is the same as source, skipping copy")
        else:
            shutil.copytree(old_images_dir, output_dir, dirs_exist_ok=True)
        
        # Regenerate labels using the new logic
        labels = []
        for i in range(window_size - 1, len(df)):
            window_df = df.iloc[i - (window_size - 1):i + 1]
            first_candle = window_df.iloc[0]
            last_candle = window_df.iloc[-1]
            label = "UP" if last_candle["close"] > first_candle["open"] else "DOWN"
            labels.append(label)
        
        labels_df = pd.DataFrame({"image_path": [f"candle_{i}.png" for i in range(window_size - 1, len(df))], "label": labels})
        labels_df.to_csv(labels_file, index=False)
        print(f"Regenerated {len(labels_df)} labels to {labels_file}")
        return labels_file
    else:
        # Fallback: Generate images if they don't exist (shouldn't happen in this case)
        print(f"Images not found at {old_images_dir}, generating new images")
        labels = []
        start_time = time.time()
        for i in range(window_size - 1, len(df)):
            window_df = df.iloc[i - (window_size - 1):i + 1]
            first_candle = window_df.iloc[0]
            last_candle = window_df.iloc[-1]
            label = "UP" if last_candle["close"] > first_candle["open"] else "DOWN"
            labels.append(label)
            
            plt.figure(figsize=(2, 2))
            mpf.plot(window_df, type="candle", style="binance", axisoff=True, title="", ylabel="", xlabel="", volume=False)
            plt.tight_layout(pad=0)
            image_path = os.path.join(output_dir, f"candle_{i}.png")
            plt.savefig(image_path, bbox_inches="tight", pad_inches=0, dpi=32)
            plt.close('all')
            
            if i % 1000 == 0:
                elapsed = time.time() - start_time
                images_generated = i - (window_size - 1) + 1
                speed = images_generated / elapsed if elapsed > 0 else 0
                print(f"Generated image {i}/{len(df)} for {month_str} 1m {period_name} w{window_size} ({speed:.2f} images/sec)")
        
        labels_df = pd.DataFrame({"image_path": [f"candle_{i}.png" for i in range(window_size - 1, len(df))], "label": labels})
        labels_df.to_csv(labels_file, index=False)
        print(f"Saved {len(labels_df)} labels to {labels_file}")
        return labels_file

# Load and preprocess images
def load_images(labels_file, images_dir):
    labels_df = pd.read_csv(labels_file)
    X = np.array([np.array(Image.open(os.path.join(images_dir, row["image_path"])).convert("RGB").resize((64, 64))) / 255.0 for _, row in labels_df.iterrows()])
    y = np.array([1 if label == "UP" else 0 for label in labels_df["label"]])
    return X, y

# Train CNN model
def train_model(X, y, period_name, month_str, window_size, coin_dir):
    model_path = os.path.join(coin_dir, "models", f"model_{month_str}_1m_{period_name}_w{window_size}.h5")
    if os.path.exists(model_path):
        print(f"Model already exists at {model_path}, loading instead of training")
        return tf.keras.models.load_model(model_path), None
    
    model = models.Sequential([
        layers.Conv2D(32, (3, 3), activation="relu", input_shape=(64, 64, 3)),
        layers.MaxPooling2D((2, 2)),
        layers.Dropout(0.25),
        layers.Conv2D(64, (3, 3), activation="relu"),
        layers.MaxPooling2D((2, 2)),
        layers.Dropout(0.25),
        layers.Conv2D(128, (3, 3), activation="relu"),
        layers.Flatten(),
        layers.Dense(128, activation="relu"),
        layers.Dropout(0.5),
        layers.Dense(1, activation="sigmoid")
    ])
    
    model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
    class_weights = compute_class_weight('balanced', classes=np.unique(y), y=y)
    history = model.fit(X, y, epochs=10, batch_size=32, class_weight=dict(enumerate(class_weights)))
    
    model.save(model_path)
    print(f"Model saved to {model_path}")
    return model, history

# Evaluate and save results
def evaluate_and_save(model, X, y, period_name, month_str, window_size, coin_dir, dataset_type="train", exp_suffix=""):
    results_file = os.path.join(coin_dir, "results", f"results_{dataset_type}_{month_str}_1m_{period_name}_w{window_size}{exp_suffix}.txt")
    if os.path.exists(results_file) and exp_suffix != "_exp2":
        print(f"Results already exist at {results_file}, skipping evaluation")
        return None
    
    y_pred_prob = model.predict(X, verbose=0)
    y_pred = (y_pred_prob > 0.5).astype(int).flatten()
    
    metrics = {
        "accuracy": accuracy_score(y, y_pred),
        "f1": f1_score(y, y_pred),
        "recall": recall_score(y, y_pred),
        "auroc": roc_auc_score(y, y_pred_prob),
        "auprc": auc(*precision_recall_curve(y, y_pred_prob)[1::-1])
    }
    
    with open(results_file, "w") as f:
        f.write(f"{dataset_type.capitalize()} Metrics for {month_str} 1m {period_name} w{window_size} {exp_suffix}:\n")
        for k, v in metrics.items():
            f.write(f"{k.capitalize()}: {v:.4f}\n")
    print(f"Results saved to {results_file}")
    return metrics

# Check if all experiments for a window size are complete
def is_window_size_complete(symbol, train_month, test_months, window_size):
    coin_dir = os.path.join(BASE_DIR, symbol)
    train_year, train_month_num = train_month
    train_month_str = f"{train_year}-{train_month_num:02d}"
    
    # Check Experiment I
    for days in TIME_LENGTHS:
        period_name = f"{days}days"
        train_result = os.path.join(coin_dir, "results", f"results_train_{train_month_str}_1m_{period_name}_w{window_size}.txt")
        if not os.path.exists(train_result):
            return False
        for test_year, test_month_num in test_months:
            test_month_str = f"{test_year}-{test_month_num:02d}"
            test_result = os.path.join(coin_dir, "results", f"results_test_{test_month_str}_1m_{period_name}_w{window_size}.txt")
            if not os.path.exists(test_result):
                return False
    
    # Check Experiment II
    period_name = "1week"
    train_result = os.path.join(coin_dir, "results", f"results_train_{train_month_str}_1m_{period_name}_w{window_size}_exp2.txt")
    if not os.path.exists(train_result):
        return False
    for test_year, test_month_num in test_months:
        test_month_str = f"{test_year}-{test_month_num:02d}"
        for days in [14, 21, 28]:
            period_name = f"{days}days"
            test_result = os.path.join(coin_dir, "results", f"results_test_{test_month_str}_1m_{period_name}_w{window_size}_exp2.txt")
            if not os.path.exists(test_result):
                return False
    
    return True

# Main experiment runner for a single coin and window size
def run_experiments_for_coin(symbol, train_month, test_months, window_size):
    if is_window_size_complete(symbol, train_month, test_months, window_size):
        print(f"All experiments for {symbol} with window size {window_size} are complete, skipping")
        return
    
    coin_dir = os.path.join(BASE_DIR, symbol)
    RAW_DATA_DIR = os.path.join(coin_dir, "raw_data")
    IMAGES_DIR = os.path.join(coin_dir, "images")
    MODELS_DIR = os.path.join(coin_dir, "models")
    RESULTS_DIR = os.path.join(coin_dir, "results")
    OLD_RAW_DATA_DIR = os.path.join(OLD_BASE_DIR, symbol, "raw_data")
    
    os.makedirs(RAW_DATA_DIR, exist_ok=True)
    os.makedirs(IMAGES_DIR, exist_ok=True)
    os.makedirs(MODELS_DIR, exist_ok=True)
    os.makedirs(RESULTS_DIR, exist_ok=True)
    
    train_year, train_month_num = train_month
    
    # Experiment I: Train and test on matching timelengths
    for days in TIME_LENGTHS:
        period_name = f"{days}days"
        train_start = datetime(train_year, train_month_num, 1)
        train_end = train_start + timedelta(days=days - 1, hours=23, minutes=59)
        train_month_str = f"{train_year}-{train_month_num:02d}"
        
        raw_file = os.path.join(RAW_DATA_DIR, f"raw_{train_month_str}_1m_{period_name}.csv")
        old_raw_file = os.path.join(OLD_RAW_DATA_DIR, f"raw_{train_month_str}_1m_{period_name}.csv")
        if os.path.exists(old_raw_file):
            print(f"Raw data exists at {old_raw_file}, copying to {raw_file}")
            shutil.copy(old_raw_file, raw_file)
            df = pd.read_csv(raw_file, index_col="timestamp", parse_dates=["timestamp"])
            df.index = pd.to_datetime(df.index)
        else:
            print(f"Raw data not found at {old_raw_file}, fetching new data")
            df = fetch_coin_data(symbol, train_start, train_end)
            df.set_index("timestamp", inplace=True)
            df.to_csv(raw_file)
            print(f"Raw data saved to {raw_file}")
        
        # Attach symbol to df for use in generate_images
        df.name = symbol
        images_subdir = os.path.join(IMAGES_DIR, f"{train_month_str}_1m_{period_name}_w{window_size}")
        labels_file = generate_images(df, window_size, images_subdir, period_name, train_month_str)
        X, y = load_images(labels_file, images_subdir)
        model, history = train_model(X, y, period_name, train_month_str, window_size, coin_dir)
        evaluate_and_save(model, X, y, period_name, train_month_str, window_size, coin_dir, "train")
        
        tf.keras.backend.clear_session()
        gc.collect()
        
        for test_year, test_month_num in test_months:
            test_start = datetime(test_year, test_month_num, 1)
            test_end = test_start + timedelta(days=days - 1, hours=23, minutes=59)
            test_month_str = f"{test_year}-{test_month_num:02d}"
            
            raw_file = os.path.join(RAW_DATA_DIR, f"raw_{test_month_str}_1m_{period_name}.csv")
            old_raw_file = os.path.join(OLD_RAW_DATA_DIR, f"raw_{test_month_str}_1m_{period_name}.csv")
            if os.path.exists(old_raw_file):
                print(f"Raw data exists at {old_raw_file}, copying to {raw_file}")
                shutil.copy(old_raw_file, raw_file)
                df = pd.read_csv(raw_file, index_col="timestamp", parse_dates=["timestamp"])
                df.index = pd.to_datetime(df.index)
            else:
                print(f"Raw data not found at {old_raw_file}, fetching new data")
                df = fetch_coin_data(symbol, test_start, test_end)
                df.set_index("timestamp", inplace=True)
                df.to_csv(raw_file)
                print(f"Raw data saved to {raw_file}")
            
            df.name = symbol
            images_subdir = os.path.join(IMAGES_DIR, f"{test_month_str}_1m_{period_name}_w{window_size}")
            labels_file = generate_images(df, window_size, images_subdir, period_name, test_month_str)
            X, y = load_images(labels_file, images_subdir)
            evaluate_and_save(model, X, y, period_name, test_month_str, window_size, coin_dir, "test")
        
        tf.keras.backend.clear_session()
        gc.collect()
    
    # Experiment II: Train on 1 week, test on 2-3-4 weeks
    exp2_test_lengths = [14, 21, 28]
    train_start = datetime(train_year, train_month_num, 1)
    train_end = train_start + timedelta(days=6, hours=23, minutes=59)
    train_month_str = f"{train_year}-{train_month_num:02d}"
    period_name = "1week"
    
    raw_file = os.path.join(RAW_DATA_DIR, f"raw_{train_month_str}_1m_{period_name}.csv")
    old_raw_file = os.path.join(OLD_RAW_DATA_DIR, f"raw_{train_month_str}_1m_{period_name}.csv")
    if os.path.exists(old_raw_file):
        print(f"Raw data exists at {old_raw_file}, copying to {raw_file}")
        shutil.copy(old_raw_file, raw_file)
        df = pd.read_csv(raw_file, index_col="timestamp", parse_dates=["timestamp"])
        df.index = pd.to_datetime(df.index)
    else:
        print(f"Raw data not found at {old_raw_file}, fetching new data")
        df = fetch_coin_data(symbol, train_start, end_time=train_end)
        df.set_index("timestamp", inplace=True)
        df.to_csv(raw_file)
        print(f"Raw data saved to {raw_file}")
    
    df.name = symbol
    images_subdir = os.path.join(IMAGES_DIR, f"{train_month_str}_1m_{period_name}_w{window_size}")
    labels_file = generate_images(df, window_size, images_subdir, period_name, train_month_str)
    X, y = load_images(labels_file, images_subdir)
    model, history = train_model(X, y, period_name, train_month_str, window_size, coin_dir)
    evaluate_and_save(model, X, y, period_name, train_month_str, window_size, coin_dir, "train", "_exp2")
    
    tf.keras.backend.clear_session()
    gc.collect()
    
    for test_year, test_month_num in test_months:
        test_month_str = f"{test_year}-{test_month_num:02d}"
        for days in exp2_test_lengths:
            period_name = f"{days}days"
            test_start = datetime(test_year, test_month_num, 1)
            test_end = test_start + timedelta(days=days - 1, hours=23, minutes=59)
            
            raw_file = os.path.join(RAW_DATA_DIR, f"raw_{test_month_str}_1m_{period_name}.csv")
            old_raw_file = os.path.join(OLD_RAW_DATA_DIR, f"raw_{test_month_str}_1m_{period_name}.csv")
            if os.path.exists(old_raw_file):
                print(f"Raw data exists at {old_raw_file}, copying to {raw_file}")
                shutil.copy(old_raw_file, raw_file)
                df = pd.read_csv(raw_file, index_col="timestamp", parse_dates=["timestamp"])
                df.index = pd.to_datetime(df.index)
            else:
                print(f"Raw data not found at {old_raw_file}, fetching new data")
                df = fetch_coin_data(symbol, test_start, test_end)
                df.set_index("timestamp", inplace=True)
                df.to_csv(raw_file)
                print(f"Raw data saved to {raw_file}")
            
            df.name = symbol
            images_subdir = os.path.join(IMAGES_DIR, f"{test_month_str}_1m_{period_name}_w{window_size}")
            labels_file = generate_images(df, window_size, images_subdir, period_name, test_month_str)
            X, y = load_images(labels_file, images_subdir)
            evaluate_and_save(model, X, y, period_name, test_month_str, window_size, coin_dir, "test", "_exp2")
        
        tf.keras.backend.clear_session()
        gc.collect()

# Run experiments for all coins and window sizes
def run_all_experiments():
    os.makedirs(BASE_DIR, exist_ok=True)
    for symbol, config in COINS.items():
        for window_size in WINDOW_SIZES:
            print(f"Running experiments for {symbol} with window size {window_size}")
            run_experiments_for_coin(symbol, config["train_month"], config["test_months"], window_size)
            print(f"Completed experiments for {symbol} with window size {window_size}")
            tf.keras.backend.clear_session()
            gc.collect()

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Crypto Minute-Based Image Classification Research with Full-Window Labeling")
    args = parser.parse_args()
    run_all_experiments()