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import pandas as pd

import matplotlib.pyplot as plt

import sys

sys.path.append("../")

from model import Kronos, KronosTokenizer, KronosPredictor

def plot_prediction(kline_df, pred_df):

pred_df.index = kline_df.index[-pred_df.shape[0]:]

sr_close = kline_df['close']

sr_pred_close = pred_df['close']

sr_close.name = 'Ground Truth'

sr_pred_close.name = "Prediction"

close_df = pd.concat([sr_close, sr_pred_close], axis=1)

fig, ax = plt.subplots(1, 1, figsize=(8, 4))

ax.plot(close_df['Ground Truth'], label='Ground Truth', color='blue', linewidth=1.5)

ax.plot(close_df['Prediction'], label='Prediction', color='red', linewidth=1.5)

ax.set_ylabel('Close Price', fontsize=14)

ax.legend(loc='lower left', fontsize=12)

ax.grid(True)

plt.tight_layout()

plt.show()

# 1. Load Model and Tokenizer

tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base")

model = Kronos.from_pretrained("NeoQuasar/Kronos-small")

# 2. Instantiate Predictor

predictor = KronosPredictor(model, tokenizer, device="cuda:0", max_context=512)

# 3. Prepare Data

df = pd.read_csv("./data/XSHG_5min_600977.csv")

df['timestamps'] = pd.to_datetime(df['timestamps'])

lookback = 400

pred_len = 120

x_df = df.loc[:lookback-1, ['open', 'high', 'low', 'close']]

x_timestamp = df.loc[:lookback-1, 'timestamps']

y_timestamp = df.loc[lookback:lookback+pred_len-1, 'timestamps']

# 4. Make Prediction

pred_df = predictor.predict(

df=x_df,

x_timestamp=x_timestamp,

y_timestamp=y_timestamp,

pred_len=pred_len,

T=1.0,

top_p=0.9,

sample_count=1,

verbose=True

)

# 5. Visualize Results

print("Forecasted Data Head:")

print(pred_df.head())

# Combine historical and forecasted data for plotting

kline_df = df.loc[:lookback+pred_len-1]

# visualize

plot_prediction(kline_df, pred_df)