| import gradio as gr | |
| import ccxt | |
| import pandas as pd | |
| from ta import add_all_ta_features | |
| import time | |
| import torch | |
| import json | |
| import numpy as np | |
| from sklearn.preprocessing import StandardScaler | |
| from huggingface_hub import hf_hub_download | |
| import matplotlib.pyplot as plt | |
| import io | |
| from PIL import Image | |
| import datetime | |
| predictions_history = [] | |
| timestamps_history = [] | |
| def gradio_interface(): | |
| def process_data(): | |
| try: | |
| df = fetch_data() | |
| df = calculate_all_indicators(df) | |
| row = df.iloc[-2] | |
| last_row_filtered = row[selected_columns].fillna(0).values.tolist() | |
| prediction = predict(last_row_filtered) | |
| result = (f"Close: {prediction['Predicted Target']:.2f}, " | |
| f"Open: {prediction['Predicted Target Open']:.2f}, " | |
| f"High: {prediction['Predicted Target High']:.2f}, " | |
| f"Low: {prediction['Predicted Target Low']:.2f}") | |
| plot_img = plot_predictions(prediction) | |
| return result, plot_img | |
| except Exception as e: | |
| return f"Error: {str(e)}", None | |
| def predict_interface(): | |
| result, plot_img = process_data() | |
| return result, plot_img | |
| def plot_predictions(predictions): | |
| current_time = datetime.datetime.now() | |
| current_prediction = [predictions['Predicted Target'], predictions['Predicted Target Open'], | |
| predictions['Predicted Target High'], predictions['Predicted Target Low']] | |
| if not predictions_history or current_prediction != predictions_history[-1]: | |
| timestamps_history.append(current_time + datetime.timedelta(minutes=15)) | |
| predictions_history.append(current_prediction) | |
| if len(timestamps_history) > 10: | |
| timestamps_history.pop(0) | |
| predictions_history.pop(0) | |
| plt.figure(figsize=(10, 6)) | |
| for idx, timestamp in enumerate(timestamps_history): | |
| prediction = predictions_history[idx] | |
| plt.scatter([timestamp] * 4, prediction, color=['green', 'red', 'blue', 'purple'], s=50) | |
| plt.title('Predicted Candles for BTC/USDT Futures', fontsize=14) | |
| plt.xlabel('Time', fontsize=12) | |
| plt.ylabel('Price', fontsize=12) | |
| plt.xticks(rotation=45) | |
| plt.grid(True) | |
| plt.tight_layout() | |
| buf = io.BytesIO() | |
| plt.savefig(buf, format="png") | |
| buf.seek(0) | |
| img = Image.open(buf) | |
| return img | |
| with gr.Blocks() as app: | |
| gr.Markdown("## Prediction Interface") | |
| gr.Markdown("This interface will give you 15 mins prediction of BTC/USD futures value") | |
| output = gr.Textbox(label="Prediction Result") | |
| plot_output = gr.Image(label="Prediction Plot", type="pil") | |
| button = gr.Button("Get Prediction") | |
| button.click(fn=predict_interface, inputs=[], outputs=[output, plot_output]) | |
| app.launch(show_api=False, auth=None) | |
| if __name__ == "__main__": | |
| def fetch_data(symbol="BTC/USDT", timeframe="1m", limit=500): | |
| exchange = ccxt.binanceus({ | |
| "rateLimit": 1200, | |
| "enableRateLimit": True, | |
| }) | |
| ohlcv = exchange.fetch_ohlcv(symbol, timeframe, limit=limit) | |
| df = pd.DataFrame(ohlcv, columns=["timestamp", "Open", "High", "Low", "Close", "Volume"]) | |
| df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms") | |
| return df | |
| def calculate_all_indicators(data): | |
| data = add_all_ta_features( | |
| df=data, | |
| open="Open", | |
| high="High", | |
| low="Low", | |
| close="Close", | |
| volume="Volume", | |
| fillna=False | |
| ) | |
| return data | |
| model_path = hf_hub_download(repo_id="alexandrlukashov/gru-model-time-series", filename="gru_model.pth") | |
| config_path = hf_hub_download(repo_id="alexandrlukashov/gru-model-time-series", filename="gru_config.json") | |
| with open(config_path, "r") as f: | |
| config = json.load(f) | |
| class GRUModel(torch.nn.Module): | |
| def __init__(self, input_size, hidden_size, num_layers, output_size): | |
| super(GRUModel, self).__init__() | |
| self.hidden_size = hidden_size | |
| self.num_layers = num_layers | |
| self.gru = torch.nn.GRU(input_size, hidden_size, num_layers, batch_first=True) | |
| self.fc = torch.nn.Linear(hidden_size, output_size) | |
| def forward(self, x): | |
| h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device) | |
| out, _ = self.gru(x, h0) | |
| out = self.fc(out[:, -1, :]) | |
| return out | |
| model = GRUModel( | |
| input_size=config["input_size"], | |
| hidden_size=config["hidden_size"], | |
| num_layers=config["num_layers"], | |
| output_size=4 | |
| ) | |
| model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) | |
| model.eval() | |
| scaler_path = hf_hub_download(repo_id="alexandrlukashov/gru-model-time-series", filename="scaler_X.json") | |
| with open(scaler_path, "r") as f: | |
| scaler_params = json.load(f) | |
| scaler = StandardScaler() | |
| scaler.mean_ = np.array(scaler_params["mean"]) | |
| scaler.scale_ = np.array(scaler_params["scale"]) | |
| scaler.var_ = scaler.scale_**2 | |
| def preprocess_input(data): | |
| data = np.array(data).reshape(1, -1) | |
| scaled_data = scaler.transform(data) | |
| return scaled_data | |
| def inverse_scale_output(predictions): | |
| dummy_input = np.zeros((1, len(scaler.mean_))) | |
| dummy_input[:, :4] = predictions | |
| unscaled_predictions = scaler.inverse_transform(dummy_input) | |
| return unscaled_predictions[0, :4] | |
| def predict(inputs): | |
| inputs = preprocess_input(inputs) | |
| inputs_tensor = torch.tensor(inputs, dtype=torch.float32).unsqueeze(1) | |
| with torch.no_grad(): | |
| predictions = model(inputs_tensor).numpy() | |
| predictions = inverse_scale_output(predictions) | |
| return { | |
| "Predicted Target": predictions[0], | |
| "Predicted Target Open": predictions[1], | |
| "Predicted Target High": predictions[2], | |
| "Predicted Target Low": predictions[3] | |
| } | |
| selected_columns = [ | |
| 'Open', 'High', 'Low', 'Close', 'others_cr', 'trend_ema_fast', | |
| 'trend_ichimoku_conv', 'momentum_kama', 'volatility_kcc', 'volume_vwap', | |
| 'trend_sma_fast', 'trend_ichimoku_a', 'volatility_kch', 'volatility_kcl', | |
| 'volatility_dcm', 'trend_ema_slow', 'volatility_bbm', 'trend_ichimoku_base', | |
| 'trend_sma_slow', 'trend_psar_down', 'trend_psar_up', 'volatility_dch', | |
| 'volatility_bbh', 'trend_ichimoku_b', 'volatility_dcl', 'volatility_bbl', | |
| 'trend_visual_ichimoku_a', 'trend_visual_ichimoku_b' | |
| ] | |
| gradio_interface() | |