Update app.py

app.py

@@ -1,26 +1,97 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import gradio as gr
+# main.py
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSequenceClassification
+from alpaca_trade_api.rest import REST, TimeFrame
+import yfinance as yf
+import pandas as pd
+import pandas_ta as ta
 import torch
 
+# Configuration
+ALPACA_API_KEY = 'AKRII2NASCQ4UYYNMUBJ'
+ALPACA_SECRET_KEY = 'amTJvuxDsojAZFVU3wEt6IZPBN9L5VvhfbwR28fj'
+ALPACA_BASE_URL = 'https://paper-api.alpaca.markets'
+MODEL_NAME = "databricks/dolly-v2-3b"
+FINBERT_MODEL_NAME = "yiyanghkust/finbert-tone"
+SYMBOL = '^KLSE'  # KLCI index symbol
+START_DATE = '2020-01-01'
+END_DATE = '2023-12-31'
+
+# Initialize Alpaca API
+api = REST(ALPACA_API_KEY, ALPACA_SECRET_KEY, ALPACA_BASE_URL)
+
+# Load Models
+dolly_tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+dolly_model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+finbert_tokenizer = AutoTokenizer.from_pretrained(FINBERT_MODEL_NAME)
+finbert_model = AutoModelForSequenceClassification.from_pretrained(FINBERT_MODEL_NAME)
+
+# Step 1: Fetch KLCI Data
+def fetch_stock_data(symbol, start_date, end_date):
+    print("Fetching stock data...")
+    data = yf.download(symbol, start=start_date, end=end_date)
+    data = add_technical_indicators(data)
+    return data
+
+# Step 2: Add Technical Indicators
+def add_technical_indicators(data):
+    print("Adding technical indicators...")
+    data['RSI'] = ta.rsi(data['Close'], length=14)
+    data['MACD'] = ta.macd(data['Close'], fast=12, slow=26)['MACD_12_26_9']
+    bbands = ta.bbands(data['Close'])
+    data['BB_upper'], data['BB_middle'], data['BB_lower'] = bbands['BBU_20_2.0'], bbands['BBM_20_2.0'], bbands['BBL_20_2.0']
+    return data
+
+# Step 3: Analyze Sentiment using FinBERT
+def analyze_sentiment(text):
+    print("Analyzing sentiment...")
+    inputs = finbert_tokenizer(text, return_tensors="pt")
+    outputs = finbert_model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    sentiment = torch.argmax(predictions).item()
+    return ["Negative", "Neutral", "Positive"][sentiment]
 
-# Load the Dolly model and tokenizer
-model_name = "databricks/dolly-v2-3b"  
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+# Step 4: Generate Dolly Prediction
+def generate_prediction(prompt):
+    print("Generating prediction...")
+    inputs = dolly_tokenizer(prompt, return_tensors="pt")
+    outputs = dolly_model.generate(**inputs, max_length=100)
+    return dolly_tokenizer.decode(outputs[0], skip_special_tokens=True)
 
+# Step 5: Execute Trade with Alpaca
+def execute_trade(signal, symbol='AAPL', qty=1):
+    print(f"Executing trade signal: {signal}")
+    if signal == "buy":
+        api.submit_order(symbol=symbol, qty=qty, side='buy', type='market', time_in_force='gtc')
+    elif signal == "sell":
+        api.submit_order(symbol=symbol, qty=qty, side='sell', type='market', time_in_force='gtc')
 
-# Function to generate model response
-def generate_response(prompt):
-    try:
-        inputs = tokenizer(prompt, return_tensors="pt")
-        outputs = model.generate(**inputs, max_length=200, num_return_sequences=1)
-        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        return response
-    except Exception as e:
-        return f"An error occurred: {e}"
+# Step 6: Main Function to Run Pipeline
+def main():
+    # 1. Fetch and Prepare Data
+    klci_data = fetch_stock_data(SYMBOL, START_DATE, END_DATE)
+    
+    # 2. Run FinBERT Sentiment Analysis
+    sample_text = "The market sentiment is bullish for KLCI."  # Example text
+    sentiment = analyze_sentiment(sample_text)
+    print(f"Sentiment: {sentiment}")
+    
+    # 3. Generate Dolly Prediction
+    prompt = f"The market trend for KLCI with sentiment {sentiment} and indicators {klci_data[['RSI', 'MACD', 'BB_upper', 'BB_middle', 'BB_lower']].iloc[-1].to_dict()}"
+    prediction = generate_prediction(prompt)
+    print(f"Dolly Prediction: {prediction}")
+    
+    # 4. Decide Buy/Sell based on Prediction and Execute Trade
+    if "buy" in prediction.lower():
+        execute_trade("buy", symbol=SYMBOL, qty=1)
+    elif "sell" in prediction.lower():
+        execute_trade("sell", symbol=SYMBOL, qty=1)
+    else:
+        print("No clear trade signal from prediction.")
 
+# Run the main function
+if __name__ == "__main__":
+    main()
 
-# Set up Gradio interface
-gr.Interface(fn=generate_response, inputs="text", outputs="text").launch()