AI Deep Learning Stock Program Development

I'm seeking an experienced developer for an AI deep learning stock program. The ideal candidate will have relevant experience in stock program development and deep learning. Key Requirements: - Proven track record in AI and deep learning applications, particularly in stock market - Excellent programming skills - Ability to integrate various data sources Ideal Skills: - Proficiency in Python with TensorFlow, R with Keras, or C++ with PyTorch - Strong understanding of stock market dynamics - Experience with real-time market data, historical stock data, and news and social media feeds Please note, only developers with relevant experience in AI deep learning stock program development are allowed to participate. Explaining how to create an **AI-based stock prediction program** to a developer who is a beginner in stocks requires a good understanding of the technical aspects, while at the same time, it is important to proceed in line with **practical goals**. Below is a step-by-step detailed explanation that even a beginner developer can understand. ### 1. **Define project goals** The goal is **to have the AI ​​analyze stock data and use various technical indicators to predict high-yield buy/sell timings**. The developer should use the AI ​​to **learn on its own** and find **the optimal trading timing**. ### 2. **Prepare necessary data** Since the AI ​​must learn based on stock data, it is important to **prepare** stock data first. - **Stock price data**: Prepare the **opening price**, **closing price**, **high price**, **low price**, and **trading volume** data of the stock. - **Technical indicators**: Create various **technical indicators** for the stock data. For example, **Moving Average (MA)**, **Relative Strength Index (RSI)**, **MACD**, **Bollinger Bands**, etc. - **API Utilization**: To obtain stock data, you can use **Yahoo Finance API**, **Alpha Vantage API**, **Quandl**, etc. to get real-time data, or save data locally as a **CSV file**. ```python import yfinance as yf # Example: 5 years of Apple stock data data = [login to view URL]('AAPL', start='2015-01-01', end='2020-01-01') print([login to view URL]()) ``` ### 3. **Calculating technical indicators** AI basically learns through **technical indicators**. For example, it can calculate indicators such as **Moving Average**, **RSI**, **MACD**, etc. These indicators are important in generating **buy/sell signals**. ```python import talib as ta # Moving average (50 days, 200 days) data['SMA_50'] = [login to view URL](data['Close'], timeperiod=50) data['SMA_200'] = [login to view URL](data['Close'], timeperiod=200) # Relative Strength Index (RSI) data['RSI'] = [login to view URL](data['Close'], timeperiod=14) # MACD data['MACD'], data['MACD_signal'], data['MACD_hist'] = [login to view URL](data['Close'], fastperiod=12, slowperiod=26, signalperiod=9) ``` ### 4. **AI model training** The AI ​​model learns stock data and technical indicators. **Predict buy/sell timing**. You can analyze stock data using **machine learning algorithms**. **Basic algorithm**: For example, you can use models such as **Random Forest** or **XGBoost**. If you want to use **deep learning**, you can also use **LSTM** (Long Short-Term Memory) networks. - **Data preparation**: Input stock data and set **buy/sell signals** as target values. - Divide into **training data and test data** to train and evaluate the model. ```python from [login to view URL] import RandomForestClassifier # Prepare features and target variables (example: Generating buy/sell signals) data['Signal'] = 0 data['Signal'][data['SMA_50'] > data['SMA_200']] = 1 # Buy signal data['Signal'][data['SMA_50'] < data['SMA_200']] = -1 # Sell signal # Features (indicators) features = ['SMA_50', 'SMA_200', 'RSI', 'MACD'] # Separate training and test data train_data = data[:-100] test_data = data[-100:] X_train = train_data[features] y_train = train_data['Signal'] X_test = test_data[features] y_test = test_data['Signal'] # Model Training model = RandomForestClassifier(n_estimators=100) [login to view URL](X_train, y_train) # Prediction y_pred = [login to view URL](X_test) ``` ### 5. **AI Model Evaluation** After training the model, you need to **evaluate** whether the predictions are good. At this time, you can use evaluation metrics such as **Accuracy**, **F1-score**, and **Confusion Matrix**. ```python from [login to view URL] import accuracy_score, confusion_matrix # Accuracy Evaluation print("Accuracy:", accuracy_score(y_test, y_pred)) # Confusion Matrix print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred)) ``` ### 6. **Backtesting** **Backtesting** is the process of **using past data** to check if the algorithm actually works well. For example, check how well the algorithm traded based on one year of stock data. ```python portfolio = 10000 # Initial capital shares = 0 for index, row in [login to view URL](): if row['Signal'] == 1 and portfolio >= row['Close']: # Buy signal shares = portfolio // row['Close'] portfolio -= shares * row['Close'] elif row['Signal'] == -1 and shares > 0: # Sell signal portfolio += shares * row['Close'] shares = 0 final_value = portfolio + shares * data['Close'].iloc[-1] print("Final asset:", final_value) ``` ### 7. **Use real-time data** After backtesting, **use real-time data** so that the AI ​​can continuously generate buy/sell signals. **You need to receive stock data in real time through the API** and enable the algorithm to trade in real time. - **Use stock exchange API**: For example, you can receive data in real time through APIs such as **Binance**, **Coinbase**, etc. ### 8. **Program optimization and improvement** You need to **continuously improve** the model while actually operating it. For example, you need to add new technical indicators or make the model more efficient to make better predictions. ### 9. **Explanation to developers** - **Goal**: Develop a program that predicts **buy/sell timing** based on stock data using AI. - **Data preparation**: Download stock data and calculate technical indicators. - **AI model training**: Use machine learning algorithms to predict buy/sell. - **Backtest**: Use historical data to verify the algorithm. - **Real-time application**: Use real-time data to execute actual transactions. ### 10. **Basic development flow** 1. **Secure stock data**: Import stock data via API or CSV file. 2. **Calculate technical indicators**: Calculate moving averages, RSI, MACD, etc. 3. **AI model learning**: Learn machine learning models to predict buy/sell. 4. **Backtest**: Evaluate models using past data. 5. **Real-time trading**: Build automated trading systems using real-time stock data. In this way, even stock beginners can fully understand and develop **AI model development** based on **technical analysis**. The important thing is to **create an algorithm** that can automatically generate stock buy/sell signals.