An innovative DeFi project, built using Starknet. It is an auto trading system on starknet.
Hereās an expanded version of your project description, incorporating more details about trading strategies, risk management, and potential optimizations:
Project Description: Trading Strategies for STRK Native Coin
In this project, I have implemented and evaluated automated trading strategies for the STRK/USDT trading pair, using historical 15-minute interval price data. The objective is to develop a robust data-driven trading model that can identify profitable trade opportunities, optimize execution, and minimize risk in a volatile crypto market.
The dataset consists of key market parameters such as open, high, low, close prices, and trading volume. These features were analyzed using technical indicators, trend analysis, and predictive modeling techniques to build a strategy that can generate effective buy/sell signals.
Trading Strategies and Techniques To construct an efficient trading system, I employed multiple trading strategies, each designed to capture different aspects of market behavior:
Technical Indicator-Based Trading This approach relies on widely used price and volume-based indicators to identify trade opportunities: Moving Averages (SMA & EMA): Detect short-term and long-term trend reversals. Relative Strength Index (RSI): Identifies overbought (>70) and oversold (<30) conditions. Bollinger Bands: Measures price volatility and helps identify breakout opportunities. MACD (Moving Average Convergence Divergence): Tracks momentum shifts for trend-following trades. Volume Weighted Average Price (VWAP): Ensures trades align with market liquidity trends.
Momentum and Trend-Following Strategies To capitalize on market trends, I utilized: Moving Average Crossover Strategy: A short-term EMA (e.g., 9-period) crossing above a long-term EMA (e.g., 21-period) generates a buy signal, while a downward crossover triggers a sell.
Breakout Trading Breakout trading takes advantage of sharp price movements after key levels are broken: Support & Resistance Breakout: Trades are executed when price breaks above resistance (buy) or below support (sell). -Volume-Confirmed Breakouts: If breakouts occur with high volume, they indicate stronger price movements.
Machine Learning-Based Strategy For a more sophisticated approach, I integrated predictive models: -Regression Models (Random Forest, XGBoost): Forecast short-term price movements.
Backtesting and Strategy Evaluation Before deploying strategies in real markets, backtesting was performed using historical STRK/USDT data:
Live Trading and Automation To integrate these strategies into a real-world automated trading bot, I implemented: Exchange API Integration: Fetching real-time STRK/USDT data using Binance API / OKX API.
Further enhancements could include portfolio diversification, hedging techniques, and alternative data sources (e.g., social media sentiment analysis) to refine predictions and trading performance.
This version gives a detailed and structured overview of your work. Let me know if you need further refinements or additions! š
How It's Made: The Technology Behind the Project
This project is built using a combination of AI agents, blockchain smart contracts (Cairo on Starknet), and a Next.js-based frontend, creating a seamless integration between automated trading intelligence, decentralized execution, and user-friendly interfaces.
At the core of the trading strategy lies an AI-powered agent designed to analyze historical market data, predict trends, and execute trades autonomously. The AI model is trained using:
The AI agent continuously ingests live STRK/USDT market data, processes it through predictive models, and generates buy/sell signals based on learned patterns.
Tech Stack for AI Models:
To ensure secure and transparent trade execution, I deployed smart contracts on Starknet, written in Cairo. These contracts handle:
By leveraging Starknetās high-speed, low-cost transactions, the system provides scalable and efficient execution without relying on centralized exchanges.
Tech Stack for Smart Contracts:
The Next.js frontend provides an intuitive dashboard where users can:
Tech Stack for Frontend:
A few notable and hacky aspects of the project include:
Final Thoughts
By combining AI agents, decentralized smart contracts, and a real-time trading interface, this project successfully integrates machine learning-driven trading strategies with on-chain execution, offering a scalable, transparent, and efficient solution for automated trading.
Let me know if you need any further refinements.