#ArbitrageTradingStrategy discrepancies of the same asset across different markets or forms. The core idea is to buy low in one market and sell high in another simultaneously or nearly simultaneously to lock in a risk-free or low-risk profit.
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๐ Key Types of Arbitrage Strategies
1. Spatial Arbitrage (Cross-Exchange)
Buy an asset on one exchange where the price is lower
Sell on another where the price is higher
Common in crypto (e.g., BTC on Binance vs. Coinbase)
2. Triangular Arbitrage
Happens within the same exchange
Exploits pricing inefficiencies between three currencies
E.g., USD โ EUR โ GBP โ USD
3. Statistical Arbitrage
Uses quantitative models and algorithms to detect temporary mispricings
Pairs trading is an example: Long one stock, short a correlated one
4. Merger Arbitrage
Involves buying stock of a company being acquired and shorting the acquirer
Profit from the spread between market price and acquisition price
5. Convertible Arbitrage
Involves long convertible bonds and short underlying stocks
Profits from volatility and pricing mismatches
6. Crypto Arbitrage (High-Frequency)
Automated bots execute trades at sub-second speeds
Volatility and fragmented markets create frequent inefficiencies
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โ๏ธ Basic Workflow for a Cross-Exchange Arbitrage Bot
1. Monitor prices across exchanges (e.g., Binance and Coinbase)
2. Detect arbitrage opportunity: Price_A < Price_B - Fees
3. Execute:
- Buy asset on Exchange A
- Sell asset on Exchange B
4. Transfer funds if needed
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๐ Key Risks
Latency โ Delays can eliminate the price difference
Fees โ Trading and withdrawal fees can wipe out profits
Slippage โ Order execution price may differ from expected
Regulatory โ Some arbitrage strategies may be restricted or banned
Capital inefficiency โ Ties up funds across multiple platforms
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โ Tools for Arbitrage
APIs of exchanges (e.g., Binance API, Kraken API)
Python Libraries: ccxt, pandas, numpy
Data Feeds: Real-time pricing data (low latency is critical)
Bots: Automation to reduce human error and improve speed
