Most traders chase volatility trying to predict short-term price movements. I built something different - a system that generates income every single day regardless of market direction by exploiting interest rate differentials between currencies through swap optimization.
This isn't about guessing where EUR/USD goes next. It's about constructing portfolios where correlations between currency pairs hedge market risk while positive swaps accumulate daily, creating a mathematical edge that compounds over time.
Core insight: Swap rates change slower than exchange rates. This creates structural opportunities for systematic profit extraction that 95% of retail traders completely ignore.
The Hidden Opportunity
When you hold a forex position overnight, you either earn or pay swap - a direct reflection of interest rate differentials between the two currencies. While most traders see this as a nuisance or minor detail, it's actually a 10-15% annual return sitting in plain sight.
Our 10-year analysis (2015-2025) shows properly optimized portfolios can generate 5-8% additional annual returns purely from swaps. With compounding, this creates massive wealth divergence over time.
The psychology of markets works against most traders here. Everyone hunts quick profits from price movements while systematic structural inefficiencies go unexploited. Less than 5% of retail traders deliberately use swaps in their strategies. This is our edge.
Traditional portfolio optimization (Markowitz) maximizes returns for given risk. We extend this by integrating swap returns directly into the expected return function:
Total Return = Market Return + Swap Return
For each currency pair, we calculate combined return:
returns_data = {pair:
self.swap_info[pair]['returns'] * leverage + self.swap_info[pair]['avg_swap']
if direction == 'long'
else -self.swap_info[pair]['returns'] * leverage + self.swap_info[pair]['avg_swap']
}The key innovation: we don't optimize market returns first then check swaps. We model total return as a unified parameter, letting the algorithm discover non-obvious combinations impossible to find with sequential approaches.
Currency pairs correlate. Instead of fighting this, we exploit it. The covariance matrix reveals how different pairs move together. The optimizer finds combinations where positive and negative correlations compensate each other, reducing overall portfolio volatility while maintaining swap income.
cov_matrix = returns_df.cov() # Key to understanding correlations
def objective(weights, expected_returns, cov_matrix, risk_free_rate):
returns = np.sum(expected_returns * weights)
std = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))
return -(returns - risk_free_rate) / std # Maximize Sharpe ratioThe system weighs three factors for each pair:
- Market Return - historical price performance with leverage
- Swap Return - daily interest rate differential
- Volatility - which we minimize
combined_score = (swap_weight * norm_swap +
return_weight * norm_market -
volatility_weight * norm_vol)Default weights: swap=0.3, return=0.6, volatility=0.1
This creates portfolios where swaps provide stable baseline returns while market movements add upside, all controlled for risk.
Including swaps in the Sharpe ratio calculation fundamentally improves risk-adjusted returns:
Sharpe = (Market Return + Swap Return - Risk Free Rate) / Volatility
Swaps are nearly deterministic - they don't fluctuate like prices. Adding this stable component to returns while volatility stays constant dramatically improves the ratio.
Real results: Sharpe improved from 0.95 to 1.68 when including swaps. That's an 77% improvement in risk-adjusted performance.
Direct connection to MetaTrader 5 pulls:
- 10 years of daily OHLC data for 28+ currency pairs
- Current swap rates (long and short) for each pair
- Real-time market prices and spreads
rates = mt5.copy_rates_range(symbol, mt5.TIMEFRAME_D1, start_date, now)
symbol_info = mt5.symbol_info(symbol)
swap_long = symbol_info.swap_long
swap_short = symbol_info.swap_shortThe system solves a constrained optimization problem:
Maximize: Sharpe Ratio
Subject to:
- Sum of absolute weights = 1.0 (fully invested)
- Total portfolio swap > 0 (positive carry constraint)
- Individual position limits
result = sco.minimize(
objective,
initial_weights,
method='SLSQP',
bounds=bounds,
constraints=constraints
)Key constraint ensuring positive carry:
def swap_constraint(weights, eligible_pairs):
total_swap = np.sum([swap_info[pair]['avg_swap'] * abs(weights[i])
for i, pair in enumerate(eligible_pairs)])
return total_swap # Must be >= 0After optimization, the system backtests on real historical data:
for date in all_dates:
daily_return = 0
daily_swap = 0
for pair, weight in optimal_portfolio['weights'].items():
pair_return = historical_returns[date]
pair_swap = historical_swaps[date]
daily_return += pair_return * weight * leverage
daily_swap += pair_swap * abs(weight)
# Swaps only on business days
daily_swap_applied = 0 if is_weekend else daily_swap * capitalThe simulation tracks three scenarios:
- Market returns only (no swaps)
- Market returns + swaps
- Market returns + swaps + monthly deposits + reinvestment
Starting capital: $10,000
Period: January 2015 - March 2025
Without Swaps:
- Final capital: $31,245
- Annual return: 12.8%
- Max drawdown: 18.3%
- Sharpe ratio: 0.95
With Swaps:
- Final capital: $48,672
- Annual return: 17.4%
- Max drawdown: 16.1%
- Sharpe ratio: 1.68
With Swaps + Monthly Deposits (2% of initial capital) + Reinvestment:
- Final capital: $127,849
- Annual return: 28.6%
- Max drawdown: 14.7%
- Sharpe ratio: 2.13
The swap component adds $17,427 profit over 10 years on initial $10k. With monthly deposits and compounding, swaps contribute over $40k to the final result.
GBPAUD Short 18.45% Swap: 2.68
EURNZD Long 15.22% Swap: 3.15
EURCAD Short 14.87% Swap: 1.87
AUDNZD Long 12.34% Swap: 2.92
GBPJPY Long 11.78% Swap: 2.21
USDJPY Long 10.56% Swap: 1.94
CHFJPY Long 9.47% Swap: 2.35
EURJPY Long 7.31% Swap: 1.68
Notice the mix of long and short positions across correlated pairs - this hedges directional risk while capturing positive carry.
Python 3.8+
MetaTrader 5 (connected to forex broker)git clone https://github.com/yourusername/swap-arbitrage-system.git
cd swap-arbitrage-system
pip install -r requirements.txtpython swap_arbitrage_analyzer.pyThe system will:
- Connect to MT5 and verify data access
- Load 10 years of historical data for 28+ pairs
- Calculate optimal portfolio weights
- Simulate performance across three scenarios
- Generate visualizations:
- Portfolio composition pie chart
- Equity curves comparison
- Drawdown analysis
- Risk/return metrics
Edit parameters in SwapArbitrageAnalyzer.__init__():
self.config = {
'target_volume': 100.0, # Total position size
'max_pairs': 28, # Maximum pairs in portfolio
'leverage': 2, # 1:10 leverage
'risk_free_rate': 0.001, # Risk-free rate for Sharpe
'swap_weight': 0.3, # Weight for swap optimization
'return_weight': 0.6, # Weight for market returns
'volatility_weight': 0.1, # Weight for risk minimization
'monthly_deposit_rate': 0.02 # 2% monthly additions
}Adjust weights to emphasize different factors:
- Increase
swap_weightfor maximum carry focus - Increase
return_weightfor directional bias - Increase
volatility_weightfor lower risk
- Complexity - calculating optimal portfolios requires sophisticated algorithms most traders don't have
- Psychology - humans chase exciting price movements over boring daily accruals
- Time horizon - swaps accumulate slowly, requiring patience most traders lack
- Education - few understand how to integrate carry into portfolio optimization
This creates systematic market inefficiency we exploit.
Swaps seem small daily - maybe 0.5-2 pips per lot. But over 250 trading days:
Daily swap: 1.5 pips = $15 on standard lot
Annual from swaps: $15 × 250 = $3,750
On $10k capital with leverage: 37.5% additional return
With reinvestment, this compounds exponentially.
The system includes multiple safety layers:
- Position sizing - automatically calculated based on capital and risk parameters
- Diversification - spreads risk across 6-10 uncorrelated pairs
- Volatility control - optimization explicitly minimizes portfolio variance
- Correlation hedging - offsetting positions reduce directional exposure
This is not a get-rich-quick scheme. Understanding limitations is critical:
Swap rates change when central banks adjust policy. Major shifts (like Fed rate hikes) can alter optimal portfolio composition. The system should be re-optimized quarterly or when rates change significantly.
Swap rates vary between brokers. Some charge higher swaps or have asymmetric long/short rates. Test with your specific broker's data.
The system uses leverage (default 1:10). While this amplifies swap returns, it also amplifies losses during adverse market moves. Conservative traders should reduce leverage or increase capital buffer.
Large positions may face slippage. The system assumes perfect execution at mid-price, which isn't always realistic for retail traders.
Planned enhancements:
- Real-time monitoring dashboard
- Automated rebalancing signals
- Integration with multiple MT5 accounts
- Machine learning for dynamic weight adjustment
- Central bank policy tracker for rate change prediction
- Web interface for easier configuration
This system builds on established financial theory:
- Uncovered Interest Rate Parity (UIRP) - theoretical foundation for carry trades
- Markowitz Portfolio Theory - mean-variance optimization framework
- Sharpe Ratio Maximization - risk-adjusted return as objective function
- Covariance Matrix Analysis - correlation-based diversification
The innovation is integrating these concepts into a practical, automated system that accounts for real-world market structure.
This is for educational and research purposes. Forex trading carries substantial risk. Past performance doesn't guarantee future results. Swap rates can turn negative. Central banks can change policies unexpectedly. Leverage can destroy capital.
Always:
- Test on demo accounts first
- Start with small capital
- Monitor positions daily
- Understand how swaps work with your broker
- Have realistic expectations (10-20% annual is excellent)
- Use proper risk management
Never trade money you can't afford to lose.
Yevgeniy Koshtenko
Qualified investor (Kazakhstan & Russia). Algorithmic trading specialist since 2016. Published 100+ research papers in 15 languages on quantitative finance and machine learning for markets.
Contact:
- Email: koshtenco@gmail.com
- Telegram: @Shtenco
- VK: https://vk.com/altradinger
MIT License. Use freely with attribution.
Built on theory from:
- Harry Markowitz (Modern Portfolio Theory)
- William Sharpe (Sharpe Ratio)
- Eugene Fama (Interest Rate Parity research)
Data provided by MetaTrader 5 platform.
⭐ If this helps your trading, star the repo!