Implementing a Time Series Momentum Strategy Across Asset Classes: A QuantConnect Blueprint

The awesome-systematic-trading repository provides a production-ready time series momentum strategy that trades 54 futures contracts across commodities, currencies, equities, and bonds using inverse-volatility weighting and a 10% volatility target.

A time series momentum strategy generates trading signals based on an asset's own past returns rather than relative performance against peers. The implementation in the paperswithbacktest/awesome-systematic-trading repository offers a transparent, modular framework built on the QuantConnect Lean engine, making it ideal for researchers seeking to replicate or extend classic cross-asset momentum research.

Asset Universe and Data Architecture

The strategy operates on a diversified universe of 54 continuous futures contracts spanning agriculture, metals, energy, FX, and sovereign bonds. In static/strategies/time-series-momentum-effect.py, the universe is hard-coded in lines 24-84, ensuring reproducibility across backtests.

QuantpediaFutures Data Ingestion

Rather than relying on generic market data, the algorithm uses a custom QuantpediaFutures data type to fetch back-adjusted price series from Quantpedia's public CSV feed. The Initialize method invokes AddData for each symbol, configuring daily resolution and warm-up periods:

def Initialize(self):
    self.SetStartDate(2000, 1, 1)
    self.SetCash(10_000_000)
    
    # 54 futures symbols defined in lines 24-84

    self.symbols = ["CME_S1", "CME_W1", "CME_CL1", "CME_EC1", "CME_TY1", ...]
    
    self.data = {}
    self.period = 12 * 21  # ≈252 trading days (12 months)

    self.SetWarmUp(self.period, Resolution.Daily)
    
    for sym in self.symbols:
        data = self.AddData(QuantpediaFutures, sym, Resolution.Daily)
        data.SetFeeModel(CustomFeeModel())
        data.SetLeverage(20)
        self.data[sym] = RollingWindow[float](self.period)

Each symbol maintains a RollingWindow storing the last 252 closing prices, enabling efficient calculation of annual returns without redundant history requests.

Signal Generation and Risk Management

The core logic executes on the first trading day of each month, guarded by the self.recent_month flag to prevent intra-month churn.

Momentum Calculation

The algorithm computes 12-month total return as the primary signal:

performance = (current_price / price_12_months_ago) - 1

Positive performance triggers a long position; negative performance triggers a short position. This binary signal is then refined through volatility scaling.

Inverse-Volatility Weighting

Position sizing employs inverse-volatility weighting to neutralize risk contributions across asset classes. The strategy calculates annualized volatility using the last 60 daily returns (approximately 3 months):

volatility = np.std(returns_last_60_days) * sqrt(252)

Raw inverse-volatility weights are normalized to sum to 1, then scaled by a volatility-targeting leverage factor. The algorithm aims for a 10% portfolio volatility ceiling, capping leverage at 4× to prevent excessive exposure during low-volatility regimes.

Execution and Cost Modeling

The CustomFeeModel class (lines 32-37) applies a realistic 0.5 basis points transaction cost to each trade, ensuring backtested results account for frictions in futures markets:

class CustomFeeModel:
    def GetOrderFee(self, security, order):
        return OrderFee(CashAmount(0.5 * order.AbsoluteQuantity * security.Price * 0.0001, 'USD'))

During monthly rebalancing, the algorithm liquidates positions that no longer meet the long/short criteria and invokes SetHoldings to establish new targets. The method automatically handles directional exposure (long vs. short) based on the sign of the 12-month return.

Extending the Framework

Practitioners can modify the strategy by adjusting three key parameters defined in the initialization block:

• self.period: Change from 252 days to 126 days (6 months) for shorter-term momentum
• self.vol_target_period: Adjust the 60-day volatility lookback to capture different market regimes
• self.targeted_volatility: Increase or decrease the 10% annualized volatility target based on risk appetite

The modular structure allows swapping the historical volatility estimator with GARCH or EWMA models without disrupting the universe selection or execution logic.

Summary

• The awesome-systematic-trading repository hosts a complete time series momentum implementation in static/strategies/time-series-momentum-effect.py
• The strategy trades 54 futures contracts across five asset classes using 12-month momentum signals
• Inverse-volatility weighting and a 10% volatility target ensure risk-balanced exposure
• The QuantpediaFutures data class fetches back-adjusted continuous contracts automatically
• A 0.5 bps fee model provides realistic transaction cost modeling for futures backtests

Frequently Asked Questions

What is the difference between time series momentum and cross-sectional momentum?

Time series momentum (absolute momentum) generates signals based on an asset's own past performance—going long if the past 12-month return is positive and short if negative. Cross-sectional momentum ranks assets against each other within the same period and goes long the top performers while shorting the bottom deciles. The implementation in this repository uses time series signals, making it suitable for trend-following across uncorrelated asset classes.

How does the volatility targeting mechanism prevent portfolio blow-ups?

The algorithm calculates the realized volatility of the portfolio using the last 60 days of returns, then scales positions to achieve a 10% annualized volatility target. If market volatility spikes, the leverage factor decreases proportionally; if volatility collapses, leverage increases up to a hard 4× cap. This dynamic sizing prevents the strategy from taking excessive risk during calm periods while maintaining exposure during turbulence.

Can I replace the Quantpedia data source with my own futures data?

Yes. The QuantpediaFutures class serves as a drop-in replacement for standard AddEquity or AddFuture calls. To use alternative data sources, modify the Reader method within the class (lines 24-31) to parse your CSV format, or replace AddData<QuantpediaFutures> with AddFuture and subscribe to specific contract chains if using QuantConnect's native brokerage data feeds.

Why does the strategy rebalance only on the first day of each month?

Monthly rebalancing aligns with the 12-month lookback period used for signal generation, ensuring that new information is incorporated while avoiding excessive transaction costs from daily turnover. The self.recent_month check in the OnData method guarantees that signals are processed only when the calendar month changes, reducing slippage and fees while capturing the bulk of momentum profits.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}