# How to Implement Momentum Factor Strategies Using Skip Month in Python

> Implement skip month momentum factor strategies in Python using a long-short portfolio approach. Learn to build and backtest these strategies to outperform the market.

- Repository: [Papers With Backtest/awesome-systematic-trading](https://github.com/paperswithbacktest/awesome-systematic-trading)
- Tags: how-to-guide
- Published: 2026-07-31

---

**A skip-month momentum factor strategy ranks assets by their 12-month returns excluding the most recent month to avoid micro-structure noise and short-term reversal effects, then constructs a long-short portfolio by going long the top performers and shorting the bottom performers.**

Momentum factor strategies exploit the tendency of winning assets to continue outperforming losing assets over intermediate time horizons. In the *Awesome Systematic Trading* repository, the skip-month implementation is demonstrated through QuantConnect-compatible Python algorithms that calculate formation-period returns while explicitly excluding the most recent month's data to mitigate micro-structure bias.

## Why Skip the Most Recent Month?

Empirical research shows that the most recent month often contains short-term reversal effects and market micro-structure noise that can distort momentum signals. By **skipping the latest month** (sometimes called "skip-1-12" formation), you measure momentum from months 2 through 13, avoiding the short-term mean reversion that typically occurs in month 1. This approach is standard in academic finance and systematic trading implementations.

## Core Implementation Architecture

The repository demonstrates this pattern in [`static/strategies/momentum-factor-effect-in-stocks.py`](https://github.com/paperswithbacktest/awesome-systematic-trading/blob/main/static/strategies/momentum-factor-effect-in-stocks.py), which implements a 12-month momentum factor with a one-month skip using a rolling window and scheduled selection logic.

### Universe Selection

The algorithm first defines a manageable universe of liquid stocks. In the source file, this is configured to select the top 500 stocks by market capitalization:

```python
self.coarse_count = 500
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)

```

This universe selection runs daily, but the actual momentum calculation and rebalancing occur only on specific scheduled dates.

### Rolling Window Setup

To capture price history while maintaining computational efficiency, the algorithm stores daily adjusted prices in a `RollingWindow` with a capacity of 252 trading days (12 months × 21 trading days):

```python
self.period = 12 * 21
self.data[symbol] = RollingWindow[float](self.period)

```

This rolling window automatically discards the oldest price point when new data arrives, keeping exactly one year of history available for momentum calculations.

### The Skip-Month Scheduling Mechanism

The critical component that enforces the skip-month logic is the **monthly scheduling rule**. The algorithm triggers selection on the first trading day of each month:

```python
self.Schedule.On(self.DateRules.MonthStart(symbol), self.TimeRules.AfterMarketOpen(symbol), self.Selection)

```

Because the selection runs on `MonthStart`, the rolling window contains prices only through the end of the previous month. The data from the just-finished month has not yet been added to the window, effectively **excluding the most recent month** from the calculation without explicit filtering code.

### Momentum Calculation

Within the `FineSelectionFunction`, the algorithm computes cumulative returns using the oldest and newest elements in the rolling window:

```python
perf = {x.Symbol : self.data[x.Symbol][0] / self.data[x.Symbol][self.period-1] - 1 for x in fine}

```

Here, `self.data[x.Symbol][0]` represents the most recent price in the window (end of last month), while `[self.period-1]` represents the price 12 months prior. Since the window excludes the current month's data due to the `MonthStart` scheduling, this calculation naturally implements the **12-month skip-1 formation period**.

### Portfolio Construction and Execution

The algorithm ranks assets by their momentum scores and splits them into quantiles:

- **Long leg**: Top-performing stocks (highest momentum)
- **Short leg**: Bottom-performing stocks (lowest momentum)

Equal weights are assigned to each position. The execution logic in `OnData` checks for a selection flag, liquidates positions not in the new basket, and rebalances using `SetHoldings`:

```python
def OnData(self, data):
    if self.selection_flag:
        self.selection_flag = False
        # Liquidate positions not in new basket

        for holding in self.Portfolio.Values:
            if holding.Invested and holding.Symbol not in self.long + self.short:
                self.Liquidate(holding.Symbol)
        # Rebalance to new weights

        for symbol in self.long + self.short:
            self.SetHoldings(symbol, self.weight)

```

## Standalone Python Implementation

You can implement the same skip-month logic outside of QuantConnect using `pandas` and `yfinance`. This example calculates 12-month momentum skipping the most recent month:

```python
import yfinance as yf
import pandas as pd

def fetch_price(ticker, start, end):
    """Download daily adjusted close prices."""
    return yf.download(ticker, start=start, end=end)['Adj Close']

def momentum_skip_month(prices, lookback_months=12, skip_months=1):
    """
    Compute momentum as the cumulative return over the last `lookback_months`
    but skip the most recent `skip_months`.
    """
    # Convert calendar months to trading days (~21 per month)

    window = (lookback_months + skip_months) * 21
    
    if len(prices) < window:
        raise ValueError("Not enough price data")
    
    # Select the window that ends one month before the last observation

    recent_end = -skip_months * 21               # exclude recent month

    start_idx = recent_end - lookback_months * 21
    
    # Cumulative return over the look-back period

    return prices.iloc[recent_end] / prices.iloc[start_idx] - 1

# Example usage:

price_series = fetch_price('SPY', '2010-01-01', '2024-01-01')
mom = momentum_skip_month(price_series)
print(f"Momentum (12-month skip-1) = {mom:.2%}")

```

## Alternative Skip-Month Implementations

The *Awesome Systematic Trading* repository contains several variations of this pattern:

- **[`static/strategies/consistent-momentum-strategy.py`](https://github.com/paperswithbacktest/awesome-systematic-trading/blob/main/static/strategies/consistent-momentum-strategy.py)**: Implements a six-month momentum strategy with an explicit one-month gap between formation and holding periods.
- **[`static/strategies/time-series-momentum-effect.py`](https://github.com/paperswithbacktest/awesome-systematic-trading/blob/main/static/strategies/time-series-momentum-effect.py)**: Demonstrates time-series momentum (long-only based on positive past returns) where the skip-month concept is implicit via monthly rebalancing rules.
- **[`static/strategies/momentum-and-reversal-combined-with-volatility-effect-in-stocks.py`](https://github.com/paperswithbacktest/awesome-systematic-trading/blob/main/static/strategies/momentum-and-reversal-combined-with-volatility-effect-in-stocks.py)**: Combines momentum with reversal and volatility effects, including logic that explicitly skips recent months to avoid micro-structure bias.

## Summary

- **Schedule monthly**: Use `MonthStart` rules to ensure your calculation window naturally excludes the most recent month's data.
- **Use rolling windows**: Maintain a 12×21 day (252 trading day) rolling window to capture formation-period prices without memory bloat.
- **Calculate correctly**: Compute returns from `window[0]` (most recent in window) to `window[period-1]` (12 months prior), which automatically skips the latest month.
- **Quantile rank**: Split the universe into top and bottom deciles or quintiles for the long and short legs.
- **Rebalance monthly**: Execute trades immediately after selection to capture the momentum premium while minimizing unnecessary turnover.

## Frequently Asked Questions

### Why is the skip month important in momentum strategies?

The most recent month typically exhibits short-term reversal patterns and market micro-structure noise that can contaminate the momentum signal. By skipping this month, you measure the persistent trend over months 2-13 rather than capturing temporary price distortions that tend to reverse quickly.

### How does the QuantConnect implementation automatically skip the latest month?

The algorithm schedules selection logic using `DateRules.MonthStart`, which fires on the first trading day of the month. Since the rolling window is populated via `OnData` (which runs daily), the window at the time of selection contains data only through the end of the previous month. The current month's data points have not yet been added, effectively implementing the skip without explicit date arithmetic.

### What is the optimal lookback period for skip-month momentum?

The repository's primary implementation uses a **12-month formation period** (252 trading days) with a **1-month skip**, which aligns with academic research on the "12-1" momentum factor. However, [`consistent-momentum-strategy.py`](https://github.com/paperswithbacktest/awesome-systematic-trading/blob/main/consistent-momentum-strategy.py) demonstrates that 6-month formation periods can also be effective, particularly when combined with volatility filters.

### Can I implement skip-month momentum without QuantConnect?

Yes. The standalone Python example using `pandas` and `yfinance` demonstrates how to manually slice price arrays to exclude the most recent 21 trading days (approximately one month) when calculating returns. The key is ensuring your return calculation ends at `current_index - 21` rather than the latest available price.