FGV Quant Capital · Simulated fund
A simulated quantitative fund
FGV Quant Capital is the league's simulated fund. It combines two systematic strategies in Brazilian equities, Pairs Trading and Momentum, run with explicit rules, transaction costs and a monthly management report against the CDI.
The fund in numbers
From inception in July 2025 to August 2026. All results are simulated.
Cumulative return
+18.34%
CDI over the period: 17.44%
Annualized volatility
5.82%
Lower than either strategy on its own
Sharpe ratio
0.11
Return above the CDI per unit of risk
Correlation between strategies
0.028
Pairs Trading × Momentum
Reported monthly returns (simulated fund)
Simulated fund12 months reported · Jul 2025–Aug 2026 · 2 months without a report
Show month-by-month values
- Jul 2025
- +1.67%
- Sep 2025
- +3.56%
- Oct 2025
- +1.18%
- Nov 2025
- +5.37%
- Jan 2026
- +1.14%
- Feb 2026
- -0.75%
- Mar 2026
- -1.97%
- Apr 2026
- +0.02%
- May 2026
- +0.75%
- Jun 2026
- +2.17%
- Jul 2026
- +0.29%
- Aug 2026
- +3.84%
Overview by strategy
| Metric | Pairs Trading | Momentum | Fund | CDI |
|---|---|---|---|---|
| Total return | 18.96% | 17.54% | 18.34% | 17.44% |
| Annualized volatility | 6.00% | 9.82% | 5.82% | – |
| Sharpe ratio | 0.18 | 0.01 | 0.11 | – |
| Maximum drawdown | -5.72% | -6.88% | – | – |
| Positive months | 12 | 9 | 10 | 14 |
| Negative months | 2 | 5 | 4 | 0 |
| Months above the CDI | 8 | 7 | 6 | – |
| Months below the CDI | 6 | 7 | 8 | – |
| Best month | 4.56% | 6.29% | 5.37% | 1.28% |
| Worst month | -2.03% | -4.01% | -1.50% | 1.00% |
Source: the league's management report for August 2026. Simulated fund.
Pairs Trading
A relative-value strategy: instead of forecasting whether the market rises or falls, it bets that the relationship between two similar assets returns to normal.
- 1
Pair universe
We start from liquid stocks and look for pairs with statistically significant cointegration and a low SSD (sum of squared distances between normalized prices): assets that have historically moved together.
- 2
Signal
For each pair, we track the spread between the two prices and standardize it into a z-score. The further from zero, the larger the deviation from the historical relationship.
- 3
Entry and exit
A position opens when the z-score moves past about 2 standard deviations: we buy the relatively cheap asset and sell the expensive one. It closes when the spread comes back to around 0.5 standard deviations.
- 4
Position management
Open positions can roll over the month end (carry-over), for up to 42 trading days. Selected pairs that never hit the entry trigger are not traded.
- 5
Costs
The simulation charges 2.5 bps per entry and per exit, plus 2 bps per day on short positions as an approximation of borrow costs.
Life of a trade
Illustrative series, generated only to show the rule. It is not a real trade from the fund.
Spread z-score
z = (spread − mean) ÷ standard deviation
How a trade ends
Convergence
The spread comes back to the exit band and the position is closed.
Divergence
The spread widens instead of converging and the position is closed.
Carry-over
The position stays open into the next month, within the trading-day limit.
Momentum
A trend strategy: it ranks stocks by the strength of their recent performance, buys the strongest and sells the weakest.
- 1
Ranking
Every month, the stocks in the universe are ranked by a momentum score that measures the strength of recent relative performance.
- 2
Long-short portfolio
We buy the highest-scoring names and sell the lowest-scoring ones. The recent configuration holds 15 names on each side, 30 positions in total.
- 3
Asymmetric weights
The long leg gets 1.5x and the short leg 0.5x. The fund stays net long the market, while the short leg aims to profit from the weakest names falling.
- 4
Rebalancing
Every position is rebuilt on the first trading day of the month. Portfolio size and weights are reviewed periodically by the managers.
Portfolio exposure
- Long+150%
- Short−50%
- Gross exposure+200%
- Net exposure+100%
Percent of net assets, in the strategy's recent configuration.
Why combine the two
The two strategies make money for different reasons: Pairs Trading relies on spreads converging, Momentum on trends persisting. As a result, their returns barely move together: the correlation between them is 0.028 since inception.
The effect shows up in volatility. Together, they form a fund that swings less than either strategy on its own.
Annualized volatility
August 2026- Pairs Trading6.00%
- Momentum9.82%
- Fund5.82%
How to read the metrics
- Annualized volatility
- Standard deviation of returns, on an annual basis. It measures how much results swing.
- Sharpe ratio
- Return above the CDI divided by volatility: how much the fund delivers per unit of risk.
- Maximum drawdown
- The largest cumulative fall from a peak to the following trough.
- Correlation
- Ranges from −1 to 1. Near zero, it means the strategies move independently.
Maximum drawdown
-5.72%
Pairs Trading
Maximum drawdown
-6.88%
Momentum
A monthly management cycle
The fund reproduces the routine of a quantitative asset manager, from research to reporting.
- 01
Research and selection
Update the asset universe, test the statistical quality of the pairs and recompute the momentum scores.
- 02
Simulated execution
Open and close positions following each strategy's rules, net of transaction costs.
- 03
Monitoring
Track z-scores, volatility, drawdown and correlation, and consider incremental parameter adjustments.
- 04
Management report
Publish the month's performance against the CDI, the breakdown by strategy and the attribution by asset.
Management reports on LinkedIn
Every month, the league publishes the simulated fund's management report on FGV Quant's LinkedIn page.
FGV Quant Capital is a fictional educational project that aims to rigorously simulate the operations and routines of a quantitative asset manager. Data, assumptions and results are simulated, may differ materially from actual market conditions and are not a promise of returns, an investment offer or a recommendation to buy or sell securities.