Read public virtual trading rankings and scores alongside their observation periods, costs and trade counts.
League Basics · Updated 2026-10-10
A virtual trading ranking runs several strategies with virtual money under the same rules and sorts them by a common score. If every strategy starts from the same conditions, is priced from the same market data and is measured with the same formulas, the differences you see are more likely to come from the strategies themselves than from the way each result was reported. A single screenshot of a winning strategy tells you almost nothing, because you do not know how long it ran, what it risked or how many others were quietly switched off. A ranking tries to fix that by putting every record side by side, losers included.
That does not make a ranking a list of recommendations. It is a measurement tool. Its value depends entirely on how it is built, how the score is calculated and how honestly it shows the strategies that are not doing well. The rest of this guide explains each of those pieces, then works through the score with made-up example figures.
The public virtual trading league currently has five strategies: HAA (splits across stocks, bonds and commodities by risk), SPY Trend Filter (holds the S&P 500 only above its 200-day average and in a clear trend, using the 3x index ETFs TQQQ and UPRO and the T-bill ETF BIL), Dynamic Regime (picks low-volatility sectors to suit the market regime), Style Selection (moves to whichever of growth and value is stronger, using 3x ETFs) and Crypto TSMOM (holds only rising coins and exits when the trend turns; long-only USDT perpetuals, 1x in total, no leverage). The first four are US ETF strategies; the last is a crypto strategy.
Every strategy runs in virtual trading only. No real order is sent to any exchange or broker. Each strategy starts with $1,000 of virtual money. The US ETF strategies decide on completed daily bars, and Crypto TSMOM trades USDT perpetual futures with funding recorded. Each virtual fill is charged a 0.04% fee on the value traded, and open positions are repriced from market prices every 15 minutes. Rankings are recomputed every hour and published as delayed snapshots, so what you see is the recent past rather than a live feed.
A strategy is ranked only after at least 28 days of virtual trading and at least one fill. Before that it shows no return or score, only "Preparing (n/28 days)", so that a few days of returns are not read as a ranking. The fill condition matters too: a record with no trades would score near the maximum on drawdown and stability simply by doing nothing. A perfectly flat record with data quality 80 and a recent-performance score of 50, for example, would collect 0.30 × 50 + 0.25 × 100 + 0.15 × 50 + 0.15 × 50 + 0.10 × 100 + 0.05 × 80 = 69.0 points without taking any risk at all.
Each ranked strategy gets a league score from 0 to 100. It is a weighted blend of six sub-scores, each also on a 0–100 scale. Total return counts for 30%, drawdown for 25%, Sharpe for 15%, recent performance for 15%, stability for 10% and data quality for 5%.
The return score is 50 plus 3 times the total return in percent, so a flat strategy sits at 50 and every percentage point of gain or loss moves it by 3. The drawdown score is 100 plus 4 times the maximum drawdown in percent; because drawdown is negative, a strategy that never fell keeps 100 and a 10% drawdown drops it to 60. The Sharpe score is 50 plus 20 times the Sharpe ratio, so a Sharpe of 2.5 or more already hits the 100 cap. Sharpe is annualized from daily returns over 365 days and is only calculated once there are at least seven daily returns. The stability score is 100 minus 5 times the absolute drawdown in percent. Every sub-score is clamped between 0 and 100. In the current formula version the recent-performance score uses the same calculation as the return score.
The data-quality score reflects how much the record can be trusted. It falls by 20 points if the equity curve has fewer than 4 points, by 30 if the strategy has never synced, by 25 if its data is more than 24 hours stale and by 20 if it is paused. In practice the table publishes this as the data confidence score next to each row.
Notice what the weights reward. Return matters most, but drawdown and stability together carry 35% and both punish deep falls, so a strategy that earns a little while barely dipping can outrank one that earns more with large swings. That is by design: the score is a risk-adjusted ranking, not a profit ranking.
The figures below are invented to show the arithmetic; they are not the record of any strategy in the league. Suppose example strategy A has traded virtually for 28 days with a total return of +4.0%, a maximum drawdown of −3.0%, a Sharpe of 1.5 and a data confidence score of 95.
Return score: 50 + 3 × 4.0 = 62.0. Drawdown score: 100 + 4 × (−3.0) = 88.0. Sharpe score: 50 + 20 × 1.5 = 80.0. Recent-performance score: the same as the return score, 62.0. Stability score: 100 − 5 × 3.0 = 85.0. Data-quality score: 95.
Now apply the weights: 0.30 × 62.0 = 18.60; 0.25 × 88.0 = 22.00; 0.15 × 80.0 = 12.00; 0.15 × 62.0 = 9.30; 0.10 × 85.0 = 8.50; 0.05 × 95 = 4.75. The six parts add up to 75.15.
Compare example strategy B over the same days: a total return of +10.0%, a maximum drawdown of −12.0%, a Sharpe of 1.2 and data confidence 95. Its sub-scores are 80.0 for return, 52.0 for drawdown, 74.0 for Sharpe, 80.0 for recent performance, 40.0 for stability and 95 for data quality, and the weighted sum is 24.00 + 13.00 + 11.10 + 12.00 + 4.00 + 4.75 = 68.85. B earned 2.5 times as much, but its drawdown was four times deeper, so A scores higher.
Two lessons stand out. First, a Sharpe annualized over 365 days from a short run of daily returns is easily inflated, and the cap means the score treats every Sharpe of 2.5 or more the same. Second, much of the score comes from not losing much rather than from earning much.
Short windows are the first limit. Twenty-eight days is only the minimum for being ranked; it is far too short to tell skill from conditions. Markets can stay in one regime for months, so a trend-following strategy that shines in a trending month may simply be in the right weather.
Luck is the second. When several strategies run at once, some will finish at the top by chance even if none has a real edge.
Different markets are the third. US ETF strategies and a crypto strategy differ in trading hours, volatility and costs. Even in one table, much of a score gap can come from the market rather than the rules, so check what each strategy trades. Strategies that use 3x ETFs (SPY Trend Filter and Style Selection) magnify both gains and losses.
Finally, virtual trading results are not live results. Virtual fills do not move the market, do not wait in a queue and do not suffer outages, so live trading with the same rules will usually do worse.
Use the ranking to learn how different rules behave under the same conditions, not to pick something to copy. Always check the length of the record, the drawdown, the data confidence and what the strategy trades before looking at the rank. Nothing in the league is investment advice, past virtual trading results do not predict future returns, and any real trading can lose money, including more than you expect when leverage is involved.
Research explanations are educational, not investment recommendations. Development implements rules defined by the customer; work scope and payment are confirmed separately.