Deep analytics
gen0194 Deep Analytics
Risk, factor, sector and capacity diagnostics computed over the full single-seed backtest (training + out-of-sample), Jan 1, 2000 – Dec 1, 2025.
This is a simulated paper portfolio — no real money is being traded here. This is not investment advice.
Returns
Calendar-year returns
Each year compares the simulated strategy with the S&P 500 benchmark over the same calendar window.
Strategy gain/lossS&P 500
Risk-adjusted
Rolling 3-year Sharpe
Annualized Sharpe over a trailing three-year window, this indicates how steady the risk-adjusted return has been, better than just the headline average.
- Current
- 1.11
- Average
- 0.92
- Best
- 2.35 · Apr 7, 2006
- Worst
- -0.09 · Mar 23, 2020
Risk
Worst drawdown anatomy
The single deepest peak-to-trough decline of the backtest along with how long it took to recover.
- Depth?
- -54.7%
- Peak?
- Oct 31, 2007
- Trough?
- Nov 20, 2008
- Recovered?
- Dec 14, 2009
Peak to recovery spanned 775 days?.
Attribution
Factor exposure (Fama–French)
Regressing the strategy's returns on standard risk factors shows how much of its return is explainable by common factors. Bars are factor betas; alpha is the leftover return not explained by the factors.
- Alpha (ann.)?
- +11.0%
- R²?
- 0.65
- Observations?
- 6,518
Positioning
Sector tilts
How the basket's sectors lean versus an equal-weight eligible universe.
- Avg active share?
- 39.7%
- Median?
- 40.7%
- Max deviation?
- 23.7%
- Eff. sectors?
- 4.79
Average sector mix
- Information Technology31.0%
- Other sectors23.5%
- Industrials13.1%
- Consumer Discretionary11.9%
- Financials11.4%
- Energy9.1%
Average share of the basket by SEC SIC-derived sector across the backtest; sectors outside the top holdings are grouped as “Other sectors.”
Average tilt vs universe (overweight ▸ / ◂ underweight)
Liquidity
Capacity
How much capital the strategy could deploy before its own trading moved prices. The impact model is more practical.
Liquidity screen
Participating in a fixed slice of each name's average daily volume over a few execution days.
- Median capacity?
- $92,061,875
- 25th pct?
- $34,657,302
- Tightest?
- $11,992,035
- Impact @ median?
- 866 bps
- Impact @ 25th?
- 531 bps
- Rebalances?
- 156
ADV participation 1%?ADV lookback 20d?exec days 3d?
Impact model
The capital at which the square-root impact model hits the configured worst-name impact cap.
- Median capacity?
- $1,227,492
- 25th pct?
- $462,097
- Tightest?
- $159,894
- Impact @ median?
- 100 bps
- Impact @ 25th?
- 61 bps
- Rebalances?
- 156
impact coef 0.50?worst-name cap 100 bps?
At the current $1,000,000 size, the worst-name modeled impact runs 90 bps (median) to 250 bps (max).
Trading
Turnover
Average basket churn between rebalances, measured from the target-weight baskets below. Two-way counts both buys and sells (a full rotation = 200%); one-way is half that.
- Two-way / rebalance?
- 22%
- One-way / rebalance?
- 11%
- Two-way / year?
- 190%
- One-way / year?
- 95%
Derived from 155 rebalance transitions across the baskets below.
Holdings
Rebalance history
Every basket the strategy held, rebalance by rebalance — 156 in all. Pick a date to see that day's names and target weights.
Basket · Nov 3, 2025
45 names- ACHR4.0%
- BITO4.0%
- CDE4.0%
- CIFR4.0%
- ERIC4.0%
- HL4.0%
- IAG4.0%
- LUMN4.0%
- NVTS4.0%
- PSLV4.0%
- UEC4.0%
- CX3.9%
- UUUU3.7%
- HMY3.6%
- EQX3.4%
- AGNC3.2%
- VLY3.1%
- NWG3.1%
- PGX3.0%
- JOBY2.8%
- HLN2.6%
- AG2.5%
- SBSW2.3%
- ASX2.1%
- HBM1.8%
- TSLL1.6%
- VOD1.4%
- SILJ1.4%
- SPDN1.2%
- YMM1.2%
- SAN1.1%
- MUFG1.1%
- PDBC0.9%
- RITM0.9%
- KGC0.7%
- PDI0.7%
- PAYO0.6%
- QYLD0.6%
- VALE0.5%
- SOFI0.5%
- RKLB0.3%
- F0.2%
- ZIM0.2%
- VTRS0.2%
- GENI0.1%