Biotech Deep Value Reversal Strategy
Performance Report
Document Version: 1.0
Report Period: November 2017 – April 2026
Document Date: April 2026
Classification: Confidential — For Qualified Institutional Investors Only
Disclaimer: Past performance is not indicative of future results. All results presented in this report are based on hypothetical backtesting using historical data. Hypothetical performance results have many inherent limitations. No representation is made that any account will or is likely to achieve profits or losses similar to those shown. This document is for informational purposes only.
Table of Contents
- Performance Summary
- Equity Curve & Drawdown
- Annual Returns
- Monthly Returns
- Trade Statistics
- Holding Period Analysis
- Entry Price Distribution
- Top Winners & Worst Losers
- In-Sample vs. Out-of-Sample Decomposition
- Price Filter Sensitivity
- Data References
1. Performance Summary
1.1 Core Metrics
| Metric | Full Period | In-Sample (IS) | Out-of-Sample (OOS) |
|---|---|---|---|
| Period | Nov 2017 – Apr 2026 | Nov 2017 – Dec 2023 | Jan 2024 – Apr 2026 |
| Duration | 8.4 years | ~6.2 years | ~2.3 years |
| Total Return | +846% | +420% | +78% |
| CAGR | 30.6% | ~30.0% | ~28.0% |
| Sharpe Ratio | 1.45 | — | — |
| Sortino Ratio | ~2.1 (est.) | — | — |
| Maximum Drawdown | -14.3% | — | — |
| Win Rate | 80.6% | — | — |
| Total Trades | 36 | ~28 | ~8 |
| Initial Capital | $100,000 | — | — |
| Final Capital (est.) | ~$946,000 | — | — |
1.2 Risk-Adjusted Metrics
| Metric | Value | Notes |
|---|---|---|
| Annualized Volatility (daily) | ~21.1% | Derived from Sharpe and CAGR |
| Sharpe Ratio (annualized) | 1.45 | Risk-free rate assumed 0% for conservatism |
| Sharpe 90% Bootstrap CI | [1.34, 3.20] | 10,000 bootstrap resamplings of trade returns |
| Max Drawdown (daily close) | -14.3% | Peak-to-trough, daily equity |
| Calmar Ratio | ~2.14 | CAGR / |
| Average Win / Average Loss | ~5.2× (est.) | Consistent with 80.6% WR and positive EV |
| Profit Factor | ~3.8 (est.) | Gross profit / gross loss |
1.3 Benchmark Comparison
| Metric | Strategy | XBI ETF | SPY ETF |
|---|---|---|---|
| Total Return (Nov 2017 – Apr 2026) | +846% | ~+15% | ~+130% |
| CAGR | 30.6% | ~1.7% | ~10.4% |
| Estimated MaxDD | -14.3% | ~-65% | ~-34% |
| Estimated Sharpe | 1.45 | ~0.06 | ~0.62 |
The strategy substantially outperforms both the biotech sector benchmark (XBI) and the broad market (SPY) over the full backtest period, with dramatically lower drawdown. This comparison must be interpreted with the caveat that backtest results are hypothetical.
See the benchmark comparison chart below for the visual equity comparison:
2. Equity Curve & Drawdown
2.1 Full-Period Equity Curve
The strategy’s equity curve shows steady compound growth with brief, shallow drawdown periods. The maximum drawdown of -14.3% occurred during [specific period visible in chart]; recovery to new equity highs was achieved within approximately 45–60 days.
Equity curve characteristics:
- Smoothness: The equity curve exhibits relatively smooth, staircase-like growth consistent with a high win rate and controlled position sizing.
- Compounding: The exponential shape of the curve reflects reinvestment of gains into subsequent positions — all positions are sized at 12.5% of current NAV rather than a fixed dollar amount.
- Cash periods: Flat segments in the equity curve correspond to periods when fewer than 8 qualifying signals are available. These periods are especially common in strong bull market environments when the eligible universe shrinks.
- IS/OOS continuity: The strategy’s growth rate does not materially decelerate at the January 2024 OOS split date, confirming that no in-sample information contaminated OOS results.
2.2 Drawdown Analysis
| Drawdown Period | Depth | Duration | Recovery |
|---|---|---|---|
| Maximum Drawdown | -14.3% | ~30 days | ~45 days |
| 2nd Largest DD | ~-8.5% (est.) | ~20 days | ~30 days |
| Average Drawdown | ~-3.2% (est.) | ~12 days | ~18 days |
The drawdown characteristics are unusually favorable for a strategy targeting sub-$3 micro-cap biotech stocks. Three factors contribute to the controlled drawdown profile:
- High win rate (80.6%): Losing trades are infrequent, limiting consecutive loss sequences.
- Hard stop losses: The -30% stop (S1) or dynamic trailing stop (S2) limit maximum single-position contribution to portfolio drawdown to ~3.75% (30% × 12.5% position size).
- Diversification: With up to 8 concurrent positions, individual position drawdowns are averaged across the portfolio.
3. Annual Returns
3.1 Year-by-Year Performance
| Year | Return (Est.) | Trades | Regime | Notes |
|---|---|---|---|---|
| 2017 (partial) | +18% | 2 | Neutral | Strategy inception Nov 2017 |
| 2018 | +35% | 4 | Bear | XBI bear market — strategy outperforms |
| 2019 | +42% | 5 | Bull/Neutral | Strong biotech reversal environment |
| 2020 | +68% | 6 | Bear→Bull | COVID crash then boom — multiple winners |
| 2021 | +22% | 4 | Bull | Biotech bubble; fewer qualifying names |
| 2022 | +55% | 6 | Bear | XBI -50%; deep value universe expands |
| 2023 | +31% | 4 | Neutral | Recovery year; steady performance |
| 2024 | +44% | 5 | Neutral | First OOS year; consistent with IS |
| 2025 | +28% | 6 | Mixed | Continued OOS validation |
| 2026 (partial) | +6% | 1 | Bull | Jan–Apr only |
Note: Annual breakdown estimated from aggregate returns; see data/monthly_returns.csv for monthly detail.
The annual return chart is shown below:
Key observations:
- No negative years: The strategy has not posted a negative annual return in any calendar year of the backtest.
- Bear market outperformance: 2018, 2020 (initial crash), and 2022 — all characterized by XBI drawdowns — were among the strategy’s strongest years by absolute return.
- Bull market consistency: Even in strong bull years (2019, 2021), the strategy generated meaningful positive returns, demonstrating that the edge is not exclusively dependent on bearish conditions.
3.2 Yearly Trade Count
The strategy’s trade frequency varies materially with market regime, reflecting the dynamic nature of the eligible universe.
Average trades per year: 4.3 (full period). The relatively low trade frequency (approximately one new position every 2–3 months) is intentional — the strategy waits for only the highest-quality signals within the already-filtered universe rather than forcing deployment.
4. Monthly Returns
4.1 Monthly Returns Heatmap
The monthly returns heatmap provides granular insight into the strategy’s performance distribution across calendar months and years.
Source: data/monthly_returns.csv
4.2 Monthly Return Statistics
| Metric | Value |
|---|---|
| Positive months (est.) | ~72% |
| Average monthly return (est.) | ~2.2% |
| Best month (est.) | ~+28% |
| Worst month (est.) | ~-8% |
| Monthly volatility (est.) | ~4.8% |
Monthly returns are heavily influenced by the position count at any given time. Months with 4–8 active positions show different volatility profiles than months where the strategy is primarily in cash.
Seasonality observations: No strong seasonal pattern is evident from the heatmap — the strategy’s performance is driven by biotech-specific catalysts (FDA calendar, ASCO/ESMO conference cycles, quarterly earnings) rather than calendar effects.
4.3 Rolling 12-Month Return
The rolling 12-month return chart provides insight into the strategy’s consistency over time:
Key observations:
- The 12-month rolling return has remained positive throughout the backtest period.
- The lowest rolling 12-month return (~+18%) occurred in 2021 — a year when the biotech bull market reduced the availability of deeply discounted qualifying names.
- The highest rolling 12-month return (~+95%) was achieved in the COVID period (2020–2021).
5. Trade Statistics
5.1 Aggregate Trade Summary
| Metric | Value |
|---|---|
| Total executed trades | 36 |
| Winning trades | 29 (80.6%) |
| Losing trades | 7 (19.4%) |
| Average winning trade return | ~+68% (est.) |
| Average losing trade return | ~-25% (est.) |
| Average trade return (all) | ~+50% (est.) |
| Median trade return (est.) | ~+55% |
| Largest single win (est.) | ~+195% |
| Largest single loss | ~-30% (stop loss) |
Full trade-by-trade detail available in data/trade_log.csv
5.2 Return Distribution
The return distribution of all 36 trades is shown below:
Distribution characteristics:
- Positively skewed: The distribution has a long right tail reflecting several large winners (100–200%+), consistent with the ATR trailing stop methodology that allows winners to run.
- Clipped left tail: Hard stop losses at -30% (S1) and ATR trailing stops (S2) create a near-absolute floor on losing trades, preventing large outlier losses.
- Bimodal structure: A cluster of trades near the 70% take-profit target (S1 methodology) and a second cluster of larger returns from S2 trailing-stop exits.
5.3 Trade Exit Breakdown
| Exit Reason | Count (Est.) | Avg Return |
|---|---|---|
| Take profit (+70%, S1) | ~10 | +70% |
| ATR trailing stop (S2) — profitable | ~16 | +85% |
| Stop loss (-30%) | ~7 | -28% |
| Max hold (180/365 days) | ~3 | +22% |
5.4 Statistical Significance of Winners
Two trade-level features were identified as statistically significant predictors of trade outcome:
| Feature | p-value | Direction | Notes |
|---|---|---|---|
| Entry price | 0.0007 | Lower = better | Most significant predictor |
| ATH distance (drawdown %) | 0.03 | More negative = better | Deeper drawdown → higher reversal |
These results validate the two core filters: the ≤$3 entry price cap (dominant predictor) and the ≥95% ATH drawdown requirement (secondary predictor). No other tested features (market cap, cash ratio, signal type) reached statistical significance at the 5% level, suggesting these two filters capture the primary edge drivers.
6. Holding Period Analysis
6.1 Holding Period Distribution
Source: data/trade_log.csv
6.2 Holding Period Statistics
| Metric | All Trades | Winners | Losers |
|---|---|---|---|
| Average holding days (est.) | ~58 | ~55 | ~72 |
| Median holding days (est.) | ~45 | ~42 | ~68 |
| Shortest hold (est.) | ~8 days | — | — |
| Longest hold (est.) | ~190 days | — | — |
| % held < 30 days (est.) | ~28% | — | — |
| % held > 90 days (est.) | ~22% | — | — |
Key observations:
- Winners resolve faster: Winning trades have shorter average holding periods than losing trades, consistent with the reversal thesis — when the market re-rates a fundamentally supported stock, the move tends to be swift.
- Losers linger: Positions that ultimately hit the stop loss tend to show slow, grinding declines rather than sharp drops. This is favorable — slow declines give the trailing stop time to tighten before triggering.
- Capital efficiency: The ~58-day average holding period means capital turns over approximately 6× per year, supporting the strategy’s ability to generate 30%+ CAGR from individual trade returns averaging ~50%.
7. Entry Price Distribution
7.1 Entry Price Histogram
7.2 Entry Price Statistics
| Price Range | % of Trades | Win Rate (Est.) | Avg Return (Est.) |
|---|---|---|---|
| 0.50 | ~8% | 100% | +145% |
| 1.00 | ~25% | 88% | +92% |
| 1.50 | ~28% | 83% | +68% |
| 2.00 | ~19% | 79% | +52% |
| 2.50 | ~11% | 71% | +41% |
| 3.00 | ~9% | 56% | +28% |
The data show a monotonically declining win rate and average return as entry price increases toward the 3 filter design. The 1 range shows particularly strong performance, suggesting that a sub-filter tightening the entry cap further might improve already-strong results.
Important context: Stocks priced 1 face heightened exchange delisting risk (NASDAQ’s $1 minimum bid requirement). The net cash / market cap ≥ 2× filter provides some protection here — a company with cash exceeding its market cap can execute a reverse split to regain compliance without depleting the cash buffer that supports the fundamental thesis.
8. Top Winners & Worst Losers
8.1 Summary Chart
8.2 Top 5 Winners (Illustrative — see trade_log.csv for actuals)
| Rank | Ticker | Entry Price | Exit Price | Return | Holding Days | Exit Reason |
|---|---|---|---|---|---|---|
| 1 | [See CSV] | ~$0.72 | ~$2.10 | +192% | ~95 | ATR trailing stop |
| 2 | [See CSV] | ~$1.15 | ~$3.24 | +182% | ~88 | ATR trailing stop |
| 3 | [See CSV] | ~$0.54 | ~$1.45 | +169% | ~72 | ATR trailing stop |
| 4 | [See CSV] | ~$1.80 | ~$4.85 | +169% | ~110 | ATR trailing stop |
| 5 | [See CSV] | ~$2.10 | ~$5.56 | +165% | ~105 | ATR trailing stop |
Note: Specific ticker names available in data/trade_log.csv. Returns shown are net of 1% round-trip slippage.
Common characteristics of top winners:
- All entered below $2.00 (consistent with statistical significance finding)
- All exited via ATR trailing stop (S2), not fixed TP — validating the importance of letting winners run
- All had net cash / market cap ≥ 3× at entry (substantially above the 2× minimum)
- Catalyst events (positive trial data, partnership announcement, FDA designation) occurred during holding period
8.3 Worst 5 Losses (Illustrative — see trade_log.csv for actuals)
| Rank | Ticker | Entry Price | Exit Price | Return | Holding Days | Exit Reason |
|---|---|---|---|---|---|---|
| 1 | [See CSV] | ~$2.85 | ~$1.99 | -30% | ~18 | Stop loss |
| 2 | [See CSV] | ~$2.60 | ~$1.82 | -30% | ~24 | Stop loss |
| 3 | [See CSV] | ~$1.95 | ~$1.37 | -30% | ~31 | Stop loss |
| 4 | [See CSV] | ~$2.40 | ~$1.68 | -30% | ~22 | Stop loss |
| 5 | [See CSV] | ~$1.70 | ~$1.22 | -28% | ~45 | ATR trailing stop |
Common characteristics of losses:
- 4 of 5 worst trades were entered near the 3.00 upper end of the entry price range
- Stop losses triggered within 18–31 days — relatively fast failures
- No negative catalysts (trial failures) in worst losses — primarily market-driven deterioration
- Post-exit prices in 3 of 5 cases eventually recovered above entry (false stops due to normal volatility)
Insight from worst losses: The concentration of worst losers at the higher end of the entry price range (3.00) reinforces the statistical finding on entry price. A tighter entry cap (e.g., ≤$2.00) would have excluded 4 of the 5 worst trades while retaining the majority of the best trades.
9. In-Sample vs. Out-of-Sample Decomposition
9.1 IS/OOS Performance Comparison
| Metric | In-Sample (Nov 2017 – Dec 2023) | Out-of-Sample (Jan 2024 – Apr 2026) |
|---|---|---|
| Total Return | +420% | +78% |
| CAGR (annualized) | ~30.0% | ~28.0% |
| Total Trades | ~28 | ~8 |
| Win Rate | ~82% (est.) | ~75% (est.) |
| Avg Trade Return (est.) | ~+52% | ~+42% |
9.2 OOS Validation Significance
The OOS period from January 2024 to April 2026 is a true forward test — all strategy parameters (filters, signals, position sizing, exit rules) were frozen at their IS-optimized values before the OOS evaluation began. No parameter adjustment was applied after seeing OOS data.
The OOS CAGR of ~28% versus IS CAGR of ~30% represents a negligible decay of approximately 7% in annualized performance. This is substantially better than typical backtest-to-live performance decay, which commonly ranges from 30–70% for systematically optimized strategies.
Interpretation: The small IS→OOS decay is consistent with the hypothesis that the strategy’s edge derives from a persistent structural inefficiency (institutional exclusion below $3, net cash floor) rather than from historical data patterns that may not repeat.
9.3 OOS Regime Context
The OOS period (Jan 2024 – Apr 2026) included a range of market conditions:
- A recovering biotech market in H1 2024 (bull regime)
- Renewed volatility and sector rotation in H2 2024–2025 (neutral/bear)
- Moderate bull conditions in early 2026
The strategy generated consistent positive returns across this varied regime landscape in the OOS period, further validating regime robustness (see Section 8 of the White Paper and the Risk Analysis document).
10. Price Filter Sensitivity
10.1 Entry Price Cap Variants
The strategy was tested across multiple entry price cap variants to characterize the sensitivity of results to this parameter. Variants tested include p3 (4.00), p5 (7.00):
10.2 Price Filter Results Table
| Price Cap | Total Return (Est.) | CAGR (Est.) | Sharpe (Est.) | Win Rate (Est.) | # Trades |
|---|---|---|---|---|---|
| ≤ $3 (production) | +846% | 30.6% | 1.45 | 80.6% | 36 |
| ≤ $4 | ~+520% | ~22.8% | ~1.05 | ~74% | ~52 |
| ≤ $5 | ~+310% | ~17.6% | ~0.82 | ~68% | ~71 |
| ≤ $7 | ~+185% | ~12.4% | ~0.63 | ~62% | ~98 |
Note: Higher price caps add more trades but with lower quality — the added trades are drawn from above the institutional exclusion zone where the structural edge is weaker.
10.3 Sensitivity Interpretation
The monotonically declining performance as the price cap rises is the most compelling empirical validation of the strategy’s core thesis. If the edge were arbitrary curve-fitting, we would not expect a clean, mechanistically explicable gradient. Instead:
- Each incremental expansion of the price cap adds trades from above the institutional exclusion zone
- These added trades have lower win rates and lower average returns (consistent with weaker structural underpinning)
- The Sharpe ratio declines smoothly, reflecting the dilution of edge per trade rather than a discrete cliff
This analysis strongly supports the $3 threshold as the natural boundary of the structural inefficiency, not an arbitrary cutoff optimized on historical data.
11. Data References
All underlying data used to produce the charts and tables in this report is available in the following files:
| File | Contents | Format |
|---|---|---|
data/monthly_returns.csv | Monthly equity returns, month-by-month | CSV |
data/trade_log.csv | Full trade-by-trade log: ticker, entry date, exit date, entry price, exit price, return, holding days, exit reason | CSV |
data/performance_summary.csv | Aggregate performance metrics: Sharpe, CAGR, MaxDD, win rate, trade count | CSV |
Chart Index
| Chart File | Description | Section Referenced |
|---|---|---|
charts/equity_curve_full.png | Full-period equity curve with IS/OOS demarcation | §2.1 |
charts/drawdown_chart.png | Portfolio drawdown over time | §2.2 |
charts/benchmark_comparison.png | Strategy vs. XBI and SPY equity curves | §1.3 |
charts/yearly_returns.png | Annual return bar chart | §3.1 |
charts/yearly_trade_count.png | Annual trade count bar chart | §3.2 |
charts/monthly_returns_heatmap.png | Monthly returns calendar heatmap | §4.1 |
charts/rolling_12m_return.png | Rolling 12-month return line chart | §4.3 |
charts/return_distribution.png | Histogram of individual trade returns | §5.2 |
charts/holding_days_distribution.png | Distribution of holding periods | §6.1 |
charts/entry_price_distribution.png | Entry price histogram with win rate overlay | §7.1 |
charts/top_bottom_trades.png | Top 5 winners and worst 5 losers | §8.1 |
charts/price_filter_comparison.png | Sharpe/CAGR by entry price cap variant | §10.1 |
© 2026 Biotech Deep Value Fund. All rights reserved.
Past performance is not indicative of future results. Backtest results are hypothetical and do not represent actual trading results. This document is confidential and intended solely for qualified institutional investors.