SH
Shirley Ho Portfolio
Invests via RedJay Asset Management Inc. | Managed by Shirley Ho
Invests via RedJay Asset Management Inc.
Managed by Shirley Ho
Automatically Tracked
Veteran data scientist and entrepreneur focused on deep-tech and climate-related applications, specializing in scalable machine learning models for scientific and commercial use. Shirley Ho combines academic training in quantitative science with industry roles building productized AI systems, advising and investing in early-stage startups across AI infrastructure, computational science and climate tech. Market-relevant strengths include technical credibility, domain network and a thesis-driven approach to seed and series-A opportunities.
Veteran data scientist and entrepreneur focused on deep-tech and climate-related applications, specializing in scalable machine learning models for scientific and commercial use. Shirley Ho combines academic training in quantitative science with industry roles building productized AI systems, advising and investing in early-stage startups across AI infrastructure, computational science and climate tech. Market-relevant strengths include technical credibility, domain network and a thesis-driven approach to seed and series-A opportunities.
Investment Philosophy & Strategy
Thesis-driven investor concentrating on deep-tech and climate-tech startups where scalable machine learning and computational science create defensible product moats. Allocates seed to Series A capital with a bias toward teams that pair strong scientific rigor with go-to-market clarity; favors capital-efficient paths to meaningful scientific validation and early revenue signals. Investment style combines technical due diligence, active board/advisor involvement, and network-led deal sourcing. Time horizon is multi-year, with disciplined follow-on capital for conviction positions and emphasis on platform-level impact over short-term exits.
Thesis-driven investor concentrating on deep-tech and climate-tech startups where scalable machine learning and computational science create defensible product moats. Allocates seed to Series A capital with a bias toward teams that pair strong scientific rigor with go-to-market clarity; favors capital-efficient paths to meaningful scientific validation and early revenue signals. Investment style combines technical due diligence, active board/advisor involvement, and network-led deal sourcing. Time horizon is multi-year, with disciplined follow-on capital for conviction positions and emphasis on platform-level impact over short-term exits.
Disclaimer: This Portfolio is derived from and computed based on publicly available data from SEC filings.
Gain and growth charts are calculated per selected timeframe:
28.01%
Gain -0.69%
Monthly
0.85%
Yearly
10.73%
Drawdown
24.38%
$723.63M
Equity
Holdings
$723.63M
Investment
$614.74M
$131.24M
Profit -$5.22M
Realized
$22.35M
Unrealized
$108.89M
Dividends
$9.16M
Goals
Avg. 0% completed goals
Today
Compared To Yesterday
Gain
-0.69%
Profit
-$5.22M
Win %
100%
This Week
Compared To Last Week
Gain
-0.31%
Profit
-$2.26M
Win %
100%
This Month
Compared to last month
Gain
-0.31%
Profit
-$2.26M
Win %
100%
This Year
Compared to last year
Gain
10.59%
Profit
$69.27M
Win %
100%
Events
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Dividends
Dividend Analysis
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| Trades 136 | Longs Won 62/136 45% | Profit Factor 11.89 |
| Profitability | Shorts Won 0/0 0% | Standard Deviation $5.56M |
| Average Win $2.31M | Best Trade (Jun 02) $52.25M | Sharpe Ratio -5.14 |
| Average Loss -$162,801.35 | Worst Trade (Jun 29) -$1.01M | Z-Score -3.12 (100%) |
| Commissions $0 | Avg. Trade Length 7m 3w | Expectancy $965,016.22 |
| Loss Size | 100% | 90% | 80% | 70% | 60% | 50% | 40% | 30% | 20% | 10% |
| Probability of Loss | <0.01% | <0.01% | <0.01% | <0.01% | <0.01% | <0.01% | <0.01% | <0.01% | <0.01% | 0.87% |
| Consecutive Losing Trades | 4,444 | 4,000 | 3,556 | 3,111 | 2,667 | 2,222 | 1,778 | 1,333 | 889 | 444 |
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Returns
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Option values are updated at end of day.
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Option values are updated at end of day.
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