Former growth-stage investor and fintech operator focused on consumer platforms, payments and commerce. Taylor Faw combines deal sourcing and operator experience to scale revenue, partnerships and product-led growth across digital payments and merchant ecosystems. Active as an angel and advisor, typically engages in early-stage equity, commercial diligence and go-to-market execution for fintech and commerce startups.
Former growth-stage investor and fintech operator focused on consumer platforms, payments and commerce. Taylor Faw combines deal sourcing and operator experience to scale revenue, partnerships and product-led growth across digital payments and merchant ecosystems. Active as an angel and advisor, typically engages in early-stage equity, commercial diligence and go-to-market execution for fintech and commerce startups.
Combines operator experience and growth-stage investing to back early-stage fintech, consumer commerce and payments platforms with product-led revenue models. Prefers concentrated, high-conviction angel and seed positions where hands-on go-to-market, partnership development and commercial diligence accelerate adoption and monetization. Allocates small initial checks with follow-on reserves, prioritizing founders able to scale partnerships and merchant distribution. Time horizon is growth-oriented multi-year value creation, with disciplined risk selection focused on unit economics and network effects.
Combines operator experience and growth-stage investing to back early-stage fintech, consumer commerce and payments platforms with product-led revenue models. Prefers concentrated, high-conviction angel and seed positions where hands-on go-to-market, partnership development and commercial diligence accelerate adoption and monetization. Allocates small initial checks with follow-on reserves, prioritizing founders able to scale partnerships and merchant distribution. Time horizon is growth-oriented multi-year value creation, with disciplined risk selection focused on unit economics and network effects.
| Trades 456 | Longs Won 339/456 74% | Profit Factor 16.28 |
| Profitability | Shorts Won 0/0 0% | Standard Deviation $719,730.29 |
| Average Win $169,428.12 | Best Trade (Jul 15) $12.22M | Sharpe Ratio -11.68 |
| Average Loss -$30,148.03 | Worst Trade (Jul 16) -$613,578.66 | Z-Score 10.89 (100%) |
| Commissions $0 | Avg. Trade Length 8m 1w 3d | Expectancy $118,221.08 |
| 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% | <0.01% |
| Consecutive Losing Trades | 12,821 | 11,538 | 10,256 | 8,974 | 7,692 | 6,410 | 5,128 | 3,846 | 2,564 | 1,282 |