This interview is with Aman Anand, Co-Founder, Nvestiq.
Aman, as Co-Founder of Nvestiq building AI tools for trading-driven financial decisions, how do you describe your current focus and expertise to someone new to your work?
We believe the future of trading isn’t human vs. machine; it’s human amplified by machine. At Nvestiq, we build systems that translate intuition into algorithmic precision, giving traders clarity, speed, and confidence during their execution.
What experiences led you to co-found Nvestiq and specialize in AI-driven trading and cash-flow visibility for non-quants and real-world operators?
Before co-founding Nvestiq, I spent years observing a consistent gap in financial decision-making. On one side, you had sophisticated quantitative systems built for institutions. On the other hand, you had capable operators (founders, traders, business owners) making high-stakes decisions without access to structured, real-time intelligence.
I saw firsthand how market complexity and cash-flow uncertainty create friction for non-quants. Many rely heavily on intuition, which is valuable, but often unsupported by systematic data tools. That disconnect led me to focus on AI as a bridge-building system that translates complex financial signals into decision-ready insights.
Co-founding Nvestiq was about solving that problem directly: giving real-world operators the clarity, discipline, and cash-flow visibility that institutional players take for granted, of course, without requiring them to be quants themselves.
From your early deployments, what specific pain point in trading automation did you prioritize solving first at Nvestiq, and why did that matter most to users?
In our early deployments at Nvestiq, the first pain point we prioritized was decision inconsistency under pressure. Most traders did not lack ideas. What they struggled with was structured execution. Emotions, market noise, and fragmented data often led to overtrading, delayed exits, and unmanaged risk exposure.
We recognized that the core issue was not just signal generation, but translating conviction into disciplined, repeatable action. So we focused on building systems that enforce structured trade planning, predefined risk parameters, and real-time visibility into cash flow and exposure.
This mattered most to users because it directly protected capital. Before optimizing for alpha, they needed clarity, consistency, and downside control. Once execution discipline improved, performance naturally followed.
When you design or evaluate backtesting algorithms, what real-world criteria do you insist on to trust results, and which pitfalls have burned you before?
I prioritize robustness over optimization. The strategy has to hold up across market regimes and out-of-sample data, and performance should not collapse with small parameter changes. Stability matters more than peak returns.
I have been burned by overfitting and overly optimistic execution assumptions before. Those experiences taught me to design backtests conservatively, because durability in live markets is what ultimately counts.
For leaders without a quant background, what no-code or low-code workflow have you seen work best to stand up a first AI-powered trading strategy from idea to backtest?
For leaders without a quant background, the most effective path is a structured no-code workflow that separates idea, validation, and execution. At Nvestiq, we’ve seen this approach work consistently:
- Start with a simple hypothesis. Define one clear edge, such as trend continuation, mean reversion, or earnings drift. Avoid stacking indicators early.
- Use a visual strategy builder. Platforms like TradingView (with strategy templates) and QuantConnect (a low-code framework) let users convert rules into structured logic without building infrastructure from scratch.
- Backtest with realistic constraints. Apply transaction costs, slippage, and position sizing rules. Focus on drawdowns and consistency, not just total return.
- Forward test in paper trading. Run the strategy live without capital for several weeks to observe behavior in real conditions.
- Automate execution only after validation. Once performance is stable and risk is defined, connect to a broker API or automation layer.
The key is discipline over complexity. Most first strategies fail because they are over-engineered. The ones that succeed start simple, validate rigorously, and scale gradually.
Once a minimal viable backtest exists, how do you operationalize data hygiene to prevent leakage and handle regime shifts in day-to-day practice?
Once a minimal viable backtest exists at Nvestiq, operationalizing data hygiene becomes a daily discipline, not a one-time fix.
First, we strictly separate in-sample, out-of-sample, and forward-testing data. No parameter tuning is allowed on validation or live periods. We also enforce point-in-time data integrity, ensuring that every feature reflects only what would have been known at that exact timestamp. This eliminates lookahead bias and hidden leakage.
Second, we implement rolling retraining and walk-forward testing. Markets shift, so instead of assuming stationarity, we evaluate performance across sliding windows to detect decay early. If edge deterioration crosses predefined thresholds, the strategy is paused or revalidated.
Third, we monitor live versus expected behavior. Slippage, fill quality, volatility sensitivity, and drawdown shape are tracked against backtest assumptions. Deviations trigger a review before capital is scaled.
Leakage and regime shifts are not theoretical risks; they are operational risks. Treating them as ongoing monitoring problems rather than academic concerns is what keeps strategies durable in real markets.
Before moving from paper to live trading, what risk controls and monitoring do you consider non-negotiable based on your experience?
Before moving from paper to live trading at Nvestiq, a few risk controls are absolutely non-negotiable.
First is hard risk caps. Every strategy must have predefined maximum position size, maximum daily loss, and maximum portfolio drawdown limits enforced at the system level, not just assumed in logic.
Second is real-time exposure monitoring. We track net exposure, leverage, correlation clustering, and concentration risk continuously. Strategies rarely fail in isolation; they fail when correlated risks stack quietly.
Third is kill-switch infrastructure. If slippage spikes, execution deviates materially from the model, or volatility exceeds tested bounds, trading automatically pauses.
Fourth is capital staging. We scale gradually, starting with a small allocation and increasing only after live performance matches forward-tested expectations.
Paper trading proves logic. Live trading tests psychology, liquidity, and execution reality. The controls exist to protect capital while those real-world variables reveal themselves.
How do you structure an experimentation cadence—hypothesis, backtest, walk-forward, and post-trade review—to keep strategies adaptive without overfitting?
We structure experimentation as a disciplined cycle to balance adaptation and robustness.
1. Hypothesis first. Every experiment starts with a single, testable idea— a market inefficiency or edge. Complexity is added only after the base hypothesis proves meaningful.
2. Backtest rigorously. We validate against in-sample and out-of-sample data, using realistic transaction costs, slippage, and position sizing. Curve-fitting is actively avoided by testing multiple parameter sets and rolling windows.
3. Walk-forward testing. We simulate forward periods in a rolling manner to mimic real-world deployment. This shows whether a strategy retains an edge under evolving market conditions and highlights regime shifts early.
4. Post-trade review. Live or paper-trade outcomes are reviewed quantitatively and qualitatively. We check drawdowns, slippage, volatility sensitivity, and deviations from expected behavior. Lessons feed back into the next hypothesis.
By cycling through this cadence, strategies remain adaptive without chasing noise, and each iteration is grounded in measurable, operational reality rather than backtest perfection.
When helping a small team adopt AI trading, what early signals of ROI and team readiness do you look for in the first 60–90 days to decide whether to scale?
When helping a small team adopt AI trading, the first 60–90 days focus on measurable signals rather than promises.
Early ROI signals include:
- consistent adherence to defined execution rules in paper trading,
- reduced decision errors, and
- preliminary performance metrics that align with backtested expectations.
Even small improvements in trade discipline or cash-flow visibility count as positive signals.
Team readiness is equally critical. We look for:
- engagement with the workflow,
- the ability to interpret AI insights,
- providing qualitative feedback, and
- following risk controls.
Teams that embrace structured experimentation and consistently report discrepancies between the model and reality are ready to scale.
Scaling only happens when both ROI potential and team discipline are visible in practice. Without both, adding capital too early usually magnifies mistakes rather than performance.
Thanks for sharing your knowledge and expertise. Is there anything else you'd like to add?
I’d just add that adopting AI in trading is as much about mindset as it is about technology. Success comes from disciplined experimentation, respecting market realities, and treating AI as a tool that amplifies human judgment rather than replaces it. Teams that embrace structured workflows, rigorous risk controls, and continuous learning tend to extract the most value.
Ultimately, the goal isn’t just better strategies—it’s smarter decision-making that scales safely over time.