Value investing tool that optimally turns market dips into higher‑return opportunities
Hi everyone,
I’m a product manager working with a small fintech team in India, and I’d love to get feedback from this community on a tool we’re building before we scale it up.
Most of us invest in mutual funds through **simple SIPs** or occasional lump sums. SIPs are great because they’re automatic and take emotions out of the picture, but they also happily buy at **high NAVs and low NAVs** without any distinction. Over long periods, that’s fine – but when markets are choppy, there *are* windows where a slightly smarter entry strategy can meaningfully improve returns.
# What we’re building
We’re working on an analytics layer that sits on top of Indian mutual funds and does three things:
1. **Continuously tracks NAV behavior** for a universe of equity mutual funds (large, mid, small, flexi/multi).
2. **Generates “value signals”** when the model believes a fund’s NAV is in a favorable zone – basically, periods where the NAV is relatively “cheap” compared with its own history and risk profile.
3. **Translates those signals into simple, actionable guidance**, like:
* “Signal strength: 1 / 1.5 / 2 / 3 / 5 – multiply by your regular SIP amount and invest this as an extra tranche.”
* Allocation bands by category (e.g., small cap 10–30%, mid cap 20–40%, large cap 20–50%, flexi 0–40%) so people don’t go overboard in one bucket.
The core idea: **keep your normal SIPs**, but when strong signals show the NAV is bottoming out *relative to that fund’s own history*, deploy additional lump‑sum tranches instead of blindly averaging every month.
# How the methodology works (high level, no black magic)
Very rough summary, without dumping equations:
* We use past NAV data and derived features (drawdown, volatility, mean‑reversion signals, etc.) to estimate how attractive today’s NAV is compared to the fund’s own long‑term pattern.
* The model outputs a **score / probability band** which we convert into a discrete *signal level* (1, 1.5, 2, 3, 5).
* On top of that, there’s a **cash‑flow strategy**:
* A 6‑month (or similar) deployment window.
* “Quadrants” based on days left and un‑invested cash, which decide whether we stay conservative or accelerate deployment.
* NAV‑based rules – the deeper the NAV goes below your previous invested NAV / first NAV / minimum seen NAV for that period, the more extra we suggest allocating (within predefined caps).
Think of it as a rule‑based, explainable layer that sits between raw ML output and the final recommendation, so a normal investor can see *why* a given day is getting, say, a “3x” signal and not just “because the model said so”.
# What we’ve tested so far
We’ve been back‑testing and paper‑trading the signals on Indian equity funds. In our internal tests (across multiple years and several funds), the **signal‑based additional investments on “cheap” days** have shown potential to:
* Improve XIRR vs a plain “same amount every month” plan over the same total capital and period,
* Especially in markets that had multiple drawdowns / recoveries,
* While still keeping behavior closer to “investor‑friendly” SIPs than to short‑term trading.
Important disclaimer: we’re not promising guaranteed outperformance, and backtests are not the same as live results. Markets can and will do whatever they want.
# What the user experience looks like
Right now, the product is split between:
* A **web information layer** (explaining methodology, showing historical results, FAQs, and risk disclosures).
* A **web app** where an investor can:
* Choose one or more funds, set a budget and horizon.
* See a side‑by‑side graph:
* normal rupee‑cost averaging vs
* signal‑driven extra deployments.
* Receive **notifications** when new signals are generated, along with simple guidance like:
* “Mid‑cap Fund X – Signal 2 – Suggested extra ₹Y today (within 20–40% mid‑cap allocation band).”
We’re designing this so that **you remain in control** – the tool does not auto‑invest your money; it suggests amounts and timing, and you execute via your preferred broker/AMC if you agree.
# What I’m looking for from the community
I’d really appreciate feedback on a few things:
1. **Would this actually be useful to you?**
* Do you already do something like “invest extra on dips” manually?
* Would a systematic signal help you, or would you rather just stick to SIPs?
2. **What would you need to see before paying for it?**
3. Possibilities:
* Detailed back-test results for your specific funds.
* Live track record vs plain SIP starting from a public “launch date”.
* Transparency on methodology (even if code itself isn’t open‑sourced).
* Independent validation / third‑party audit.
4. **Pricing and format**
* One flat subscription for all funds?
* Per‑fund or per‑portfolio pricing?
* Would you prefer “freemium” (limited signals free, full access paid) or straight paid with a free trial?
5. **Red flags / concerns**
* Anything about this approach that makes you uncomfortable?
* What kind of risk disclaimers and education would you expect to see front and center?
We’re still early and actively iterating, so I’m not here to hard‑sell anything; I genuinely want to sanity‑check the concept and messaging with people who think critically about their investments.
If this sounds interesting, I’m happy to share more specifics about the design, show wireframes, or walk through a sample signal sequence for a popular fund (without giving advice on which fund to choose, obviously).
Thanks for reading this long post – any feedback, skepticism, or brutal criticism is welcome. It’ll help us build something that’s actually useful instead of just another shiny dashboard.