About ChartMath
We are a research firm that
ships a consumer product.
Retail trading runs on intuition because the alternative (statistical research, backtesting, validated edge) has always been institutional infrastructure. We built that infrastructure for retail. Every signal the app surfaces is the conclusion of a research run we ran for you.
Why now: a generation looking for financial agency.
Traditional paths to wealth are closing. Housing is 2x what it was. Wages have barely moved. AI is eating white-collar jobs. A generation is looking for financial agency, and they are finding it in trading apps. The numbers confirm it is not a blip. ChartMath is here to make that demand productive: to give this generation tools that match its ambition.
$5.4T
US retail trading volume
2025, +47% YoY
30M
new US brokerage accounts
opened in the last two years
75%
of retail trades on mobile
the desktop terminal era is over
0
AI-native research platforms
built for retail traders, until now
The unlock
Retail does not need to become an algorithmic trader.
Retail needs to become systematic.
Systematic means: rules you can follow. Evidence you can trust. Repeatable decisions that do not depend on your mood, your focus, or whether you happened to be watching the right ticker. Risk framed before you enter the trade, not calculated in panic during the loss. It is an identity shift, not a feature shift.
The series
Becoming systematic, in 3 essays.
A short curriculum for the identity shift: why your sample size is the actual problem, the four numbers behind every real edge, and what a systematic week looks like in practice. Each essay carries interactives — drag a slider, click a decision point, watch the math show up.
Before
“I think AAPL looks good.”
After
“AAPL matches a setup with a 72% historical win rate and a 2:1 reward:risk.”
The trader keeps the judgment. They still decide whether to take the trade. But they decide with evidence instead of emotion, and they decide inside a framework that improves every time they use it. From gut-driven to evidence-driven. From reactive to ready. From dependent on someone else's call to confident in their own.
Show me the math
The setup wins 72% of the time. The math is on your side, but only on average. The way you take that average bet decides whether the math ever plays out.
ONE leveraged trade
3x leverage on a single setup
One bad day = wiped out
Outcome is one coin flip
Same expected return
Chance of total ruin: meaningful
ONE HUNDRED systematic trades
1x size, every trigger the rules allow
Bad days get drowned in good days
Outcome is the average of one hundred
Same expected return
Chance of total ruin: near zero
The mistake retail makes is reaching for leverage to amplify a single bet. The unlock is the opposite: keep size small, take every trigger the rules allow, let the edge compound across many trades. You don't beat variance with leverage. You beat it with reps.
The research engine
AI agents propose. The backtester validates. The user gets the conclusion.
Every signal is an expensive research artifact: GPU-hours of backtest, months of internal validation, continuous re-testing against new market conditions. The user gets the conclusion. The cost of producing it is ours.
Read the full methodology →01
AI agents propose
Internal research agents generate candidate setups across price action, indicator confluence, and market regimes. Hundreds of hypotheses every cycle.
02
Backtester validates
Each candidate runs against years of historical data. Win rate, profit factor, expectancy, equity curve. Most candidates are rejected. The survivors get a track record.
03
User feed
A real-time push notification fires when a stock matches: chart, win rate, expected return, plain-English reasoning. Thirty minutes of manual research, compressed into a glance with evidence.
Who is behind this
Ankush Jindal
Co-founder, CEO
Computer science at IIT Mandi, third startup. Built data-heavy real-time systems since 2017; now applies the same discipline to retail trading research. Writes the Becoming Systematic series.
Prateek Rajdev
Co-founder, CTO
Computer science at IIT Mandi. An active trader who lives the problem; built the real-time scanning and backtesting engine that powers every signal in the app.
Rishik Reddy
Founding Member
Early team member. Helping shape the product and bring it to traders.
See what sets up on your watchlist this week.
All of this research ships inside the app, with a 14-day free trial. Add your tickers and the screens will flag your names the moment they set up, with the win rate attached.
Get the appWant to partner or get API access? We are open to data partnerships, distribution partnerships, and API access for serious builders. Write to ankush@chartmath.com.
ChartMath is built by SKAS Fintech Private Limited, a research company incorporated in Bangalore, India, in January 2026. We are not a broker, a dealer, or an investment adviser, and we never hold your money. Educational use only.