Why saral.money exists
India has the largest underserved retail equity market in the world, and SEBI has documented — with its own data — exactly how it goes wrong. Seven reasons, each anchored to a source, and what we actually do about it.
Nine in ten lose. Now SEBI is closing the casino.
SEBI data shows 93% of individual F&O traders lost money over FY22–FY24. In response, SEBI's November 2024 clampdown is actively pricing retail out of F&O. Systematic cash equities are no longer just a smarter choice; they are becoming the only accessible one.
SEBI has now run the study three times, and the answer is always the same: the overwhelming majority of individual derivatives traders lose money. The aggregate losses are measured in lakhs of crores, driven by well-documented behavioural traps like the disposition effect and overtrading.
In response, SEBI effectively closed the cheap tables. By tripling minimum contract sizes to ₹15–20 lakh and slashing weekly expiries, retail capital is being pushed out. saral.money provides the bridge: transitioning traders from impulsive F&O bets to disciplined, quantitative equity portfolios.
Read: Why 93% of F&O traders lose — and what changes the oddsYou are trading against machines. Now you can build your own.
In FY24, 96% of proprietary-trader profits and 97% of foreign-investor profits came from algorithms — while only 13% of individual traders used them. Retail brings manual decisions to an automated fight.
Algorithmic trading crossed 53% of NSE cash-market turnover in 2024, overtaking manual trading for the first time. The counterparty on the other side of a retail trade is, more often than not, an automated system designed to harvest liquidity.
saral.money gives retail the same primitive the other side uses: a strategy defined as rules, validated on history, and executed without flinching. The edge here is not microsecond latency, but systematic factor exposure and behavioural discipline over months and years.
Read: Retail vs the machines — who is on the other side of your tradeSEBI opened retail algo trading in Feb 2025. Walk through the door correctly.
For 13 years, algorithmic access was effectively institutions-only. SEBI’s February 2025 framework is the first to give retail a sanctioned path — through broker APIs, with rule-disclosed "white-box" strategies favoured.
From 2012 to 2024, algorithmic execution was kept out of retail hands. SEBI’s February 2025 circular reverses that, explicitly favouring white-box algorithms (where logic is disclosed and built by the user) over opaque black-box systems that require heavy compliance.
A visual strategy graph where every rule node is visible is white-box by construction. saral.money maps directly onto this lightest-compliance path, routing through approved broker APIs while staying well below the high-frequency order limits.
Read: What SEBI’s Feb 2025 retail algo framework changes for youA backtest is only as honest as its data.
Most retail "backtests" run on today’s index projected backwards — silently deleting every stock that went to zero. Combined with look-ahead bias and ignored slippage, a real historical return turns into a fantasy.
Run a backtest of the "current Nifty 500" over 2010–2024, and DHFL, RCOM, and JETAIRWAYS never appear. Add "close-to-close" prices that ignore slippage, and the equity curve climbs for reasons that have nothing to do with genuine edge.
saral.money runs on a point-in-time universe. The index is reconstructed as it existed on each rebalance date, including delisted names. Corporate actions are applied correctly, and real-world frictions are modelled. The years a strategy underperforms — like 2018 or March 2020 — stay in the dataset where they belong.
Read: How to backtest properly on Indian marketsNo-code should not mean no-power.
Incumbent no-code tools force you to trade power for simplicity. saral.money refuses that trade, putting institutional machinery — cross-sectional ranking and multi-factor pipelines — directly in your hands.
Institutional strategies require screening a universe, ranking cross-sectionally by a factor, sizing positions, and evaluating the pipeline over decades. That capability used to live strictly behind Python scripts and Bloomberg terminals. Other retail platforms trade this power away for simple, single-stock signals or multi-leg options.
As SEBI squeezes retail out of options, the workflow a long-horizon investor actually needs — ranking the Nifty 500 by momentum, holding the top 15, and rebalancing monthly — requires a proper portfolio engine. saral.money is built exactly for this systematic equity reality.
Read: The Indian algo-tooling gap — what the incumbents can’t doBuilt for Bharat, not just for coders.
Code-first tools assume you program in English. Yet only ~11% of Indians report speaking English, and coders are a tiny fraction of the investor base. Systematic investing cannot require Python.
The tooling that serves US retail assumes Python or Pine Script. In India, with 130 million+ investors but only ~3 million developers, requiring code locks out over 97% of the market. Furthermore, 60% of new demat accounts come from beyond the metros where English proficiency drops sharply.
saral.money is visual-first by design. A peer-reviewed HCI consensus shows that block-based environments drastically reduce syntax friction for novices. The trader who understands momentum perfectly well is no longer blocked by a missing semicolon.
Read: Why algorithmic investing in India has to be no-codeDescribe the idea. Get a strategy you can actually edit.
Knowing markets is not the same as knowing how to build a pipeline. saral.money’s AI assistant turns a plain-language thesis into an editable, white-box strategy flow — a scaffold you control.
The blank canvas is where most systematic investors stall. "Buy quality companies when momentum turns up" is a clear idea, but translating it into filters, ranking rules, and rebalance schedules is a separate skill.
The AI assistant does the scaffolding. You describe the thesis, and it returns a visual pipeline of editable rule nodes. This is deliberately not a black-box model that trades for you. It lowers the authoring barrier while keeping the rules inspectable and compliant.
Read: From a plain-English thesis to an editable strategySee it for yourself
Build a strategy as a visual pipeline and backtest it against 15+ years of survivorship-bias-free NSE/BSE data.
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