Trade your own thesis.
Backed by the desk's research.
Sarvy is a student-run research desk tracking NSE & BSE — Nifty, Bank Nifty, Sensex, and the sectors underneath. Members publish their thinking, the desk stress-tests it, everyone trades their own account.
A shared research log for the Indian market.
Every member brings their own edge — a support/resistance rule, a half-built factor model, a habit of reading concalls closely. It gets written down, argued with, and logged before anyone trades on it.
Four steps, in this order, every time.
Nothing gets logged until it's been through review, and no thesis is a signal to copy.
Research
A member drafts a thesis on a stock, sector, or index — chart, data, and the level at which they'd be wrong.
Review
The desk pokes holes in it — weekly sit-down and an open thread, nothing passes unchallenged.
Log it
Reviewed notes go into the shared ledger with entry logic and invalidation marked.
Trade independently
Each member decides for themselves — own account, own size, own risk.
Some entries are a chart. Others are a backtest.
Members comfortable with Python push their theses further — screening the Nifty universe, backtesting a rule before it's logged.
import pandas as pd import numpy as np import yfinance as yf # mean-reversion screen, Nifty 50 name df = yf.download("RELIANCE.NS", period="2y", interval="1d") df["ret"] = df["Close"].pct_change() roll = df["Close"].rolling(20) df["zscore"] = (df["Close"] - roll.mean()) / roll.std() # fade extreme moves back to the mean df["signal"] = np.where(df["zscore"] > 1.5, -1, np.where(df["zscore"] < -1.5, 1, 0)) strat_ret = df["ret"] * df["signal"].shift(1) sharpe = strat_ret.mean() / strat_ret.std() * np.sqrt(252) print(f"Annualised Sharpe: {sharpe:.2f}")
A 20-day z-score mean-reversion screen on a Nifty 50 name — nothing exotic, but it's the kind of check the desk expects before a "range-bound" thesis gets logged: does the number back up the chart.
Every skill level, one shared ledger.
You don't need a finance degree — you need a method you're willing to write down and defend.
The Screener
Support, resistance, volume on Nifty names. Learning to write a thesis before a trade, not after.
The Quant-in-training
Backtests and factor screens in Python across the Nifty 500. Brings numbers, not certainty.
The Macro Reader
RBI policy, crude, FII/DII flows — ties single-stock ideas back to the bigger picture.
The Options Reader
Bank Nifty and Nifty weekly OI data for timing. Keeps the desk honest on entries and exits.
Sarvy is an independent student community, not a SEBI-registered advisor. Notes are shared for discussion; every member trades their own capital, at their own risk.
Bring a method, not a track record.
The desk runs on people who'll write their thinking down and let it get argued with.
- Send a short note on how you currently research or trade — even one indicator or a gut rule you're formalising.
- Sit in on one review session as a guest, no commitment required.
- Publish your first entry to the shared log within your first two weeks.
- Keep trading your own account — the desk reviews the thinking, never the money.
Tell us where your research currently stands. We'll follow up by email.