Technical & Price Action
1-Year Return Ranking (Top Performers) is a Technical & Price Action screen. It checks every NSE-listed stock against 1 condition computed from nightly data, after excluding heavily pledged promoters and very small market caps. This page explains the rule, each figure and its source. It lists no stocks and is not advice.
Price momentum — the tendency of recent outperformers to continue outperforming — is one of the most durable anomalies in empirical finance, documented across geographies, asset classes, and time periods. This screen ranks the entire NSE universe by 1-year price return in descending order, providing a pure performance leaderboard for discovery and watchlist purposes. It is distinct from the 12-1 month momentum factor used in the quant composite: that factor normalizes returns, adjusts for volatility, and combines with other signals; this is a raw ranking for human interpretation. Market participants who want to know which stocks the market has rewarded most over the past year — whether for fundamental, technical, or thematic reasons — use this screen as a starting point for deeper investigation.
A stock must satisfy every condition below. Conditions are grouped under the pillar they test.
1-Year Price Performance
Hard excludes
Ranked by 1-Year Return, highest first.
1-Year Price Return (%)
Percentage price return over the trailing 52 weeks (approximately 252 trading days), computed as (current_price / price_52wk_ago - 1) * 100 using closing prices from daily OHLCV data.
High is better
Current Price (₹)
Latest available closing price of the stock in Indian Rupees, sourced from daily OHLCV data.
High is better
Price 52 Weeks Ago (₹)
Closing price of the stock approximately 52 weeks (one calendar year) prior to the current date, sourced from daily OHLCV data.
The base price used in the return calculation. Displaying this metric allows users to verify the return computation and understand where the stock was trading one year ago. It is particularly useful for identifying whether recent price levels represent meaningful absolute appreciation or a recovery from an anomalous trough.
High is better
Type hypothetical values to see which conditions they satisfy. These are not real stocks and nothing here is stored.
Enter values to begin
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In the past data we checked, the stocks this screen picked did better than the ones it left out, but a cost-aware, out-of-sample portfolio built from them did not hold up well against our deflated-Sharpe bar.
In the past data we checked, the stocks this screen picked did better than the ones it left out, but a portfolio built from them did not clear our test after trading costs. Each month, across the whole period we have data for, we checked whether the stocks this screen picked did better, over the following month (21 trading days), than the stocks it didn't pick. We score that from -1 (the picks always did worse) to +1 (always did better); 0 means no pattern. This screen scored 0.0996. As a measurement, the score is moderate, but clearly above zero. If the screen had no real edge, a score at least this far from zero would show up less than 1 in 1,000 by luck alone.
We also tested a portfolio of the screen's picks over 64 months, using only what was known at the time and after estimated trading costs: it earned 0.24 for each unit of ups and downs, per year. Because this screen has been tested 2 times, we mark down results that could be flukes. After that, the chance that it truly beats simply holding the index is 0.204, about 20 in 100.
A pattern can exist and still not be worth trading after costs. This is a record of the past, not a prediction. A screen is still a way to narrow a list by your own rules, whatever the measurement says.
Not yet testable — 5 of 6 points needed to chart a rolling information coefficient.
This is a historical, statistical measurement produced by FINOME TECHNOLOGIES PVT LTD's research harness on past data. It is not a prediction, and past results do not indicate future results. The universe is today's Nifty 500 constituents, so the sample is affected by survivorship bias. It describes how a screen's selections behaved in the past and does not constitute investment advice or a recommendation.
Rule version 1.0.0