Data-driven
Every idea starts as a testable hypothesis on clean, point-in-time Indian market data, never a screenshot or a story.
Quant trading education · India
Numbers > emotions.
Learn to build, test and run rules-based trading systems on Indian markets with data, backtests and strict risk control, not tips or gut feel.
Education only. Not SEBI-registered. No buy/sell calls. Read the disclaimer.
# A system is a hypothesis, not a hunch rules = { "universe": "point-in-time, incl. delisted", "entry": "defined before seeing results", "exit": "stop + time + signal", "risk": 0.01, # per trade "costs": "brokerage + STT + slippage", } result = backtest(rules, in_sample="2010-2019") check = backtest(rules, out_sample="2020+") if robust(result, check, neighbours=True): paper_trade(rules) # then, maybe, small size else: discard(rules) # most ideas end here
Why Dhanyug
Trading is a process you can learn, test and improve.
Most retail traders lose money because of discretion, overconfidence and untested ideas. We teach the opposite: write the rules down, test them honestly, size positions to survive, and follow the process.
Rules-based
Entries, exits, position sizes and filters are written down before trading, so results can be measured and repeated.
Risk-first
Drawdown control comes before returns. We teach sizing, stops and regime filters that keep you in the game.
The research pipeline
From idea to system, honestly tested.
The same six-step workflow runs through every program. Most ideas fail at step three or four, and that is the point.
Hypothesis
State a clear, falsifiable market idea: momentum, trend, mean reversion, rotation.
Data
Point-in-time universes, corporate-action adjusted prices and delisted stocks, so survivorship bias doesn't flatter results.
Backtest
Realistic brokerage, taxes and slippage. No look-ahead. Split into in-sample and out-of-sample periods.
Robustness
Does it still work with neighbouring parameters, other periods and other universes? If not, it is curve-fit.
Risk design
Position sizing, max drawdown limits, volatility caps and market-regime filters.
Paper → live
Paper-trade, compare with the backtest, start small, and keep a journal of every deviation.
Programs
Structured training, from basics to building your own systems.
Quant Trading Foundations
Returns, risk, market structure and position sizing for Indian markets.
Backtesting with Python
Build a clean, bias-aware backtester in pandas, step by step.
Systematic Strategy Design
Momentum, trend following, rotation and regime filters, all tested.
Risk & Trading Process
Drawdown control, execution, journaling and discipline.
Our principles
What we will and won't do.
01
We teach, we don't tip
No stock tips, trading calls, entry/exit levels or targets. Ever.
02
No profit promises
No guaranteed returns, no "double your money", no cherry-picked screenshots.
03
Show the failures
We publish what didn't work, including drawdowns, losing periods and discarded ideas.
04
Costs are real
Every backtest includes brokerage, statutory charges, taxes and slippage.
05
Older data only
Our educational examples use historical data, not live or recent market setups.
06
Your decisions are yours
We build skills and judgment. What you trade, and how much, is your responsibility.
Risk warning: Trading in equities, futures and options involves substantial risk of loss. According to SEBI studies, 9 out of 10 individual traders in equity F&O incurred net losses. Backtested results are hypothetical and do not guarantee future performance. Dhanyug is not registered with SEBI as an Investment Adviser or Research Analyst. All content is for educational purposes only. Full disclaimer.
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