Quant research has a face for every stage of the process. The expressions below form a complete research lifecycle: conviction, discovery, doubt, cleaning, robustness, production, and finally risk.

A good meme is a compressed post-mortem. One face can contain several months of preventable work.

ᕙ(⇀‸↼‶)ᕗ Serious derivation

The idea is still mostly algebra, intuition, and stubbornness. Nothing has been backtested, so the expected Sharpe ratio remains unconstrained by reality.

ᕦ(ò_óˇ)ᕤ High conviction

The economic story is strong, the mechanism sounds inevitable, and the researcher has begun saying “structural” before opening the data.

Conviction is useful for starting a project. It should not determine which evidence is allowed to finish it.

ᕕ( ಠ‿ಠ)ᕗ Found a signal

The first backtest works. The equity curve moves from the lower left to the upper right. For several minutes, selection bias, transaction costs, and alternative specifications do not exist.

ᕙ( •̀ ᗜ •́ )ᕗ The result looks good

The signal survives a second sample and the researcher permits one small celebration. This is the ideal moment to save the original specification—before the robustness garden begins to grow.

σ(￣、￣〃) What is the mechanism?

Does the return compensate risk, capture a behavioural error, proxy for market structure, or merely rediscover a familiar exposure? A result becomes research only when the explanation can make predictions beyond the original chart.

(￢_￢) ｡o○ The market story sounds too neat

Every clean narrative should meet a sceptical face. Commodity inventories, financing conditions, policy, positioning, and market access rarely line up as neatly as the final paragraph suggests.

( ˘•ω•˘ ).｡oஇ Staring at the residuals

The factor works, but something remains in the errors: a calendar pattern, a volatility cluster, a country exposure, or one extraordinary year doing most of the work.

Residuals are where elegant stories go to become more specific.

(・_・ヾ Why did the sign flip?

Change the lag, sample, rebalance rule, or neutralisation scheme and the coefficient changes sign. This may reveal a conditional mechanism. It may also reveal that the original estimate was mostly noise.

( •́ _ •̀)? Is this price comparable?

LME copper, SHFE copper, a continuous futures series, and a copper-miner basket are related—not interchangeable. Currency, location, grade, tax, funding, rolls, and market hours all sit inside the word price.

(。-`ω´-) Calculation mode

Now come the returns, lags, ranks, z-scores, winsorisation rules, covariance estimates, and one spreadsheet opened only to check whether the code is wrong.

The calculation is rarely the hard part. The hard part is remembering which choices were made after seeing the result.

(⊙_◎) The data moved

Yesterday’s history no longer matches today’s download. A vendor revised a field, adjusted a contract, changed a constituent, or repaired an error without leaving a note.

A reproducible backtest begins with a versioned data extract, not just versioned code.

ლ(ಠ_ಠ ლ) Why, market?

The signal predicted correctly and still lost money. Timing, implementation, crowded positioning, convexity, or an unmodelled basis moved faster than the thesis.

Being directionally right is not the same as owning the right instrument at the right time.

┐(￣ヘ￣)┌ The model cannot explain it

“Residual,” “idiosyncratic,” and “regime change” are sometimes technical descriptions. They are also excellent places to hide surprise.

A model should make clear what it does not know before the unexplained event arrives.

乁( •_• )ㄏ Risk is unknowable

Some uncertainty cannot be estimated from the available history. That does not remove the need to choose exposure; it changes the question from “What is the exact risk?” to “What loss can the strategy survive?”

┬─┬ノ( º _ ºノ) Clean the data table

Before the dramatic conclusion: units, duplicates, stale prices, exchange calendars, corporate actions, contract rolls, survivorship, and missing values.

Most quant research is less like discovering a theorem and more like carefully putting the table back upright.

(╯°□°)╯︵ ┻━┻ The factor suddenly stopped working

The historical line is compelling until it reaches the word now. Perhaps the factor was arbitraged, the mechanism weakened, implementation changed, or the original estimate was lucky.

“Factor decay” describes the picture. It does not yet identify the cause.

༼ つ ◕_◕ ༽つ Give alpha

After data cleaning, neutralisation, costs, constraints, and risk controls, the researcher presents the remaining signal to the market with both hands.

The market is under no obligation to accept the offering.

٩(•̤̀ᵕ•̤́๑)ᵒᵏ Robustness passed

The result survives different periods, reasonable parameter choices, nearby universes, realistic costs, and a test designed to make the thesis fail.

Passing robustness does not prove a strategy is true. It earns the right to continue.

ヘ(°◇、°)ノ Commodity shock

A mine closes, weather changes, freight dislocates, sanctions arrive, or an exchange changes a rule. The shock travels through physical supply, futures curves, currencies, margins, and equities at different speeds.

This is where “one commodity, several prices” stops being an abstraction.

╭( ๐_๐)╮ Risk arrived

Production adds prices that move during execution, turnover that was cheap only in a spreadsheet, limits, capacity, failed fills, and other people trading the same idea.

Production is not a noisier backtest. It is a different measurement system.

The complete quant researcher moves through all twenty faces—ideally before the portfolio reaches the sixteenth.