Quantitative Analysis with Python

1. Create a reproducible data contract

1.1 Provenance and schema

Begin with a manifest: provider, retrieval date, permission, universe, date range, currency, frequency and price convention. Save a read-only raw file and checksum, then create cleaned data separately. Record Python and library versions and any random seed. A notebook whose final cells depend on forgotten manual edits is not reproducible. Another learner should be able to run the pipeline from raw inputs to the same outputs.

1.2 Validate before plotting

In pandas, parse dates explicitly, sort by security and trading date, and test that the security-date key is unique. Inspect missing values, impossible prices and duplicate observations before joining datasets. A missing trading day may reflect a holiday, suspension or unavailable record; filling every gap with the last price invents zero returns. Preserve the reason for exclusion. PSX portal availability does not mean every desired historical adjusted CSV or point-in-time constituent file is provided.

Step-by-step method

1. Write a data dictionary and save the original download unchanged.
2. Validate unique keys, dates, numeric types and missing observations.
3. Log each cleaning decision and pin the analysis environment.

2. Construct a return series that matches the question

2.1 Corporate actions and distributions

For a simple price return, r[t]=P[t]/P[t−1]−1. A total-return calculation also includes relevant distributions under a stated reinvestment convention. Bonus issues, splits, rights and dividends require careful treatment; adjusted prices must be understood rather than adjusted a second time. Maintain a corporate-action ledger and check unusual jumps against actual disclosures. A halved unadjusted price after a two-for-one split is not automatically a fifty-percent economic loss.

2.2 Alignment and rolling calculations

On an appropriately prepared price Series, pct_change(fill_method=None) calculates fractional change; multiply by100 only for percentage display. Compute within each security so the last price of one company never becomes another’s denominator. Align dates before combining holdings or comparing benchmarks. A rolling standard deviation measures the selected observation window, not an immutable risk characteristic. State whether sample standard deviation uses ddof=1 and how incomplete windows are handled.

Step-by-step method

1. Choose price return or total return and document adjustments.
2. Calculate changes within security after chronological sorting.
3. Inspect extreme returns and verify corporate actions before removal.

3. Measure risk and test a limited hypothesis

3.1 Volatility and Sharpe assumptions

For n comparable observations, sample volatility is the standard deviation of returns. Annualising daily volatility as daily standard deviation times √N assumes the selected scaling is appropriate; N must be a disclosed trading-period convention, not a hidden universal constant. Serial dependence or changing volatility can weaken this shortcut. Sharpe measures mean excess return divided by its standard deviation using consistent frequency, currency and risk-free assumptions. It does not describe every tail risk.

3.2 Significance after many searches

Define the hypothesis before ranking results: for example, whether a specified sector signal predicts next-period returns after a fixed lag. Estimate uncertainty and investigate sample size, dependence and extreme observations. Testing hundreds of sectors, windows and thresholds makes a lucky winner likely. Report the search space, retain failed variants and use suitable multiple-testing controls or fresh validation data. A small p-value is not a probability that the strategy will make money.

Step-by-step method

1. Specify return frequency and matching risk-free inputs.
2. Publish the hypothesis and number of variants investigated.
3. Report uncertainty, drawdown and stability alongside average returns.

4. Simulate decisions available at the time

4.1 Leakage and changing universes

A fair backtest uses information only after it became available. Financial statements belong to their publication dates, not simply the fiscal period end. Signals computed from the closing price generally cannot assume a fill at that same close without a defensible order process. Lag the position and define the next permitted execution opportunity. Historical sector research needs historical constituents, delisted companies and classification changes; today’s membership creates survivorship bias.

4.2 Execution and costs

Subtract realistic commissions, spreads, applicable charges, taxes and liquidity-sensitive slippage under explicit assumptions. Model turnover and whether the intended order size could trade in observed volume. A backtest cannot assume a buyer, a permitted product or an unrestricted broker interface merely because code can express a trade. Distinguish research calculations from live execution permissions. Test pessimistic cost and delayed-fill cases before judging whether the observed effect remains economically meaningful.

Step-by-step method

1. Join fundamental data by actual availability date.
2. Lag signals and reconstruct the eligible historical universe.
3. Stress execution delays, turnover costs and capacity limits.

5. Validate, report and monitor without rewriting history

5.1 Walk-forward discipline

Separate development, validation and a final untouched test chronologically. Fit transformations and parameters using past training data only. In walk-forward research, roll the training window forward, freeze the rule for the next segment and concatenate genuinely unseen predictions. Repeatedly checking the final test while tuning turns it into training data. Compare the strategy with a simple benchmark using the same return convention and exposure constraints.

5.2 A research report someone can challenge

Deliver the manifest, scripts, environment file, cleaning log, parameter choices, gross and net results and failure cases. Chart cumulative wealth, drawdown and turnover rather than only the best period. State unresolved data limitations, such as incomplete historical sector membership. If the original task asks for the best sector over ten years but the required records are unavailable, narrow the conclusion instead of manufacturing completeness. Paper monitoring can then test whether future behaviour resembles the original assumptions.

Step-by-step method

1. Freeze the rule before opening the final test segment.
2. Compare net results with a relevant baseline and stress cases.
3. Publish limitations and set monitoring triggers for model review.

A small gross effect is sensitive to execution assumptions

Reference table

Glossary of key terms

Corporate-action adjustment

Treatment of distributions and capital changes so returns match a stated convention.

Data leakage

Use of information that was unavailable when the modeled decision occurred.

Drawdown

Decline from a previous portfolio value peak.

Out-of-sample

Data not used to choose or fit the tested rule.

Sharpe ratio

Mean excess return divided by its standard deviation on a consistent basis.

Slippage

Difference between a modeled reference price and achieved execution.

Survivorship bias

Distortion caused by excluding companies that later disappeared.

Turnover

Trading activity relative to portfolio size under a stated definition.

Frequently asked questions

Common pitfalls