Program structure
Seven phases, one integrated capstone.
Each phase produces artifacts that a working quant engineer would recognize: notebooks,
data checks, model reports, backtest results, risk dashboards, implementation reviews,
and final system documentation. Companion notes, short workshops, and phase review
interviews connect those artifacts back to real engineering judgment.
A personal interview is an important part of every phase. It gives the student time
to reflect on the work, ask questions that are specific to their own project, and
receive individualized feedback. It also lets the instructor hear the student's
reasoning directly and verify that the intended understanding and progress are
actually developing.
The required curriculum follows a direct path to a complete model. Material is
included when it advances a capability the model needs or protects the system from a
meaningful failure. Additional indicators, statistical variations, historical
context, and advanced techniques can remain available as optional depth without
distracting from the connected route.
The Certified Quant Engineer credential is awarded by Quant Institute for completed,
reviewed work. It is a professional certification, not a university degree or
government license.
Graduates receive a credential record that can be verified by prospective employers
through Quant Institute's secure verification process.
Phase 01
Markets and Instruments
Students learn how markets actually behave before they build systems around them:
their history, human behavior, time frames, asset classes, orders, liquidity,
spreads, volatility, market sessions, futures, equities, options, fixed income,
crypto, and execution constraints.
- Beginner recap of major exchanges and global market breadth
- Market purpose, persistent behavior, and participant time horizons
- Market structure and participant behavior
- Instrument mechanics and contract specifications
- Transaction costs, slippage, liquidity, and capacity
Phase 02
Data Engineering for Markets
The program treats data as a production responsibility. Students learn to ingest,
clean, validate, join, version, and audit financial datasets before they trust a
model built on top of them.
- Market data APIs, files, schemas, storage, and data contracts
- Missing values, corporate actions, timestamps, and survivorship bias
- Repeatable pipelines and dataset quality checks
Phase 03
Statistics as Market Evidence
Students learn the statistical backbone of quant systems: probability, distributions,
estimation, time series, regression, factor models, uncertainty, validation, and
what can go wrong when a model looks good too quickly. The objective is a
representative toolkit for building and challenging a model—not a survey of every
statistical procedure.
- Probability, conditional evidence, sampling, estimation, and empirical distributions
- Effect size, uncertainty intervals, dependence-aware resampling, and statistical power
- Time series behavior, stationarity, autocorrelation, volatility clustering, and regimes
- Regression diagnostics, stability analysis, multiple testing, and false discovery
- Expected value, tail behavior, economic materiality, and out-of-sample decisions
Phase 04
Signal Research and Technical Indicators
This phase separates chart decoration from evidence. Students study common technical
indicators, then learn why many are lagging confirmations, how leading features can
carry useful forward information, and why statistical profile tools are often missed
by retail traders. The purpose is not to collect indicator names. Representative
families are decomposed so students can identify the system job, information source,
redundancy, handoff, and failure mode.
Students then move from individual features to model architecture. A trend model is
one useful example: detecting a trend is only one responsibility. A complete design
may also need separate detectors for a dead zone, expansion, drift, and exhaustion
so that entry, suppression, position management, and exit preparation are governed
by the appropriate operating state. These are strategy-specific design choices, not
a required state vocabulary for every kind of model.
- Leading, lagging, and statistical profile indicators and their failure modes
- Why thousands of technical indicators are mostly different views of lagging data
- Indicators as sensing components inside state-aware decision architectures
- Trend-model example: dead zone, expansion, drift, and exhaustion as separate responsibilities
- Indicator adoption, feedback effects, candlesticks, model fit, regimes, and order flow
- Feature normalization, signal decay, turnover, redundancy, and regime sensitivity
- Cross-sectional and time-series signals, ensembles, and orthogonal evidence
- Optional build: automatic trendline anchoring from causal pivot and slope rules
- Lookahead bias, data snooping, false discovery, and validation discipline
Phase 05
Backtesting, Portfolio, and Risk Systems
Students connect signals to portfolio decisions. They learn how position sizing,
exposure, drawdown, turnover, constraints, stress testing, and risk monitoring
change the meaning of a strategy.
- Backtest engines, event-driven logic, costs, and realistic assumptions
- Portfolio construction, allocation, rebalancing, and constraints
- Risk dashboards, scenario analysis, drawdown, and kill-switch thinking
Phase 06
Forward-Only Stream Validation
Students move from historical validation into a controlled forward-only test. Their
system receives a simulated data stream or unseen dataset and must process each new
update without looking ahead. Running this test before the final phase gives
students evidence early enough to improve the model before final submission.
- Simulated live feed testing and unseen-data evaluation
- Output logging, decision traces, and risk-control behavior
- Anti-curve-fitting review and robustness confirmation
Phase 07
Production Quant Engineering
The final technical phase turns validated research into something maintainable:
tests, packaging, APIs, deployment, observability, documentation, review, and
handoff standards. Students can use what they learned in Phase 06 to adjust the
model, strengthen controls, and submit a cleaner final version.
- Version control, testing, logging, monitoring, and reproducibility
- Research-to-production workflow and model governance
- Documentation, implementation review, explainability, and operational controls