A 500-chapter, desk-readiness program in cross-asset trading and risk management, taught end-to-end on an integrated platform, from first principles to a signed-off reserve. Instruments span every asset class - rates, FX, credit, equity, commodity, power, inflation and digital assets - mirroring the breadth of a tier-one platform.
The Enterprise Trading & Risk Masterclass is a complete, desk-readiness curriculum in cross-asset trading and risk management. It assumes no prior background and builds every concept from first principles to the point where a learner can sit on a desk, in a middle office, or on an implementation team and be useful on day one. The program is taught on an integrated trading and risk platform used as the single reference system throughout, so learners see every concept where it actually lives in a product, not as abstract theory.
Coverage spans the full institutional value chain: deal capture and trade lifecycle, static and market data, pricing and valuation, market and counterparty risk, XVA, collateral and margin, settlement and post-trade, accounting, regulatory reporting, integration, administration, and advanced projects. That is the same journey a trade takes through a real institution, from the moment a salesperson agrees a price to the moment the resulting reserve posts to the general ledger. Most programs teach these as disconnected topics. This one teaches them as one continuous system, because that is how they behave in practice: a decision in trade capture changes the number in the risk report, and a flag in a collateral agreement changes the reserve that hits the balance sheet.
The result is a program that produces ability rather than recall. You will not simply learn that CVA exists; you will configure the exposure simulation that produces it, query the datamart to reconcile it, and defend your assumptions to a model-validation challenge. That is the difference between someone who has read about a trading platform and someone an institution can put to work.
There is a reason the program is structured this way rather than as a sequence of lectures. Trading and risk is not a body of facts to be memorised; it is a system of interlocking decisions where the consequences of each choice surface somewhere far downstream. A booking convention chosen in trade capture determines whether a settlement instruction is correct three modules later. A curve-construction choice made for discounting changes the mark-to-market that feeds the risk report that drives the hedge. A single flag on a collateral agreement changes the reserve that hits the balance sheet and the capital the firm must hold. You cannot learn this as isolated topics, because the whole point is the connections. The masterclass therefore teaches the platform as one continuous system and follows real trades through it, so the connections are visible rather than asserted.
It is also honest about the level it pitches at. Because it assumes no prior background, a capable person moving in from an adjacent field - a software engineer, a data analyst, a graduate quant, a consultant - can start at chapter one and build up. But it does not stay shallow. By the exemplar chapters it is teaching the same material a practitioner is expected to defend in front of a regulator. The ramp is gradual and the ceiling is high, which is exactly what desk-readiness requires: you are useful early and you keep getting more capable as the program deepens.
The curriculum is organised into twelve modules that follow a trade from front office to finance. Each module is a coherent block of the platform; together they are the whole institution.
Module 1 grounds you in the markets themselves - what capital markets are, how instruments and derivatives work, and why an integrated platform exists at all. Modules 2 through 5 build the data spine every downstream number depends on: the platform architecture, static and reference data, market data and curve construction, and trade capture and booking. Get these wrong and everything after them is quietly wrong too, which is exactly why the program spends real time here rather than rushing to the glamorous topics.
Modules 6 and 7 are the analytical heart: pricing and valuation, then risk and analytics - VaR, credit exposure, XVA and the machinery behind them. Modules 8 and 9 handle what happens after the trade: settlement, post-trade processing, collateral and margin. Modules 10 and 11 are the plumbing and operations that make a real platform run - integrations, interfaces, administration, monitoring, disaster recovery. Module 12 is advanced topics and capstone projects, where the pieces come together into build-and-defend work.
Instruments are not confined to one corner of the market. Across the program you work with rates, FX, credit, equity, commodity, power, inflation and digital assets - the breadth of a tier-one platform - so the concepts generalise rather than staying trapped in a single asset class. A netting-set idea learned on a commodity swap applies directly to a rates swaption; a curve-construction technique learned for discounting applies to a credit hazard curve. The program is deliberately built so that skills transfer.
Consistency is what turns a large curriculum into muscle memory. Every one of the 500 chapters follows the same eight-part structure, designed to produce ability rather than recall. By the time you finish, you will have walked this exact path hundreds of times, and the platform - and the reasoning a desk uses - will be second nature.
Platform entry. Every chapter opens the way a working day does: you sign in, choose your workspace, and preview the end-to-end screen flow for the task. You are never handed a concept in the abstract without first seeing where it lives in the product. Screen-by-screen walkthrough. Then you walk the actual click-path through the platform for whatever the chapter covers, with a mock screen at every step, so the interface becomes familiar before the theory gets hard.
Theory from first principles. Only then do the concepts, formulae and market context arrive - assuming no background, building up rather than dropping you in the deep end. Working code artifacts. Each chapter includes real, inspectable technology: SQL, ORE/QuantLib-style XML, FpML and REST that you can read, run and check into version control. This is the difference between knowing what a valuation is and being able to configure and audit one.
Day-on-the-Desk incident. Every chapter includes a real trading-incident investigation drawn from that chapter's own failure modes - the number that looks wrong at 08:40 and must be explained before the risk committee at 09:30. End-to-end case study. One of eight recurring golden trades is followed across every function, so the same deal accumulates meaning as it moves through the institution. Regulator & model-validation perspective. You see what audit, model risk management and the regulator will challenge, and what a weak answer looks like. Interview preparation and capstone. Role-specific question banks with strong answers and the red flags that expose weak ones, then a build-and-defend project graded on artifacts, not recall.
To show the standard every chapter is built to, consider one of the most complex topics in the program: XVA, the family of valuation adjustments that turns a model price into a number a bank can actually reserve against. The full chapter walks the complete workflow end to end, and it is the exemplar included in the downloadable prospectus.
The chapter begins where every chapter begins - by getting into the platform. You sign in, choose your workspace (a multi-tenant platform scopes every screen, trade and number to the desk you select), and preview the twelve-stop screen flow you are about to walk. That flow is the spine of the chapter:
Here is the platform sign-in itself - the same door every chapter opens - with single sign-on and access scoped to the desks your role entitles you to:
Once inside, the chapter answers the question that motivates all of XVA: why is the price not the price? Two traders can book the identical five-year commodity swap on the same day at the same strike, and their screens can show an identical mark-to-market of, say, +USD 412,000 - yet the true economic value of those two trades to the bank can differ by tens of thousands of dollars. One is with a strongly rated counterparty under a tight collateral agreement; the other is with a weak, uncollateralised name in a region with a poor legal opinion on netting. The textbook price treats them as equal. Reality does not. The gap - once you account for the possibility that your counterparty defaults, that you must fund the position, that you post and receive collateral, and that regulators make you hold capital against the exposure - is the family of valuation adjustments, collectively XVA.
Everything in the chapter rolls up into a single cockpit view. Clean mark-to-market enters from the left; each adjustment (CVA, DVA, FVA, ColVA, KVA) is a step in a waterfall that walks the base price down or up to the adjusted, reservable value:
Read the waterfall left to right. The base mark-to-market is the risk-free price the pricing engine produces. CVA is a charge - we lose money if the counterparty defaults while they owe us - so it steps the value down. DVA is a benefit. FVA reflects the cost of funding the uncollateralised portion of the position. ColVA captures the economics of the collateral we actually post and receive. KVA is the lifetime cost of the regulatory capital the trade consumes. The rightmost bar is what the trade is genuinely worth to the institution. Every number on that screen is derived from the exposure simulation and curve calibration the chapter builds over the following pages.
You do not compute CVA trade by trade. You compute it per netting set - the pool of trades with a single counterparty that can be legally set off against each other on default. If one trade is worth +10 and another -6 with the same counterparty under an enforceable master agreement, your exposure on their default is +4, not +10. Netting is the single largest reducer of counterparty exposure in a real book, and getting the netting-set boundaries right is the difference between a defensible number and a regulatory finding. Layered on top is the Credit Support Annex, the collateral agreement whose threshold, minimum transfer amount, margin frequency and - critically - netting-enforceability flag all move the final number:
That netting-enforceability flag is a genuine trap the chapter teaches through its Day-on-the-Desk incident. A trade will happily book against a netting set even when the legal opinion does not support netting in that jurisdiction. If the flag is set optimistically, the engine computes netted exposure that legally should be gross - and a surprising number of overstated netting-benefit findings trace back to that one boolean. You learn to verify it against the legal-opinion reference before you trust any netted CVA.
Every XVA number rests on one thing: a distribution of what each netting set will be worth at many points in the future, under many scenarios. This is the exposure simulation, by far the heaviest computation in the whole system. You choose risk-factor models, generate thousands of Monte Carlo paths, reprice every trade on every path at every future date, net and collateralise, then aggregate into the expected-exposure and potential-future-exposure profiles that feed CVA, limits and capital:
The near-term bump in that profile is the seasonality of the commodity leg; the long decay is amortisation and mean reversion. CVA integrates the expected-positive-exposure curve against the counterparty's default probability; limits and regulatory capital lean on the potential-future-exposure envelope. The chapter then teaches you to turn credit spreads into default probabilities via a CDS bootstrap, to feed the engine a canonical trade representation (and emit FpML at the interoperability boundary), to read the structured result object back, to compute incremental XVA for a pre-deal quote, to run the whole thing as a nightly batch, and finally to reconcile a counterparty-level CVA back to the trades that make it up and defend it through a sign-off workflow to a posted reserve.
The exposure engine gives you one half of the CVA integrand; the other half is the probability that the counterparty defaults in each future interval, and that comes from their credit curve. For a name with liquid single-name CDS you bootstrap a term structure of hazard rates directly from the quotes; for a name without - which is most corporates and all mid-market counterparties - you build a proxy curve from a sector, rating and region mapping onto liquid index constituents. The chapter teaches both, and it teaches why proxy curves are where model validation digs hardest: a proxy mapping is a modelling choice with real profit-and-loss consequences, and you must be able to state, for any proxied counterparty, exactly which index and adjustment produced its curve. That is the kind of defensible-answer discipline the whole program is built to instil.
CVA and DVA are about default; FVA is about the simpler, ever-present fact that money has a cost. When you are in-the-money on an uncollateralised trade you are effectively lending to the market and must fund that lending at your own funding rate, above the risk-free rate; when you are out-of-the-money you may earn a benefit. FVA is the present value of that funding cost and benefit strip over the life of the trade, computed off the same exposure profiles you already built for CVA but weighted by your funding spread rather than the counterparty's default probability. The chapter is careful about a genuine trap here: the funding benefit on your negative exposure and DVA on the same negative exposure economically overlap, and booking both in full double-counts the benefit. Desks resolve this by choosing a funding framework that nets the overlap, and you must know which convention your book uses because it changes the sign and size of the total adjustment materially. This is the kind of nuance a formula alone never teaches.
Computing the reserve once a night is necessary but not sufficient. The XVA desk runs a profit-and-loss that moves every day as markets move, and it must hedge that. To hedge it needs sensitivities: how much the adjustment changes for a one-basis-point move in the counterparty's credit spread, in interest rates, in the commodity forward, in volatility. These are the same Greeks you met in pricing, but computed on the adjustment itself, and they are numerically expensive because each one requires re-running or bump-and-revaluing the whole exposure simulation. The chapter explains why XVA sensitivities are second-order, path-dependent and cross-gamma-heavy - a move in commodity prices changes not just the commodity delta but the exposure profile and hence the credit delta - which is why XVA desks are among the most computationally demanding on the trading floor.
The highest-value use of XVA, though, is before a trade is done. When a salesperson quotes a new trade they must know what charge to build into the price, or the desk wins business that destroys value. The key insight is that XVA is not additive: because of netting, a new trade's charge depends entirely on what is already in the netting set, and a trade that offsets existing exposure can carry a negative incremental charge - the desk should be willing to pay to do it. The chapter teaches the incremental calculation, why the pre-deal tool reuses the previous night's scenario grid to answer in seconds rather than minutes, and how to convert the total incremental charge into a spread add-on a salesperson can actually quote.
Everything comes together in the nightly batch - the orchestrated end-to-end run that produces the official numbers the institution reserves against. It is a pipeline: snapshot the market, extract and revalue trades, generate scenarios, aggregate exposure, integrate the adjustments, then publish to the datamart for reporting and sign-off. The chapter teaches the three failures that break this batch most often - missing market data that gets silently substituted, a single trade the engine cannot price taking the whole grid down without partition isolation, and grid exhaustion when a spike in book size blows the compute budget - and the operational patterns that mitigate each. Then it closes the loop: a computed number is not yet a reserve. It passes through desk review, independent price verification and senior risk sign-off, each logged with user, timestamp and comment, before it can post to the ledger. Learning where that control sits, and why, is part of what makes a candidate credible in a middle-office or risk interview.
That is one chapter. There are 499 others, each built to the same standard, each ending in artifacts you can inspect and a capstone you must defend. The sample chapter in the prospectus shows the full format applied end to end.
The program is deliberately built so that its output maps to the roles institutions actually hire and staff for. The same 500 chapters serve several destinations, and the interview-preparation and capstone work are organised around the seat you are targeting. Here is what desk-readiness means for each.
For a quant or XVA-modelling seat. You will be able to explain each XVA term in plain English and in formulae and say who pays whom; configure an exposure-simulation run - models, paths, time grid, netting and collateral - and defend every non-default choice; turn credit spreads into a survival curve via a CDS bootstrap; and account for wrong-way risk with a concrete example rather than a hand-wave. Crucially, you will be able to explain why the same trade costs more with counterparty A than counterparty B, which is the question that separates someone who has read about XVA from someone who can sit on the desk.
For a risk or counterparty-credit seat. You will be able to distinguish EPE, PFE and effective EPE and say which consumer each one serves - CVA, credit limits and regulatory capital respectively - and never use the three interchangeably. When a counterparty's CVA jumps but their spread barely moved, you will run a structured elimination: rule out population (new or amended trades), rule out credit (a small spread move cannot explain a large jump), and localise the cause to exposure - most often a collateral or CSA-mapping problem. That discipline, not a memorised formula, is what a credit-risk desk values.
For a middle-office or product-control seat. You will be able to reconcile a counterparty-level number back to the trades that make it up, decompose a day-on-day move into its market, exposure and population components, and confirm each trade's contribution ties out. You will treat an unexplained move as a red flag for a data or pricing error, not as something to sign off. On a real desk, unexplained is never an acceptable attribution, and this program trains the reflex to always find the driver.
For an ETRM or CTRM implementation-consultant seat. You will be able to design the trade representation that feeds a valuation engine - a clean canonical object holding the economics, the netting-set and CSA links, persisted and queryable, with an open-standard export at the interoperability boundary so nothing is locked to a proprietary format. You will understand where static data, market data, trade capture, risk and settlement connect, so you can scope and de-risk a real implementation rather than discovering the dependencies the hard way.
Most programs teach the happy path. This one spends as much time on the ways a number goes silently wrong, because that is where real desk value lives - and because the settings that corrupt a result almost never throw an error. They produce a clean, wrong number, which is far more dangerous. Every chapter carries a Day-on-the-Desk incident drawn from its own failure modes, and by the end you will recognise a catalogue of traps on sight.
You will learn, for instance, that a netting-enforceability flag set optimistically makes the engine compute netted exposure that legally should be gross, understating the risk - and that a surprising number of overstated netting-benefit findings trace back to that one boolean. You will learn that an exposure-simulation horizon set shorter than the longest trade silently truncates the tail and understates CVA; that a time grid too coarse near a large cashflow or option expiry misses the exposure spike entirely; and that ApplyCollateral set true against a stale or mis-mapped CSA makes the engine assume collateral that legally is not there. None of these throws. All of them require a human who knows to look.
The program also trains the reconciliation discipline that catches genuine errors. A CVA move always decomposes into a market piece (their spread moved), an exposure piece (the profile changed) and a population piece (trades were added, matured or amended). If a move cannot be attributed to one of those three, it is a red flag - usually a data problem, a mis-mapped netting set, or a pricing failure that should have been quarantined in the batch. You will practise this attribution until it is automatic, because it is the single most useful habit a middle-office or risk analyst can carry into a job.
Finally, you will see every number through the validator's eyes. Independent model validation, internal audit and ultimately the regulator will challenge your proxy-curve mapping, your wrong-way-risk alpha, your margin-period-of-risk assumption, your Monte Carlo convergence and your model choice. The program teaches you what each challenge asks and what a weak answer looks like, so you can defend your work rather than merely produce it. Learning XVA without learning how it is challenged leaves you half-ready; this program closes that gap deliberately.
Because every chapter ends in working, inspectable technology, you accumulate a portfolio as you go. You will read and author the canonical trade representation the platform uses - its JSON and its FpML - and drive the engine via REST. You will query the risk datamart in SQL to reconcile a counterparty-level number to the trade level and to run day-on-day attribution. You will edit and check in the configuration XML that parameterises a valuation or exposure run, and you will recognise the three settings in that file that can silently corrupt it. You will stand up the nightly batch that produces the official numbers, monitor it, and diagnose the failures that most often break it.
The capstones are build-and-defend, not multiple-choice. In the flagship XVA capstone, for example, you author the run configuration for a netting set of golden trades, justify the time grid, path count and models, enable wrong-way risk with a stated alpha, compute and reconcile the CVA/DVA and net FVA, produce the day-on-day attribution, and assemble a one-page reserve pack with the key assumptions and known limitations - then prepare a two-sentence defence of the total to a sceptical chief risk officer. The grading criterion is not recall; it is whether your configuration is internally consistent, your reconciliation ties out exactly, your attribution explains one hundred percent of the move, and your validator responses survive a follow-up challenge. That is the bar an institution pays for, because it is the bar a real reserve must clear before it posts to the ledger.
This is why the program describes itself as desk-readiness rather than a course. A certificate says you attended. A portfolio of configured runs, reconciled numbers and defended assumptions says you can do the job. The masterclass is engineered to produce the second thing.
The complete program spans twelve modules and all 500 chapters are listed below, numbered 1 to 500. Modules are collapsed for readability - expand any module to see its full chapter list. The downloadable prospectus contains the same list plus the exemplar chapter.
It is taught on a system, not on slides. Concepts are introduced where they live in a real platform, so you build the mental model a desk actually uses - from the deal outward, never from the formula inward. When you later meet a live trading and risk system, its shape is already familiar. It produces artifacts, not certificates. Every chapter leaves you with working SQL, configuration XML, trade representations and a defended project. You finish with a portfolio that demonstrates ability, which is what hiring managers and implementation leads actually test for.
It teaches the failure modes, not just the happy path. Each chapter includes a real incident investigation and the regulator's and model-validator's challenge. Anyone can quote a formula; the program trains you to find the stale CSA, the mis-mapped netting set, the time-grid that misses a cashflow - the things that quietly corrupt a number and that separate juniors from desk-ready analysts. It is cross-asset and connected. Because it follows eight recurring golden trades across every function, you see the same deal accumulate meaning as it moves through valuation, risk, collateral, settlement and finance. XVA stops being a silo and becomes the connective tissue you can reason across.
It maps directly to real roles. The interview-preparation sections are organised by the seat you are targeting - quant / XVA modelling, risk / counterparty-credit, middle-office / product-control, and ETRM/CTRM implementation-consultant - with the questions actually asked, the answers a strong candidate gives, and the red flags that expose a weak one. It is honest about what it is. All screens are illustrative mock-ups for learning; nothing depicts a specific commercial product. The value is the reasoning and the transferable skill, taught rigorously, not a walkthrough of any one vendor's buttons.
The same practitioner-grade curriculum, delivered two ways.
Cohort delivery for institutions onboarding trading, risk and implementation staff. Capstone review, the golden-trade case studies run as team exercises, and the syllabus scoped to your platform, data and use cases. Ideal for banks, energy and commodity trading companies, merchants and consultancies standing up or upgrading a trading and risk capability.
Self-paced with certification, for quants, risk analysts, middle-office professionals and consultants building credentials. Lifetime access to the full curriculum, the working artifacts and the build-and-defend capstones, so you finish with a portfolio you can show, not just a certificate you can name.
It is built for people who work on or around a trading and risk platform: trading, middle-office and risk staff at investment banks, energy and commodity trading companies and merchants; hedge fund and risk-consultancy analysts; quants; and TRM/ETRM implementation teams. It assumes no prior background and builds every concept from first principles, so a motivated learner from an adjacent field can start here.
This is an enterprise masterclass, not a consumer video course. Every one of the 500 chapters is taught on an integrated trading and risk platform used as the single reference system throughout, and each chapter ends in working artifacts and a build-and-defend capstone. It is graded on ability - can you configure a run, reconcile a number, and defend it to a validator - not on recall.
Sit on a desk, in a middle office, or on an implementation team and be useful on day one. Concretely: capture and value cross-asset trades, build curves and run an exposure simulation, compute and reconcile an XVA reserve, stand up and monitor a nightly batch, and defend your assumptions to model validation and a regulator.
Real, inspectable technology: SQL for the risk datamart, ORE/QuantLib-style XML for pricing and exposure configuration, FpML for trade representation at the interoperability boundary, and REST for driving the engine. Every chapter includes working code artifacts you can read, run and check into version control.
Yes. The same curriculum is delivered in two tracks off one syllabus: an Enterprise / Team track (cohort delivery, capstone review, golden-trade case studies as team exercises) for institutions onboarding trading, risk and implementation staff; and an Individual Professional track (self-paced with certification) for quants, risk analysts and consultants building credentials.
All screens shown in the program are illustrative mock-ups created for training purposes only. They do not depict any specific commercial product and are not a manual for any live system. All counterparties, trades, prices and results are fictional and internally consistent for teaching.
It is a substantial program - 500 chapters across 12 modules. Pace depends on your track and prior background; the Individual Professional track is self-paced with lifetime access, and corporate cohorts are scheduled to your timeline. A single exemplar chapter (Chapter 193, XVA) alone runs to a full production-grade unit.
The downloadable prospectus includes the full 500-chapter curriculum and a complete exemplar chapter (Chapter 193, Valuation Adjustments / XVA) showing the standard every chapter is built to.