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STRATEGY RESEARCH / INTERACTIVE DEMONSTRATORS

Strategy Research

Examine payoff construction, scenarios and risk before a decision is made.

Calcul local · flux externe facultatif

Scope and decision framework

Why this laboratory?

A strategy needs to be understood through cash flows, exposures and evaluation conditions. This laboratory connects payoffs, scenarios and historical simulations to examine how a rule behaves. It complements the Quant and Risk Labs without turning a simulation into a performance promise.

Ambition with discipline

Results depend on the supplied data, period and implementation assumptions. They are neither investment advice nor a return guarantee. Conclusions require examination of costs, liquidity, selection bias and out-of-sample validation.

Payoff structures

Option combinations and factor exposure.

Simulation and comparison

Rules, periods and comparable assumptions.

Implementation risks

Costs, liquidity and result limitations.

Related methods and sections
All methods and sections
Option strategies · interactive explorer

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OPTIONS / 01

Atelier de payoff

Model assumptions and parameter effects

Premiums and Greeks use constant-volatility Black–Scholes. Spot, strikes and contract count also determine the displayed payoff profile. Volatility, maturity and rates affect theoretical option premiums; FX inputs apply only to the corresponding FX or quanto strategies. Educational scenario; market fees and market data are not included.

PROFIL À L’ÉCHÉANCE

Call acheté

P&L net à l’échéance
Prime nette—
Profit maximal—
Perte maximale—
Points morts—
Δ Delta—
Γ Gamma—
V Vega / 1 pt—
Θ Theta / jour—

Scénarios à l’échéance

Model reference

Black–Scholes lognormal valuation underlies the option premiums and Greeks. Strategy payoffs combine the selected legs; the strategy contract is not attributed to the paper.

Fischer Black, Myron Scholes (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy 81(3), 637-654.

Original publication ↗

STRATEGY RESEARCH / WORKING PAPERS

Credit, structuring, FX, market regimes and commodities

Each note describes an economic construction, a profit-and-loss scenario, the principal risks and the conditions under which the analysis stops being informative. Scenarios are parametric, not historical.

Scenario assumptions and parameter effects

P&L is a parametric proxy: notional, carry, shock, horizon, hedge ratio and costs feed the scenario. Any beta shown in a strategy note is illustrative and uncalibrated. Results are neither a market valuation nor a forecast.

P&L du scénario—
Choc +100 bp—
Stress +300 bp—

STRATEGY MAP

Lecture économique des payoffs

Le profil de risque dépend de la direction, de la volatilité, du temps et de la convexité.

DIRECTION

Vertical spreads

Exprimer une vue haussière ou baissière avec gain et perte généralement bornés.

VOLATILITY

Straddle / Strangle

Acheter la convexité lorsque l’amplitude anticipée domine la prime versée.

RANGE

Butterfly / Iron Condor

Cibler une zone d’échéance avec risque borné.

HEDGE

Protective Put

Conserver l’exposition haussière du sous-jacent en achetant un plancher de protection.

RISK BUDGET

Volatility Target

Ajuster l’exposition au risque réalisé avec plafonnement du levier.

HEDGE FUND / EQUITY

Equity Long/Short

Combiner positions longues et courtes et contrôler séparément gross et net exposure.

HEDGE FUND / MACRO

Global Macro

Vues de régime cross-asset, dimensionnées par budgets de risque et scénarios.

HEDGE FUND / RELATIVE VALUE

Fixed-Income Relative Value

Arbitrage de convergence avec neutralisation explicite du risque directionnel.

HEDGE FUND / EVENT

Merger Arbitrage

Valeur probabiliste d’un événement avec risque explicite de rupture.

METHOD & SOURCES

Références et limites

  1. Thomas A. McCaffertyAll About Options — stratégies d’options, gestion du risque et plans de trading.
  2. Yves HilpischPython for Algorithmic Trading — stratégies SMA, momentum, mean reversion et backtesting vectorisé (ch. 4).
  3. Alexander ElderVivre du trading — discipline, contrôle du risque et gestion de position.
  4. Mhamed BettaiebOptions Pricing with Python — payoffs, stratégies, Greeks, tests de parité et stress tests (ch. 2–4).
  5. Yves HilpischListed Volatility and Variance Derivatives — volatilité réalisée, variance swaps et réplication (ch. 3, 9–10).
  6. Uwe WystupFX Options and Structured Products — conventions de change, call spreads, risk reversals, straddles, strangles, butterflies, condors et seagulls (ch. 1.3–1.8).
  7. Martin Sewell et al.Developing High-Frequency Trading Systems — stratégies, architecture de marché, coûts et contraintes d’exécution (ch. 1–3).
  8. Robert A. JaegerAll About Hedge Funds — leverage, short selling and hedging (ch. 9); strategy taxonomy (ch. 15); equity/global allocators (ch. 16), relative value (ch. 17) and event-driven investing (ch. 18).
  9. Giles JewittFX Derivatives Trader School — surface ATM et smile (ch. 11–13), expositions de trading (ch. 14–17) et exotiques FX (ch. 18–31).
  10. Sofien KaabarMastering Financial Pattern Recognition — patterns de tendance et contrarians (ch. 4–7), Heikin-Ashi (ch. 8), sorties (ch. 9) et stratégies combinées (ch. 10–12).
  11. Iain J. ClarkCommodity Option Pricing — futures, Asian and spread options, precious-metal smiles, energy, power, swing, spark and crack-spread products (ch. 2–9).
  12. Viviana FanelliFinancial Modelling in Commodity Markets — spot/forward models, seasonality, electricity hedging, spark spreads and statistical arbitrage (ch. 1–5).
  13. Thomas J. DorseyCommodity Strategies — trend, relative strength, breakouts, momentum, position management and diversification (ch. 1–6).
  14. Carley Garner & Paul BrittainCommodity Options — long/short volatility, straddles, strangles, credit spreads, limited-risk spreads and synthetic swing structures (ch. 3–7).
  15. Limite méthodologiqueLes primes utilisent Black–Scholes à volatilité constante ; le backtest repose exclusivement sur une trajectoire synthétique.

Hybrid products and models

20 energy strategies

Interactive research · Synthetic scenarios

Event-driven strategies

DECISION FRAMEWORK

A quoted opportunity is not an executed trade

Bid, ask and last-trade prices describe different market observations. A price signal becomes a trading outcome only through an execution convention: an immediately marketable order and a passive order have different costs and fill uncertainty. Evaluate the spread and explicit fees alongside the time allowed for execution and the treatment of unfilled orders. Historical fill statistics in the book concern a particular Chinese futures sample; they are not transferable success probabilities for another instrument, venue or period. Interpret a Lab payoff as a stated scenario, not evidence of achievable net profit.

Reference relation and assumptions

st=at−bts_t=a_t-b_t
  • s_t: quoted spread, in price units.
  • a_t: ask; b_t: bid, for the same instrument, timestamp and size convention.
  • t: quote observation time. Non-crossed quotes assume a_t≥b_t.
  • This definition excludes fees, impact and non-fill risk.
Decision, data and controls

Declare the reference quote and fill assumption; preserve quote timestamps, direction, quantity and fee units; reconcile theoretical payoff with executed cash flows before reporting performance.

Scope, limitations and source

Historical source, not a live feed, brokerage connection or executable strategy. Do not reuse the book’s 2012 fill rates, fixed fees or claims that ordinary transaction costs are ignorable.

Wang & Zheng (2015), §2.6 printed p35 / PDF50; chapter8 printed pp155–157 / PDF170–172.

Before selecting a strategy: examine the evidence

Portfolio construction begins with a testable hypothesis. These notes set out what a research protocol needs to control before interpreting performance.

Published research note · Methodological synthesis

Validate a strategy without information leakage

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A financial observation may span an interval: the label depends on a future move or a barrier crossing. Adjacent observations can therefore share information. A random train–test split can treat dependent information as independent.

Record each label’s start and end dates, purge training events overlapping the test and define an embargo justified by feature construction. Fit transformations, feature selection and parameter tuning within training data only.

These controls address a specific bias; they do not guarantee future returns. Combinatorial validation is not a chronological deployment simulation. Research also needs a final time-ordered test, explicit costs and a verifiable availability date for every input.

Sources and attribution

Marcos López de Prado, Advances in Financial Machine Learning, Wiley, 2018, chapters 3 (Labeling), 7 (Cross-Validation in Finance) and 12 (Backtesting through Cross-Validation).

Published research note · Methodological synthesis

Address selection bias before discussing Sharpe

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The best strategy among many trials is not comparable to a hypothesis tested once. Signal variants, windows and parameter choices create more opportunities to select chance results. Retain discarded trials and selection criteria, not just the final chart.

We propose an experiment register: initial hypothesis, point-in-time universe, parameters tried, costs, selection criterion and reserved test period. Report sensitivity to modest cost or window changes. Stability matters more than an isolated optimum.

Statistical corrections depend on return assumptions and dependence between trials. They cannot repair an incomplete research history. Trading Lab profit-and-loss profiles are synthetic scenarios with no claimed historical performance.

Sources and attribution

Marcos López de Prado, Advances in Financial Machine Learning, Wiley, 2018, chapters 11 (The Dangers of Backtesting), 12 and 14 (Backtest Statistics).

Published research note · Methodological synthesis

Separate signal, sizing and execution

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A directional signal does not define a position. Specify exposure size, holding period, exit rules and liquidity constraints. A classifier probability is not itself an expected-return estimate or a justification for leverage.

Separate forecast quality from the decision to take risk. Compare position sizes under fees, slippage and adverse scenarios. Preserve the ability not to trade when confidence, liquidity or payoff asymmetry fails the stated criteria.

Simulations do not automatically reproduce order books, funding restrictions or market breaks. This is a methodological framework, not a recommended allocation or a trading signal.

Sources and attribution

Marcos López de Prado, Advances in Financial Machine Learning, Wiley, 2018, chapters 3 (meta-labeling), 10 (Bet Sizing) and 16 (Machine Learning Asset Allocation).

Before and after the trade: test the thesis and document the decision

Overconfidence can increase turnover and understate uncertainty. The disposition effect can encourage selling gains quickly while retaining losses. Hindsight can then make the outcome appear inevitable. Review decisions and their evidence, rather than realised P&L alone.

Application to the workflow

Proposed application: record the thesis, falsifying evidence, adverse scenarios, position size and next review. Assess winners and losers with the same criteria and independently challenge material decisions. Synthesis: distinguish process quality, accepted risk and outcome; a gain alone does not establish a sound decision.

Assumptions and limits

Assumptions: timestamp the decision log before execution, trace costs and define limits before the scenario. Increasing exposure after a fall can follow a documented strategy and does not prove bias. Scenarios are conditional tests: a 3D surface assigns no probabilities to displayed regions and does not predict their occurrence.

Read the note: model, formula and synthesis →