| Document Reference | KC-OM-v0.4 |
| Version | 0.4 BASELINE |
| Supersedes | KC-OM-v0.3 (30 May 2026) — view v0.3 | v0.2 | v0.1 |
| Classification | INTERNAL Not for distribution |
| Date | 30 May 2026 |
| Prepared By | Clinton Ratanatray, Founder — Kalaya Capital |
| In Consultation With | Matt Breakwell, Partner — Kalaya Capital |
| Purpose | To document the operating philosophy, research methodology, portfolio construction framework and governance model of Kalaya Capital. This document is the Kalaya Capital Operating System. |
| v0.4 Changes | First baseline version. LLP Governance Development added (§1.2). Primary asset statement strengthened (§2). Institutional knowledge framework added (§11, §16, §26). Regime framework updated with Dalio attribution and research objective language (§18). Current phase and unproven capabilities added (§31, §32). July LLP Session rewritten as first LLP engagement (§33). Path to October added. Editorial pass throughout. |
Kalaya Capital is a systematic, directional, long/short trading operation. It identifies recurring behavioural patterns in financial markets, builds mechanical rules to exploit them, validates those rules rigorously, and deploys capital systematically.
Kalaya operates through two entities with distinct responsibilities:
| Entity | Primary Activities |
|---|---|
| Kalaya Capital Limited | Research, strategy development, code development, intellectual property ownership |
| Kalaya Capital LLP | Capital deployment, portfolio construction, capital allocation, member capital management |
Research and IP are owned by the Limited company. Capital management is conducted through the LLP. These are distinct activities and are not interchangeable. This document applies across both entities.
The Kalaya Capital LLP governance framework is currently under development. The following components are being built in parallel with the research and execution infrastructure so that the LLP can operate in a structured, repeatable, and transparent manner from the point of launch:
These components are being developed progressively. Their current status is reflected in §31 (Current Phase).
Kalaya exists to build and maintain a repeatable, evidence-driven process for discovering, validating, and allocating capital to systematic trading strategies.
Kalaya's primary asset is not any individual strategy, model, report, or codebase. Its primary asset is the integrated operating system that continuously produces, validates, deploys, monitors, and manages systematic investment strategies — and the accumulated institutional knowledge embedded within it.
Most systematic trading operations fail not because their strategies are wrong, but because their process is wrong: strategies are over-fitted to historical data, deployed without rigorous validation, and retired too late — or too early — without an objective framework. Kalaya is built to avoid those failures. The primary output is not any individual strategy. It is a process that continuously produces, tests, and manages strategies.
The objective is not to predict markets. It is robust portfolio construction — assembling independently evidenced strategies across markets, behaviours, and timeframes such that the portfolio maintains positive expectancy across a wide range of conditions, even when individual strategies fail.
We expect strategies to degrade, fail, or be superseded over time. The durability of this operation depends entirely on the quality of the process — not on the durability of any individual strategy. A business that depends on a single strategy surviving indefinitely is not a business. A business with a robust process for continuously producing and replacing strategies is.
These principles govern how Kalaya thinks, researches, and makes decisions. They apply to every component of the business — not just strategy development.
Markets change. Regimes shift. No single strategy produces durable edge across all market conditions in perpetuity. The objective is a repeatable, disciplined process for identifying, validating, deploying, and retiring edges as market behaviour evolves.
Strategy development begins with a fundamental question: what market behaviour are we trying to exploit? This shifts the work away from curve-fitting and indicator layering toward testing whether well-documented behaviours — momentum continuation and mean reversion — produce statistically meaningful edge when expressed as simple, mechanical rules.
Portfolio construction is more important than any individual strategy. A single strategy may fail in a given regime. A well-constructed portfolio of uncorrelated strategies is expected to maintain positive expectancy across a wider range of conditions. Strategies are assessed not only on their individual metrics but on their portfolio contribution — their regime behaviour, failure conditions, and correlation to existing deployed strategies.
Kalaya is built to be durable — for the markets, for the process, and for the people involved in building it.
The business is structured to generate long-term returns that are meaningful to founders, partners, and future participants. Short-term performance pressure creates exactly the wrong incentives in a systematic trading business: it encourages over-fitting, premature deployment, and reluctance to retire failing strategies. Kalaya's model is deliberately long-cycle. Research takes time. Validation takes time. Genuine edge accumulates over time.
The benefits of Kalaya's research, capital growth, and business development should flow equitably to those contributing to them. This applies to founders, partners, and any future contributors who bring genuine value to the research, operations, or governance of the business.
A sustainable business is one that can be operated at high quality over the long term. Kalaya prioritises a research pace that maintains quality over quantity; an operational model that does not require continuous human intervention; documentation and systems that can be handed off or scaled without loss of quality; and a culture of honesty about what is working and what is not.
Kalaya strategies operate primarily using price, time, and volume — where available. The relative importance of each input varies by strategy. External data — economic releases, central bank communications, fundamental valuations — are not used as signal inputs. They may inform portfolio-level decisions, but they do not enter the signal generation process.
| Characteristic | Description |
|---|---|
| Systematic | All signals, entries, exits, and sizing are generated by code. No human judgement at the execution level. |
| Directional | Strategies express a view on price direction — long or short. No hedging within a strategy. |
| Long / Short | Both directions with equal mechanical rules. No structural bias. |
| Multi-market | Strategies are instrument-agnostic. A validated strategy is tested across the full universe, not tuned to a single market. |
To identify recurring changes in market behaviour — moments where the probability of a directional move is measurably better than random — and exploit them systematically, across multiple markets, with defined risk, at scale.
Kalaya organises all research around four behavioural categories describing what the market is doing when a signal fires — not what the strategy is called. Multiple strategies can operate within the same category and compete for capital based on evidence.
| Category | Behaviour Being Exploited | Expected Failure Regime |
|---|---|---|
| Momentum / Breakout | Directional persistence after a structural level break, driven by order flow imbalance | Choppy, low-volatility ranging; high-frequency false breaks |
| Mean Reversion | Price overextends from a statistical anchor and returns to it | Strong trending markets; sustained momentum moves |
| Trend Pullback | Continuation entry at a retracement within an established directional trend | Ranging markets; deep pullbacks that become full reversals |
| Reversal / Exhaustion | Trend termination at structural extremes; directional flip after exhaustion of order flow | Strong trends with no exhaustion; low-conviction reversal signals |
Strategies compete for capital within their category based on cross-market evidence, regime fit, and correlation to existing portfolio positions. A category with no validated strategy receives no allocation.
A strategy idea is a hypothesis, not a conviction. The research process exists to test the hypothesis. All findings — positive and negative — are valid outputs. A rejected strategy produces as much value as an approved one: it eliminates an idea that should not receive capital and adds to the institutional knowledge base.
Before entering the research pipeline, a strategy must be codified as unambiguous, deterministic rules; classified into a behavioural category with a declared expected failure regime; testable on historical data without human interpretation; and applicable to the full research universe without instrument-specific modifications.
A strategy with high Sharpe on one instrument is not automatically valuable. A strategy with modest but consistent positive expectancy across eight instruments, with failure conditions that differ from the existing portfolio, is more valuable. Individual performance metrics are diagnostic. They do not determine approval.
No strategy receives allocation until it has completed the full validation process and passed all defined approval criteria.
Capital deployed to a strategy that has not passed validation is speculation, not investment. Every pound of capital deployed must be backed by a documented research and validation record. A strategy that looks good on a chart is not a validated strategy.
AI tools are used across the Kalaya research and operational workflow in clearly bounded roles.
AI accelerates the process. Every deployment decision, risk limit, and governance review is a human responsibility. The accountability structure does not change because AI was involved in producing an output.
This accumulated knowledge is a core asset. It must be systematically captured, version-controlled, and accessible — not fragmented across conversations, emails, or undocumented side processes.
A systematic trading operation generates large volumes of research data, validation records, code changes, decision records, and operational logs. Without a single authoritative source, decisions get made on incomplete information. Strategies get re-researched unnecessarily. Hard-won lessons are lost. The operating model drifts from documented reality. Institutional knowledge evaporates when individuals step away.
The specific technical architecture for knowledge management — tooling, integration, search — remains under development. The principle is fixed: one source, multiple outputs, full version control, zero information lost to undocumented processes.
The Kalaya operating model is a linear, sequential process. Capital does not advance until each stage is complete. No stage can be skipped.
The Python-native research pipeline implements stages 1–4 of the operating model. Each stage produces outputs consumed by the next. All stages run without human intervention once triggered. (Source: quant-pipeline/)
Data is sourced from Interactive Brokers via ib_insync (MIDPOINT for FX). The pipeline runs across all instruments in config/instruments.yaml in a single execution. A cross-instrument summary is produced for portfolio-level comparison.
Every strategy moves through a defined lifecycle. The lifecycle governs how capital is allocated — and how it is withdrawn — as evidence accumulates.
The pipeline is permanent. Individual strategies are not. A retired strategy is archived with its full specification, validation record, and failure analysis — preserving the knowledge it represents.
Kalaya's structural edge is not any individual strategy. It is the ability to continuously discover, validate, deploy, monitor, and replace strategies as market behaviour evolves.
| Response | When Appropriate |
|---|---|
| Reduce | Early degradation signal detected. Edge weakening but not confirmed absent. |
| Suspend | Degradation confirmed. Capital paused while diagnosis is underway. |
| Retire | Re-validation fails. Edge confirmed absent. Archived as institutional knowledge record. |
| Replace | A new validated strategy covers the same or better behavioural role. |
| Reintroduce | A retired strategy shows renewed edge in changed regime conditions. Must re-validate. |
The correct response to degradation is never to repair the strategy by adding complexity. That increases fragility without addressing the underlying cause.
Kaizen applies to the entire organisation — not just strategy development. Every component of the business is subject to ongoing review and improvement. Every lesson learned strengthens the institutional knowledge base.
| Component | What is Reviewed |
|---|---|
| Research | Signal detection quality, data sources, research efficiency. Which approaches produce candidates and which do not — captured as institutional knowledge. |
| Validation | Are gating thresholds correctly calibrated? Are the right metrics being used? Each cycle's validation decisions are recorded as decision records. |
| Execution | Infrastructure reliability, fill quality, guard layer calibration, execution cost accuracy. |
| Governance | Are governance reviews producing useful decisions? Are risk controls still appropriate? Are lessons from previous governance cycles being applied? |
| Reporting | Does reporting give the right information at the right time? Is the portal useful and navigable? |
| Business processes | Operational efficiency, documentation quality, communication between founders and partners. Lessons from operational failures are documented. |
| Operating model | This document. Updated with each version to reflect current reality — not historical aspiration. Each version is a decision record in itself. |
Improvements are documented, version-controlled, and implemented as discrete changes. The operating model at any point in time reflects the accumulated learning from all previous cycles. This institutional memory is a proprietary asset that grows with every iteration.
The objective of diversification is stable portfolio behaviour across a wide range of conditions — not risk reduction for its own sake. A well-diversified portfolio maintains positive expectancy even when significant parts of it are in drawdown.
Single-strategy dependence is a portfolio-level risk. A portfolio that generates most of its returns from one strategy is not a robust portfolio — it is a concentrated bet on that strategy continuing to work. Kalaya treats this as a risk to be actively managed.
Capital allocation should be informed by the current market environment. Kalaya uses a structured regime framework to form a view on prevailing conditions. This is not forecasting. It is structured market thinking that supports portfolio allocation conversations.
The Growth / Inflation two-axis framework used below is adapted from concepts popularised by Ray Dalio and Bridgewater Associates. Kalaya does not claim ownership of this framework. It is used as a practical organising structure for regime assessment, applied alongside Kalaya's own analysis.
Market regime is assessed on two axes — Growth (Increasing / Decreasing) and Inflation (Increasing / Decreasing) — producing four quadrants:
One objective of the Kalaya research programme is to determine, through empirical testing, which behavioural categories and strategies perform best in each regime quadrant. If reliable regime-strategy relationships are identified in the data, this evidence may eventually be used to inform dynamic allocation decisions. Until that evidence exists, regime awareness informs qualitative allocation thinking only — it does not drive quantitative rebalancing.
Portfolio construction and capital allocation are distinct activities. Portfolio construction decides which strategies belong in the portfolio. Capital allocation determines how much capital each strategy receives.
Passing validation is a necessary condition for capital allocation. It is not a sufficient condition. A validated strategy that duplicates the regime exposure of an existing strategy, or adds concentration risk, may receive zero allocation until portfolio conditions change.
Between validation approval and live capital allocation sits the Portfolio Risk Layer. Every validated strategy receives capital through this layer — not directly.
| Responsibility | Description |
|---|---|
| Position sizing | Applying the risk sizing formula to determine actual position size from a fixed cash risk amount and defined stop loss. |
| Diversification | Ensuring new strategy additions improve portfolio diversification rather than add correlated exposure. |
| Exposure management | Monitoring total long and short exposure. Preventing unintended directional bias at the portfolio level. |
| Concentration control | Capping the percentage of total risk allocated to any single strategy, behavioural category, or instrument. |
| Regime adjustment | Adjusting allocation based on regime view and expected strategy behaviour in that regime — where evidence supports it. |
Every position is sized from a fixed cash risk amount and a defined stop loss distance — always. This applies in backtesting, paper trading, and live deployment.
The specific live deployment parameters — maximum risk per trade, maximum portfolio risk, and regime adjustments — remain under development and will evolve as live experience is accumulated. The formula is fixed; the parameters are calibrated over time.
All four must pass. (Source: quant-pipeline/evaluation/approve.py)
| Metric | Threshold | Rationale |
|---|---|---|
profit_factor_net | ≥ 1.20 | Gross profit exceeds gross loss by ≥20% after costs |
trade_count | ≥ 50 | Minimum sample size for statistical reliability |
expectancy_net_pct | > 0.0% | Mean return per trade positive after all costs |
max_drawdown_net_pct | > −30% | Extreme guard only — unusable signal protection |
Computed and reported. Do not influence approval. Inform portfolio construction and monitoring: Sharpe ratio, Calmar ratio, payoff ratio, maximum consecutive losses.
Monte Carlo — 1,000 simulations resampling trade returns with replacement. Key outputs: p95 max drawdown, p5 final return, median outcome. Reported; not gated. (Source: quant-pipeline/montecarlo/mc.py)
Walk-Forward — data split chronologically into thirds and run independently. A strategy that passes on full data but fails individual segments is flagged as fragile. (Source: quant-pipeline/robustness/walkforward.py)
Monthly Consistency — high total return concentrated in a small number of months is flagged. Consistency across months indicates structural edge, not outlier events.
The current portfolio construction model evaluates strategies on positive net expectancy. No position sizing or leverage is applied in the research pipeline. Results are expressed as return-per-trade percentage, normalised for cross-instrument comparison.
The target model adds a regime overlay that adjusts strategy allocation based on market state. This is architected but not yet implemented. Market state is classified on two axes: structure (trending, ranging, transitioning) and volatility (low, normal, high). Evidence linking regime conditions to specific strategy performance must be established before this capability is deployed.
All simulations deduct round-trip execution cost from every trade:
round_trip_cost_pct = 2 × (spread_pct + slippage_pct) + commission_pct
Per-instrument cost assumptions live in config/instruments.yaml.
The current Kalaya execution stack is Python-native, IBKR-connected. Signals are generated by Python strategy modules. Orders are placed via IB Gateway using ib_insync. All execution logic, safety guards, and monitoring run in Python.
The Kalaya repository contains MQL5 Expert Advisors built for MetaTrader 5 (DR, FBO, BB_KL, 2B). These represent an earlier research phase and are not part of the current operating stack. The MQL5 code is retained as a research archive.
Every order attempt passes through four mandatory guard layers before reaching the broker. (Source: quant-pipeline/execution/execution_guard.py)
config/kill_switch.yaml on every call. If active: true — all orders blocked immediately. Takes effect within milliseconds of file save.Humans do not intervene in trade execution. Humans intervene through review, allocation, risk decisions, and governance.
At the individual trade level — signal generation, order placement, position sizing, stop setting, exit execution — everything is automated. Human intervention is reserved for:
| Intervention Point | Nature |
|---|---|
| Portfolio Construction | Which validated strategies enter the live portfolio. Initial allocation weights. |
| Capital Allocation | Adjusting allocation percentages. Responding to equity changes and regime shifts. |
| Strategy Disposition | Retain, reduce, retire, or replace decisions based on observed behaviour. Each decision is recorded. |
| Governance Review | Reviewing system health, monitoring outputs, risk controls. |
| Emergency Override | Kill switch activation. Immediate halt of all execution. |
Every deployed strategy has an expected behavioural profile derived from its validation run. Live behaviour is compared against this profile continuously. Material deviations trigger a structured review — the objective is early identification of degradation before it causes portfolio damage.
A strategy enters enhanced monitoring when:
A strategy in a temporary drawdown within its expected envelope is behaving normally. Over-reacting to normal variance by retiring strategies too early is as damaging as under-reacting to genuine degradation. The monitoring framework must distinguish between the two. All review decisions are logged as decision records.
This accumulated knowledge forms a long-term proprietary asset that compounds in value over time. Its quality determines the quality of every future allocation decision.
| Data Type | Location | Value |
|---|---|---|
| Historical price data | quant-pipeline/data/raw/ | Foundation for all research |
| Validation runs | quant-pipeline/outputs/ | Evidence base for approval decisions |
| Approval records | outputs/<instrument>/approval.json | Documented evidence trail |
| Execution logs | outputs/executions/execution_log.csv | Governance audit trail |
| Research portal reports | kalaya-research-portal/research/ | Published research history |
| Strategy metadata registry | quant-pipeline/strategy/metadata.py | Strategy specification and history |
| Operating model versions | kalaya-research-portal/memorandum/ | Decision record of business evolution |
A rejected strategy, with its full evidence record, prevents that idea from being re-researched without new evidence. Each rejected hypothesis narrows the search space and reduces wasted future effort. Lessons from failures — what structural weaknesses were detected, what conditions caused failure — are retained as institutional knowledge and applied to future research design.
The value of this knowledge base compounds over time. An operation that has been running for five years, with disciplined record-keeping, has a research edge that cannot be replicated quickly by a new entrant. Building and maintaining this asset is a core responsibility of every Kalaya founder, partner, and contributor.
Current research is focused on FX instruments. This is a deliberate starting point, not a permanent limitation.
| Phase | Asset Class | Status |
|---|---|---|
| 1 | FX — Major and Minor Pairs | Active |
| 2 | Equities & Indices | Planned |
| 3 | Bonds & Rates | Planned |
| 4 | Commodities — Metals | Planned |
| 5 | Commodities — Energy | Planned |
| 6 | Commodities — Softs | Planned |
The methodology, validation framework, and pipeline are instrument-agnostic. Universe expansion requires data access, cost model calibration, and infrastructure testing — not methodology changes.
A system status JSON is written after every execution cycle, providing a complete operational snapshot without requiring an active IBKR connection. Fields: kill_switch_active, connection_status, system_state, orders_today, open_orders, rejected_orders_today, last_block_reason. (Source: quant-pipeline/monitoring/system_status.py)
Kalaya governance is lean and purpose-built. It exists to ensure allocation decisions are made on evidence; the research pipeline is producing candidates continuously; live strategies are monitored on a defined schedule; the operating model remains current; and the kill switch and execution guards are tested and functional. All governance decisions are recorded.
Every order attempt is logged with timestamp, symbol, side, quantity, order type, status, and block reason. This provides the governance audit trail and contributes to the institutional knowledge base. (Source: quant-pipeline/execution/execution_logger.py)
Every strategy that completes validation receives a structured HTML research report, generated programmatically from pipeline outputs. The report is designed to be scannable in under 30 seconds and is published to the research portal immediately upon generation. (Source: quant-pipeline/reporting/html_report.py)
All strategy reports are published to the Kalaya Capital Research Portal — a static HTML site organised by instrument and strategy type. The portal is the primary access point for all research outputs and is version-controlled and deployed via GitHub. (Source: kalaya-research-portal/)
| Classification | Criteria | Implication |
|---|---|---|
| PASS | All gating metrics pass on ≥1 instrument. Cross-market consistency confirmed. | Advances to portfolio construction consideration |
| WATCHLIST | Marginal metrics, insufficient data, or regime-dependent edge. | Monitored. Not deployed. Re-evaluated on additional data. |
| REJECT | One or more gating metrics fail on all instruments. | Archived as institutional knowledge record. Not eligible for resubmission without new evidence. |
Operational status as of 30 May 2026.
Kalaya Capital is currently in its Research & Infrastructure Validation phase. This phase has clear objectives and defined success criteria before the next phase begins.
Kalaya Capital has built a rigorous process and demonstrated research capability. The following capabilities have not yet been demonstrated in live conditions and should not be claimed as proven:
| Capability | Current Status |
|---|---|
| Long-term profitability | Not yet demonstrated. No validated strategies have been deployed to live capital. |
| Live execution performance | Not yet demonstrated. Paper execution infrastructure built; live session validation pending. |
| Portfolio construction effectiveness | In development. No live portfolio has been constructed. Framework is architected. |
| Regime allocation effectiveness | Research objective. No empirical evidence linking regimes to Kalaya strategy performance yet exists. |
This transparency is not a weakness. It is the correct intellectual posture for a business at this stage of development. The value Kalaya offers at this stage is the quality of the process, the rigour of the research, and the discipline of the infrastructure — not claimed performance that does not yet exist.
Fact FACT — An external session was delivered on 30 April 2026 to Andrew and Matt, presenting a weekly breakout strategy validation across five FX instruments. (Source: kalaya-research-portal/Andrew_Matt_30.04.26/)
Assumption ASSUMPTION — A July 2026 session is planned. Specific date, venue, and final attendee list are not yet confirmed.
The July session is the first formal LLP engagement — an opportunity to present the Kalaya vision, operating model, governance approach, research methodology, and progress to date. It is a forum for feedback, discussion, and relationship building. It is a milestone on the path toward live deployment — not the deployment itself.
Specific objectives for the session:
The session is successful if attendees leave believing that Kalaya Capital is:
| Area | What to Show |
|---|---|
| Philosophy | Operating Memorandum v0.4. The Kalaya operating system: principles, process, and long-term vision. |
| Research | Research portal with published validation reports. Cross-instrument results. The pipeline flow. |
| Infrastructure | IBKR paper execution stack. Guard layers. Kill switch. System monitoring. |
| Governance | LLP structure. Corporate entities. Governance framework under development. |
| Reporting | Auto-generated HTML reports. Portal publish workflow. Audit trail. |
| Intellectual honesty | What has been built. What has not yet been proven. The realistic path to live deployment. |
The July session is not intended to demonstrate live capital deployment. Its purpose is to validate the operating model and build the foundations for the next phase. The path from July to a controlled live portfolio involves:
The October timeline is directional, not committed. ASSUMPTION Each step above is a dependency. Progress will be reported through regular LLP governance sessions.
(Source: quant-pipeline/evaluation/approve.py)
| Metric | Threshold | Type |
|---|---|---|
profit_factor_net | ≥ 1.20 | Hard Gate |
trade_count | ≥ 50 | Hard Gate |
expectancy_net_pct | > 0.0% | Hard Gate |
max_drawdown_net_pct | > −30.0% | Extreme Only |
| Sharpe / Calmar / Payoff / Consec Loss | Observation only | Not Gated |
| p95 MC drawdown | Reported, not gated | Not Gated |
Current FX universe. (Source: quant-pipeline/config/instruments.yaml)
| Instrument | IBKR Symbol | Sec Type | Exchange | Spread (bps) | Slippage (bps) |
|---|---|---|---|---|---|
| EURUSD | EUR | CASH | IDEALPRO | 1.5 | 0.5 |
| GBPUSD | GBP | CASH | IDEALPRO | 2.0 | 0.5 |
| USDJPY | USD | CASH | IDEALPRO | 2.0 | 0.5 |
| AUDUSD | AUD | CASH | IDEALPRO | 2.0 | 0.5 |
| NZDUSD | NZD | CASH | IDEALPRO | 3.0 | 0.5 |
| USDCAD | USD | CASH | IDEALPRO | 2.0 | 0.5 |
| USDCHF | USD | CASH | IDEALPRO | 2.0 | 0.5 |
| EURGBP | EUR | CASH | IDEALPRO | 2.0 | 0.5 |
| XAUUSD | XAU | CMDTY | SMART | TBC | 0.5 |
| Term | Definition |
|---|---|
| Alpha | Return attributable to systematic edge — not market exposure. |
| Behavioural Category | One of four market behaviour types: Momentum/Breakout, Mean Reversion, Trend Pullback, Reversal/Exhaustion. |
| Capital Competition | The process by which validated strategies compete for portfolio allocation based on evidence, regime fit, and portfolio correlation. |
| Decision Record | A documented record of a governance, research, or allocation decision — including the rationale and evidence considered. |
| Edge Degradation | The weakening or disappearance of a strategy's positive expectancy due to regime shift, crowding, or structural change. |
| Expectancy | Mean return per trade, net of all costs. Positive expectancy is required for approval. |
| Execution Guard | Pre-trade safety layer validating every order before it reaches the broker. |
| IB Gateway | Interactive Brokers' server application providing TCP interface for automated order placement and data retrieval. |
| Institutional Knowledge | The accumulated research history, decision records, lessons learned, and operational experience embedded in Kalaya's systems and documentation. |
| Kill Switch | File-based emergency stop that blocks all order placement immediately without system restart. |
| Monte Carlo | Simulation technique resampling historical trade returns to estimate the distribution of possible outcomes. |
| Portfolio Risk Layer | The risk management layer between strategy validation and capital allocation, responsible for sizing, diversification, exposure, concentration, and regime adjustment. |
| Profit Factor | Gross profit divided by gross loss. ≥ 1.20 net is required for approval. |
| Regime | A persistent state of market behaviour — assessed on growth and inflation axes, or by structure and volatility. |
| Walk-Forward | Out-of-sample testing splitting data chronologically to test strategy stability across time segments. |