Section 1 — General Evaluation Criteria

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Trading algorithms are rule-based strategy constructs that process market data to systematically drive entries/exits, position sizing, and risk controls. These notes summarize key evaluation criteria that go beyond headline returns, focusing on risk severity, backtest credibility, execution/microstructure effects, cost sensitivity, and parameter stability, with the goal of assessing whether historical results can be reproduced under live conditions.

The following metrics provide a baseline framework for evaluating an algorithmic trading strategy’s performance and implementability, treating return generation jointly with risk, validation discipline, execution quality, and operational constraints.

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Maximum Drawdown (MDD)

Measures the largest peak-to-trough decline in the equity curve and serves as a primary indicator of capital durability.

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Curve-Fitting / Overfitting Risk

Refers to excessive adaptation to historical data that inflates in-sample performance while undermining out-of-sample generalizability.

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Backtest Biases

Bias mechanisms—such as look-ahead effects, survivorship bias, information leakage, and data snooping—systematically overstate backtested results.

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CAGR

Summarizes the compound annual growth rate over the evaluation horizon; by itself, it does not directly encode the strategy’s risk profile.

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CAGR-to-Maximum-Drawdown Ratio (CAGR/MDD)

A compact risk-adjusted performance proxy that normalizes compound growth by the maximum drawdown; higher values indicate stronger growth for a given drawdown magnitude.

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Liquidity and Market Impact

Captures execution-driven performance erosion arising from order-book depth constraints, partial fills, and slippage—particularly pronounced in adverse liquidity regimes.

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Trade Statistics

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Transaction-level descriptors (win rate, average win/loss, expectancy, loss-streak behavior, and holding-time distribution) characterize the strategy’s microstructure and failure modes.

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Turnover and Trading Frequency

Portfolio turnover and trading intensity amplify cost sensitivity; elevated turnover often weakens implementability and robustness.

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Parameter Stability and Sensitivity (very brief)

Sharp performance degradation under small parameter perturbations signals brittle optimization.

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Transaction-Cost Sensitivity and Mid-Quote Validity Risk

Commissions constitute a direct cost; however, the more material risk often lies in broker-specific spread dynamics and, critically, whether the mid price (bid+ask)/2 is a defensible proxy for a “fair” and tradable reference price. Microstructural divergence between historical reference prices used in backtests and real-time bid/ask-based execution can structurally impair expected value when mid-quote representativeness deteriorates.

This topic warrants separate treatment under pair trading, where leg-level liquidity/spread regimes and quote synchronization are typically asymmetric. Moreover, an algorithm operates as a continuously running loop: liquidity, volatility, spreads, and peak/trough dynamics evolve over time, so cost and execution assumptions cannot be treated as static.

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Leverage, Broker Constraints, and Dynamic Adaptation

Leverage is a dominant scaling variable—especially in pair and portfolio constructions—and is frequently broker-defined and subject to change. Accordingly, tight coordination between the strategy designer and the broker is operationally critical: when leverage regimes shift, pairs and portfolio weights must be rescaled promptly and systematically, without falling into curve-fitting and while explicitly accounting for cost and microstructure constraints. In this sense, trading algorithms are not static artifacts but systems that require ongoing recalibration as operating conditions evolve.

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Section 2 — Pair Trading vs. Naked (Unhedged) Trading

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Pair trading is a relative-value, hedged construction in which the strategy expresses a view on the spread between two instruments rather than on the outright direction of a single price series. In contrast, a naked trade (i.e., an unhedged position) carries predominantly directional market risk because the P&L is driven mainly by the instrument’s absolute move rather than by a hedged differential.

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2.1 Hedge Ratio Logic: Dollar- and Beta-Hedging

A defining element of pair trading is the hedge ratio—the scaling that attempts to neutralize common risk drivers (e.g., market beta, factor exposure, or dollar exposure). In practice, ratios are often specified as:

• Dollar-neutral (matching gross dollar exposure across legs), and/or

• Beta-neutral (choosing weights to minimize sensitivity to a benchmark or factor), often expressed via dollar beta (exposure × beta).

Example (illustrative): Long 50 SLV and short 130 PSLV reflects a chosen ratio intended to reduce a targeted exposure (e.g., metal-price beta or a proxy for market-related covariance). Critically, the hedge ratio is not static: it should be re-estimated over defined intervals (rolling windows, regime-aware updates, or scheduled recalibration), and some implementations apply dynamic hedging (continuous or event-driven ratio updates). This is typically where a quantitative researcher (quant) adds material value: ratio estimation, stability diagnostics, and robustness controls are non-trivial and directly affect out-of-sample behavior.

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2.2 Market-Risk Reduction and Residual Risk

​A well-constructed pair aims to minimize net market risk by neutralizing shared components of return variation. However, residual risks remain material:

• Basis risk (imperfect co-movement)

• Liquidity asymmetry between legs

• Execution slippage and quote synchronization issues

• Regime shifts that invalidate historical hedging relationships​

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2.3 Broker Constraints and Leverage Mechanics in Pair Trading

​Brokers commonly condition margin/leverage treatment on multiple interacting quantities, including:

• Total gross size of the paired position (sum of leg notionals)

• The ratio structure (e.g., 50 SLV long vs. 130 PSLV short), which affects net exposure and hedge effectiveness

• Size relative to Net Liquidation Value (NLV): leverage and margin requirements are frequently evaluated as a function of (pair gross exposure) / (account NLV), with tighter constraints as this ratio rises or as the hedge is judged less effective

 

Operationally, this reinforces the need for coordination between the strategy designer and the broker’s margin model, particularly when leverage regimes or margin schedules change and the strategy must be rescaled without destabilizing the hedge.

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2.4 Debit/Credit Structure and Financing Costs (U.S. Context)

​Pair trades commonly involve one long leg and one short leg, producing debit/credit balances:

• The long leg typically creates a debit (cash outflow / margin usage)

• The short leg creates a credit (short-sale proceeds), but these proceeds are usually restricted as collateral rather than freely deployable

Financing costs in the U.S. can include:

• Margin interest on net debit balances

• Stock borrow fees (especially for hard-to-borrow shorts)

• Potentially a short rebate or interest treatment on short proceeds (broker- and market-dependent)

These financing terms can materially shift realized performance and should be modeled explicitly in strategy evaluation.

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2.5 Spread Orders vs. Leg Orders and “Theoretical” Quotes

​Many brokers and execution platforms represent pair trades not as two independent orders but as a spread order, e.g.:

50⋅SLV−130⋅PSLV

In this representation, the displayed spread bid/ask is often theoretical, derived from the contemporaneous leg quotes. However, the resulting fills are still mediated by leg-level execution realities (depth, latency, partial fills, slippage). Accordingly, even when the order is placed at a “theoretical” spread level, realized execution can deviate due to microstructure effects and asynchronous quote updates.

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2.6 Naked Trades: Demand and Forward Scope

A naked strategy refers to a non-paired, non-hedged exposure—e.g., being long BTC (or short BTC) without an offsetting hedge designed to neutralize shared risk factors. There is substantial demand for such directional algorithms, but the implementability and risk profile differ sharply from pair trading. While this section is framed around pair structures, it is reasonable to treat non-pair algorithms as a separate design family for future consideration (e.g., potential applications in instruments such as VIX futures), where the risk, execution, and financing mechanics require a different analytical framework.

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Section 3 — Rationale for Operating Through a Custom Backtest Simulator

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Operating through a custom backtest simulator is, in my view, materially more effective than relying on Pine Script or other platform-native backtesting engines for the following reasons:

• Leverage and dynamic leverage compatibility: it allows broker-driven and time-varying leverage constraints to be modeled as first-class components of the simulation.

• Dynamic, conditional position sizing: it supports sizing rules that adapt to signals, volatility, risk brakes, and evolving account state.

• Spread-form pair trading support: it more faithfully represents multi-leg pair structures expressed as a spread (i.e., x⋅A−y⋅B ), including hedge-ratio and leg-weighting logic.
  
  Even if compounded growth is the primary objective, the extent to which accumulated equity is allowed to scale position size should be governed by predefined, confidence-based tiers. This ensures that the position-sizing framework remains applicable—and materially safer—for a new participant onboarding into the algorithm.

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Section 4 — From Theoretical Performance to Practical Deployability

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Reference Benchmarks (Nominal USD; December 15, 2025; CPI through September 2025)

To scale and contextualize strategy performance, I use the following long-horizon references as a baseline: Berkshire Hathaway reports a long-run compounded return of approximately 19.9% (1965–2024), while the S&P 500 with dividends is approximately 10.4% over the same horizon. Over a ~20-year window, the S&P 500 price index implies an annualized CAGR of approximately ~8.77%, U.S. inflation (CPI-U) compounds at approximately ~2.48% annually, and gold (spot, USD) compounds at approximately ~11.35% annually. For equity-level context, approximate ~20-year compounded rates are AAPL ~27% and NVDA ~38% (indicative magnitudes).

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Practical Deployability: Capital Structure, Margin Reality, and Portfolio Integration

Core operational advantage of pair trading (capital efficiency): for an investor holding a well-constructed equity/ETF portfolio, a meaningful portion of that portfolio (often on the order of roughly half of equity, subject to institutional rules and instrument eligibility) can be used as initial and maintenance margin collateral; the paired spread legs can then be carried through standard debit/credit balance mechanics.

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In addition, pair candidates are typically selected from instruments exhibiting high internal correlation, often a cointegration relationship, and—where feasible—constructed with aligned directionality and comparable leverage profiles (unlevered–unlevered or similarly levered). However, these parameters must be validated end-to-end “as if traded at the broker,” because outputs from risk-measurement tools (e.g., risk-navigator-style reporting) have been observed not to match live margin and execution dynamics. Since brokers frequently evaluate the two legs as a paired structure, certain configurations may enable higher effective leverage, lower initial/maintenance margin, and correspondingly improved capital efficiency; moreover, in appropriately chosen pairs, a very strong liquidity advantage can arise relative to naked market trades.

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Within this framework, the “debt” is not treated as a fixed-term obligation with a predetermined repayment date; rather, it is managed as a dynamic margin relationship, adjustable via cash additions or by reducing a portion of existing holdings. That said, margin deterioration can create a risk of forced de-risking and/or position reduction.

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The algorithm operates across seven pairs. Capital usage is assessed pair-by-pair, and partial utilization may be applied by design for certain pairs. Accordingly, each pair’s performance is monitored not only for its contribution to aggregate P&L, but also for its internal consistency and risk–return profile. Unallocated capital may be maintained as a liquid buffer against rising initial/maintenance margin needs and can be allocated, for example, to U.S. Treasuries, gold, and/or related instruments; stablecoins are treated as a last-resort example within this reserve set.

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While pair trading is among the most direct frameworks for reducing broad market exposure, systematic execution is not limited to hedged structures: non-pair, unhedged trades can also be executed using the conservative and unencumbered portion of margin remaining outside the portfolio; however, this approach typically entails lower capital efficiency and higher market risk relative to pair trading.

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Target Orientation and Priority Ordering

The strategic orientation is to be positioned such that, over a sufficiently long horizon, the process can plausibly target 2–3× cumulative performance relative to gold; moreover, if adequate instrument coverage, data quality, and execution infrastructure are available, it is not excluded that the strategy could eventually compete with exceptionally efficient “golden-egg” equities. However, these targets represent an upper-band perspective and should not be treated as the primary short- or medium-term evaluation benchmark.

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The strict first-order priority is not aggressive upside, but robustness: as trades accumulate and sample size expands, the primary objective is to remain positive at a meaningful and operationally safe level, emphasizing sustainability and reproducibility. If large long-horizon equity drawdowns occur, they may also provide material tax-loss harvesting capacity, which can partially neutralize “tax-exempt reporting” optics and reduce the wedge between pre-tax strategy reporting and after-tax investor outcomes. This framing is not a vision of gold substitution; rather, it is intended to function as an additional safety layer within equity diversification excluding commodities.

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Section 5 — Why I Do Not Simply Deploy the Algorithm to Harvest Returns in Isolation, and Why External Engagement Remains Necessary

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Positioning should not be interpreted as an absolute statement or a permanent commitment. Rather, it reflects the current operating view under today’s constraints and objectives; the appropriate approach may evolve as evidence, infrastructure, and market conditions change.

Believing that an algorithm is promising does not automatically imply that the optimal course is to run it solely at a personal scale and passively harvest returns. The first constraint is liquidity risk and liquidity needs: as market conditions, margin regimes, and execution dynamics evolve, sustainable scaling requires a meaningful liquidity buffer, a flexible capital structure, and operational capacity.

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The second driver is growth: the objective is not merely to consume current performance, but to develop and scale the strategy across a broader instrument universe in a manner that supports faster, more realistic compounding and institutionalization.

In this context, the central priority is not to position the algorithm primarily as a rental product for individuals or small institutions, but to obtain access to a more institutional research-and-development ecosystem.

Technology,equipment and operating environment available to a quantitative researcher at institutional scale—higher-quality data, broader market access, more robust testing infrastructure, ongoing peer consultation with other developers, and direct exposure to experienced institutional practices—can materially improve both the scope and the quality of subsequent iterations. Accordingly, the objective is to prioritize accelerated, real-world growth and company-building rather than short-term licensing outcomes.

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This field also has an inherently global footprint: while U.S. markets are a strong starting point, algorithmic and quantitative trades can, in principle, be implemented across many liquid venues worldwide. Related approaches may also extend to FX in theory (treated as out of scope for the current phase). At present, however, the immediate priority is the disciplined validation and maturation of the underlying signals, which will be addressed in the methodology section.

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Finally, it is premature to label the process as definitively “valuable” or “non-valuable” until a longer observation horizon has elapsed and the evidence base has expanded. During this period, the strategy may require controlled modifications to remain implementable under changing constraints—for example, adjusting pair leg weights / lot allocations (or analogous sizing parameters) to reflect shifts in liquidity, margin conditions, or execution quality. For this reason, the operating stance is explicitly iterative and governance-driven: performance claims are deferred until sufficient time and sample size are accumulated, and modifications are introduced under disciplined change control rather than unsystematic, reactive parameter tuning.

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In parallel, the operational approach to debugging and controlled execution is an explicit part of the risk posture. Running the research and execution stack in a Lean-style (event-driven research/execution) environment on self-managed infrastructure—rather than solely in third-party hosted environments—is materially safer from a governance and security standpoint: it reduces unnecessary exposure of strategy logic, preserves confidentiality of proprietary code and data handling, and enables tighter control over access, logging, and incident response. Where appropriate, containerization (e.g., Docker) further improves operational integrity by isolating dependencies, pinning library versions, and making runtime behavior reproducible across development, backtest, and live environments. This approach also supports auditable builds (image hashes), controlled rollouts, and consistent debugging—minimizing “it worked on my machine” failure modes and reducing the probability of production drift.

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Section 6 — Legal Notice and Non-Association Statement

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Non-Association and Unauthorized Use: My name, identity, and any products, methodologies, or strategy structures under my development shall not be referenced, represented, marketed, copied, reproduced, distributed, or otherwise associated with any third-party algorithm developer, platform, script, or distribution channel without my prior explicit written authorization. Any unauthorized association, implication of endorsement, attribution, copying, or derivative use is expressly rejected.