Hypotheses

Faithful port of HighFrequencyStrategy by 51bitquant, MIT licensed. Preserve the source's absolute-price grid, net long/short inventory, limit-order exits, timer counters, cancellation behavior and defaults: grid_step=1.0, stop_multiplier=15.0, trading_size=1.0, max_pos=15.0 and stop_mins=15.0. Trade LTCUSDT.BINANCE using genuine quote ticks and trade ticks; bars must not substitute for ticks. The source's on_bar and on_stop_order callbacks do nothing. Adapt only position sizing: one logical trading_size unit represents 1% of current equity in LTC notional, calculated and frozen when starting a new flat cycle. Maintain source inventory thresholds in logical units. Use one netted position, not independent opposing positions. Reproduce the source's one-second timer with the platform clock, including between market-data events. Stop-loss orders are ordinary limit orders submitted after a quote breach, not protective stop-market orders; preserve this distinction. No additional indicator, regime filter, time exit or hard inventory cap is added. Performance is unverified.

Derived by the Research Lead from Zero's FMZ post: buy the cheaper exchange and sell the richer exchange using synchronized executable depth, then defer rebalancing until the spread contracts. The post supplies a mechanism, not a complete trading contract. This operationalization uses AVAX linear perpetuals on Hyperliquid and OKX instead of pre-funded spot inventories, permitting either hedge direction without borrowing spot coins or transferring inventory. Entry thresholds, exits and sizing are Research Lead choices, not Zero's published rules. Evaluate reconstructed order books and quote ticks; 1-HOUR bars provide bookkeeping and warm-up only. Submit two market orders in the same decision callback, without assuming atomic execution or guaranteed simultaneous fills. No exchange-hosted stops, independent opposing positions on one instrument or selectable same-bar policy are required. Source performance claims remain unverified.

Derived by the Research Lead from Triantafyllopoulos and Montana's Dynamic modeling of mean-reverting spreads for statistical arbitrage: trade deviations in the GDX–GLD price spread only while an online Bayesian TVAR(1) model supports local mean reversion. Use synchronized daily bars on GDX.USEQ and GLD.USEQ, preserving the paper's ETF example. The paper supplies the modeling mechanism, not a validated executable strategy; entry thresholds, exits, sizing and trading parameters below are Research Lead choices. Section 4.5's stated position direction appears inconsistent with convergence of a spread defined as primary minus hedge: this operationalization explicitly shorts a high spread and buys a low spread. Orders execute at the synchronized signal session's close; next-session-open execution is neither requested nor assumed. All performance expectations are unverified. Only one hypothesis is proposed because adaptive estimation and stability monitoring are components of the same trading mechanism.

Derived by the Research Lead from Jaehyung Choi's Physical approach to price momentum and its application to momentum strategy: test the paper's p(3) simple-return ranking through weekly overlapping six-week contrarian portfolios. Preserve the six-week formation period, six-week holding period, decile selection, equal weighting and long-losers/short-winners direction. Adapt the universe to the twenty explicitly declared USEQ stocks, sizing to account equity, and execution to market orders at the first session's daily close each week. Ranking excludes that execution session's return. This is a fixed-universe adaptation, not a reproduction of historical S&P 500 membership or the paper's reported performance. No historical shares outstanding, capitalization, or transaction-value inputs are required. Implement as a multi-instrument FactoryStrategy with synchronized daily bars and a virtual cohort ledger whose aggregate targets become one net position per instrument.

Derived by the Research Lead from Fernando F. Ferreira et al., Information ratio analysis of momentum strategies: test the paper's moving average of causally volatility-normalized weekly log returns, with signed exposure proportional to that average and weekly rebalancing. Use DIA.USEQ, a tradable Dow-Jones Industrial Average ETF proxy, rather than claiming reproduction of the paper's 1896–2013 index experiment. Construct weekly observations from completed daily session bars, using each calendar week's final exchange session close. The source specifies weekly rebalancing and explores formation lookbacks of 1–43 weeks, but the supplied text does not specify the normalization lookback p or a deployable capital scale. Research choices are a baseline 20-week formation window, 20-week normalization window, equity-relative proportional sizing, and protective risk limits. Execute at the final session's close; no next-session-open fill is required. Do not use retrospectively identified BFAST regimes or the fitted 180-week oscillation as trading signals.

Faithful port of HedgeAssetController by hummingbot, Apache-2.0, from https://github.com/hummingbot/hummingbot/blob/9af100d6822da7d2d0291a906c730ef172284ee2/controllers/generic/hedge_asset.py. Maintain a short SOL perpetual position equal to the configured fraction of existing Binance spot SOL inventory. Preserve hedge_ratio=0, min_notional_size=10, cooldown_time=10.0 seconds, leverage=20 and total_amount_quote=0 exactly. The source requests HEDGE mode but manages only a short position; faithfully substitute the platform's single net short position, with BUY adjustments reducing it without opening a long. SOLUSDC spot is the inventory reference; SOLUSDT perpetual is the traded hedge. The controller does not buy spot inventory. For an isolated research fixture, initialize spot SOL inventory worth 20% of starting capital at the first available spot price, deduct its purchase value from cash, and keep its quantity constant; this is an explicitly chosen account initialization, not a source entry rule. Evaluate at each 1-minute perpetual bar close using current account inventory and that close as the market-price proxy. This sampling preserves the strict 10-second cooldown condition but does not reproduce continuous sub-minute polling. With the exact default hedge ratio of zero and no initial short, the correct result is no hedge trades. Do not change the ratio or invent signals to satisfy a trade-count gate.

Derived by the Research Lead from MQL5 Code Base's ZZ Fibo Trader: test a return through a previously broken swing-boundary Fibonacci level, with Wilder Parabolic SAR trailing protection. The article supplies the reverse-breakout mechanism and SAR exit; the following causal swing construction, symmetric long/short interpretation, completed-bar signals and risk settings are Research Lead completions, not claims about the unavailable EA code. Trade LTCUSD.HYPERLIQUID on 4-HOUR bars. Use only confirmed swing points and freeze each setup's level; never trade retrospectively plotted ZigZag points. Submit market entries at the signal bar's close. Intrabar protective stops are matched by the platform. Baseline parameters are pivot_bars=5, reclaim_window_bars=6, sar_step=0.02 and sar_max=0.20.

Derived by the Research Lead from 发明者量化-小小梦’s FMZ post《期货隔夜交易（趋势交易）的金石良言》: follow established rising or falling peaks and troughs, tolerate ordinary pullbacks, limit losses, and let profitable positions run. This operationalization trades LTC and BCH perpetuals independently in both directions on 4-HOUR bars, holding across UTC days without a daily flattening rule. The Research Lead chooses causal swing confirmation, structure-break entries, equity-relative sizing, intrabar protective stops, and delayed trailing protection. These executable rules are not supplied by the author, and the source’s performance assertions are not adopted.

Faithful rule port of Baldovin, Camana, Caraglio, Stella and Caporin's intraday strategy to SPY.USEQ, an executable S&P 500 ETF proxy. Preserve the paper's 10-minute observations, 10:00–16:00 America/New_York window, three conditioning returns, 25% quantiles, crossing entries, crossing exits and same-day liquidation. Construct observations from completed 1-minute bars; never substitute hourly or daily bars. Use the paper's published model coefficients as fixed transferred parameters. This tests its trading rule with transferred coefficients, not a reproduction of its annually recalibrated 15-year historical experiment: the platform's short accumulating minute history cannot reproduce that calibration. Execute market orders at the observed signal close, as in the paper. No next-session-open execution, independent opposing positions or selectable same-bar stop policy is required. Source performance claims are unverified, and modeled spread and impact apply.

Faithful port of Peter_O's educational TradingView strategy, https://www.tradingview.com/script/9Iwinz7I-How-to-use-Leverage-and-Margin-in-PineScript/ (MPL-2.0). Preserve K=13, D=3, Smooth=4, TP=100 ticks, pyramiding=100, both directions, and no protective stop. The source specifies 3000% of equity per entry and margin_long=margin_short=1/30*50=1.6666667%; these are distinct sizing and margin settings. Adapt entry notional to 2000% of current mark-to-market equity, with aggregate exposure capped at the platform's 20x leverage ceiling and its own margin/liquidation model. This is an execution adaptation, not reproduction of TradingView margin calls. The source specifies no instrument or timeframe; select SOLUSD.HYPERLIQUID 4-HOUR bars. Substitute signal-bar-close market fills for Pine's default next-bar-open fills, as required by the capability registry. Plots and shaded oscillator bands are display-only.

Faithful port of FengkieJ's MIT-licensed Jesse SMACrossover: https://github.com/jesse-ai/example-strategies/blob/7c91e0a37bf62165790120d730442e4f6eb00364/SMACrossover/__init__.py. Preserve the exact 50-bar and 200-bar close-price simple moving averages and strict inequality rules. Despite the crossover terminology, the code tests the current SMA relationship, not a crossing event. The source specifies neither an instrument nor a timeframe; this platform experiment selects LTCUSD.HYPERLIQUID on completed 4-HOUR bars. Translate source entries at self.price into market orders at the signal bar's close. Preserve full-balance, fee-adjusted sizing at 1x exposure and exits only on the opposite SMA relationship. Add no stop-loss, target, trailing stop, funding filter or time stop. All performance expectations below are unverified predictions.

Faithful port of QuantRocket's Apache-2.0 VMOTTrend, the Trend component of Alpha Architect's Value/Momentum/Trend approach. Trade SPY.USEQ using daily session-close bars. QuantRocket identifies source SID FIBBG000BDTBL9 as SPY: https://www.quantrocket.com/docs/. Preserve the source's 252-session moving average, 252-session return, weekly Sunday-labeled resampling, equal half-weight hedge signals, and one-session position delay. This is the standalone short hedge component, not the complete VMOT long portfolio. Replace the source's $0.005/share commission with platform USEQ zero commissions and modeled spread/impact. Replace SMART MOC routing with the platform's market execution at the prescribed daily session close; this preserves close-based simulated timing but does not reproduce auction execution. Source: https://github.com/quantrocket-codeload/vmot/blob/4c579d703c4dec99c8e3b0f42031ea2dbf210207/vmot/vmot_trend.py.

Faithful port of MultiTimeframeStrategy by 用Python的交易员, MIT licensed, from https://github.com/51bitquant/howtrader/blob/fcad8e2d86825506b9d33441ab78be853c2c3bdd/examples/strategies/multi_timeframe_strategy.py. Preserve the 5-minute execution clock, 15-minute trend clock, RSI thresholds, SMA defaults, aggressive limit orders and signal-based exits. Adapt only the unspecified market to NEARUSDT.OKX and the original fixed_size=1 quantity to capital-relative sizing. Aggregate completed 1-minute bars into both clocks, invoking the 5-minute callback before the 15-minute callback at shared boundaries, exactly as the source's on_bar ordering does. Consequently, the shared-boundary trading decision uses the previously computed 15-minute trend. Preserve ArrayManager's default 100-bar initialization for each timeframe and its TA-Lib RSI/SMA semantics; verify helper defaults against the pinned source during translation. The source requests 10 days of initialization history. No supplementary signal, stop-loss, target, trailing stop, maximum holding period or reversal-on-the-same-callback is added. Performance is unverified.

Port of 发明者量化-小小梦’s JavaScript版本SuperTrend策略: trade completed-bar SuperTrend direction changes using the article’s trading defaults, ATR period 45 and multiplier 3, on 15-minute bars. The demonstration’s 10/3 settings are not the trading defaults. Preserve the source’s indicator recursion and close-before-reopen behavior. Adapt the historical OKEX BTC quarterly contract to BTCUSDT.OKX, the platform-supported linear perpetual on the same venue; this tests the signal mechanism, not quarterly-contract economics. Replace repeatedly submitted two-tick marketable limits with taker market orders at the signal bar’s close, an execution approximation that does not reproduce FMZ polling or partial fills. Replace one-contract sizing with equity-relative sizing. No extra trend filter, profit target, intrabar stop or time exit is introduced. Source performance claims remain unverified.

Derived by the Research Lead from Alexander Kiselev and Lenya Ryzhik's A simple model for asset price bubble formation and collapse: test whether a sustained speculative advance, followed by a confirmed negative delayed return while price remains elevated, produces persistent downside. Trade AVAX, NEAR and INJ perpetuals independently on 4-HOUR bars. This is an operationalization of the paper's bubble-to-collapse transition, not an author-provided trading strategy or a calibrated reproduction of its stochastic equation. The paper supplies no empirical trading thresholds. Research choices are the delayed-return lookback, elevated-price threshold, collapse confirmation, frozen historical price anchor and holding limit; their permissions are registered below. The anchor represents a historical price reference, not an observed fundamental valuation. All entries and discretionary exits execute at completed signal-bar closes. Protective stops trigger intrabar through platform matching. No exchange-hosted protection, next-session execution or independent opposing positions are required.

Derived by the Research Lead from Anna Ananova, Rama Cont and Renyuan Xu's Model-free Analysis of Dynamic Trading Strategies: trade two-sided excursions of the KO–PEP regression spread and close on return to its frozen reference. Use synchronized 1-HOUR bars on USEQ instead of the paper's unavailable historical second-by-second NYSE midprices. Preserve its five-session regression window and use its reported 3.2-sigma KO–PEP threshold as the baseline, without treating that empirical optimum as verified performance. Add a cost-aware minimum excursion, a bounded spread-loss exit and a time stop. Estimate coefficients before each session only while flat; freeze coefficients, residual scale and quantities throughout each position. This is an adapted experiment, not a reproduction of the paper's empirical results. The supplied full text establishes the trading rules that the abstract alone leaves unspecified.

Derived by the Research Lead from QuantConnect's HistoricalReturnsAlphaModel (Apache-2.0): preserve its default lookback=1, resolution=DAILY and one-day prediction interval. Source: https://github.com/QuantConnect/Lean/blob/856327ff33869cba98393781000c4ab1d2af423d/Algorithm.Framework/Alphas/HistoricalReturnsAlphaModel.py. This is an alpha model with actionable signals, but it does not specify portfolio construction, orders, stops or position sizing. Complete those omissions with independent equity-relative positions and signal-reversal/expiry exits; these additions are platform choices, not QuantConnect's rules. Trade LTC and BCH perpetuals independently using daily close-stamped bars. Preserve signed insight magnitude, including negative magnitudes for DOWN insights. No thresholds, trend filters, funding vetoes or added technical indicators. Market orders execute at the signal bar's close. Maintain same-direction exposure when a fresh daily insight renews it, rather than manufacturing daily round trips.

Derived by the Research Lead from Sonam Srivastava and Ritabrata Bhattacharyya's paper: test whether a causal variance-state classifier improves regime-dependent daily pullback trading on SPY, preserving the paper's equity-market setting. This is a distilled Bollinger-based implementation of its four-regime framework, not a reproduction of its reported returns or complete multi-indicator system. Preserve the stated 250-day triangular moving average, 20-day ATR channel, regime-specific Bollinger widths, EMA10 reference and ATR exit multiples. Complete unspecified Bollinger lookback, model-estimation window, crossover semantics, position sizing and regime-switch reference prices explicitly below. Replace retrospectively smoothed regime probabilities with point-in-time filtered probabilities to remove future information. Trade market orders at the signal session's close; no next-session-open execution is required. All protective stops trigger intrabar through platform matching. The supplied full text supports these rules; author code would still be needed to confirm indicator periods, signal aggregation, Fibonacci construction and exact regime-switch implementation.

Derived by the Research Lead from 43zombiegit's 'Cointegration and hedge ratio': operationalize the question about regression coefficients and trade quantities as a two-stock pairs experiment. The source discusses hedge-ratio interpretation but supplies no executable strategy. Research Lead choices are MSFT/ORCL, daily bars, rolling log-price cointegration estimation, residual reversal entries, frozen trade-level coefficients, and explicit exits. Regress log PRICE LEVELS rather than log returns: return regression estimates return exposure, not a cointegrating relationship. Convert the log-price coefficient into dollar exposure and then shares; never interpret it directly as a share ratio. Trade both spread directions using USEQ margin, with market orders at completed daily closes. No next-session-open execution is required. This tests a software-sector relationship without claiming that these stocks are already known to be cointegrated.

Derived by the Research Lead from Erik Bartoš and Richard Pinčák's Identification of market trends with string and D2-brane maps: test whether the two-endpoint price-history map identifies recurring shapes with informative subsequent returns. This is an operationalization of the supplied full paper, not a reproduction of StringAlgo v.16, whose complete order rules are not disclosed. Use AVAXUSD.HYPERLIQUID 4-HOUR closes as a declared substitute for the paper's FX midpoint samples. Keep the two-endpoint mapping from Equation (3); add a standard nearest-neighbor forecast, explicit trading thresholds, and protective exits. All map windows end at the decision timestamp; historical neighbor outcomes must already be fully observed. Execute market orders at the signal bar's close. Protective stops trigger intrabar through platform matching. Parameters remain frozen after deployment, while the causal historical observation buffer advances.

Derived by the Research Lead from QuantConnect's RsiAlphaModel: preserve its daily Wilder RSI(14), strict 30/70 triggers, 35/65 hysteresis and 14-day insight duration. Source: https://github.com/QuantConnect/Lean/blob/856327ff33869cba98393781000c4ab1d2af423d/Algorithm.Framework/Alphas/RsiAlphaModel.py (QuantConnect, Apache-2.0). The source is an AlphaModel that emits directional insights, not a complete execution strategy. Complete it with equity-relative sizing, opposite-insight reversal and insight-expiry liquidation; these are platform choices, not source rules. Trade XRPUSD.HYPERLIQUID using XRPUSD.HYPERLIQUID-1-DAY-LAST-EXTERNAL. Market orders execute at the signal bar's close. Preserve all source signal defaults without additional filters, targets or protective stops.

Derived by the Research Lead from Kieran Wood, Stephen Roberts and Stefan Zohren's Slow Momentum with Fast Reversion: use online Gaussian-process changepoint severity to switch between persistent-trend exposure and short-horizon contrarian exposure. This is an interpretable operationalization, not a reproduction of the authors' learned LSTM policy or reported performance. Trade AVAX and ATOM independently, long or short, on 1-DAY bars. Preserve the paper's GP changepoint construction and 60-day volatility estimator; replace the learned policy with explicit regime rules and add turnover controls, protective stops and equity-relative exposure limits. These choices are registered below. Execute market orders at signal-bar closes; this is the registry's equivalent of next-bar-open execution on a continuous venue. Parameters remain frozen during each evaluation window and deployment.

Derived by the Research Lead from PyRsquared's 'Meta Labeling for trading opportunities': test whether a secondary classifier improves the economic precision of a high-recall primary opportunity model. The source supplies the two-stage classification idea, not a trading strategy. Research choices are AVAX 4H bars, long/short directional candidates, regularized logistic models, chronological out-of-fold training, and a fixed holding period. AVAXUSD.HYPERLIQUID is the only traded instrument; AVAXUSDT.BINANCE supplies synchronized context features. Orders execute at the signal bar's close. Models and thresholds are trained exclusively within each historical training window and remain frozen throughout its subsequent evaluation window. Compare the filtered strategy against an otherwise identical primary-only control; neither control results nor evaluation labels may alter the registered contract.