Hypotheses

Derived by the Research Lead from Lamoureux and Zhang: test whether increasing power-utility curvature when estimating characteristic-dependent portfolio weights improves subsequent portfolio performance. Use daily USEQ bars, aggregated into monthly observations, for nine established sector ETFs. This is an operationalization of the paper's portfolio-selection mechanism, not a reproduction of its CRSP/Compustat experiment. Retain its 12-month momentum excluding the latest month and 60-month market-model residual volatility. Omit size and book-to-market because historical capitalization and accounting inputs are unavailable; neither is replaced by a price proxy. Choose an equal-weight benchmark, SPY as the market-model reference, constrained exposures, and a shorter estimation sample. Fit coefficients exclusively from pre-evaluation warm-up history and freeze them throughout each evaluation window. Execute monthly rebalances at a completed session's close; no next-session-open execution is required. The supplied full text provides the algorithm, so this proposal does not rely solely on the abstract.

Faithful port of Myquant's Apache-2.0 RSI_STOCK strategy, with daily bars on AAPL.USEQ. Preserve the source's rolling-window RSI, strict thresholds, entry cooldown and close-evaluated profit/loss rules. Configuration defaults retrieved from the repository's master rsi_stock.ini are rsi_period=14, over_buy=85, over_sell=25, hist_size=18, open_max_days=22, is_fixation_stop=0, is_movement_stop=1, stop_fixation_profit=0.35, stop_fixation_loss=0.068, stop_movement_profit=0.068 and open_vol=2000. The pinned commit's INI could not be retrieved, so these values require confirmation against that revision before implementation. Replace fixed 2000-share sizing with equity-relative whole-share sizing; use $100,000 starting capital, 1x exposure and platform spread/impact costs. Preserve the source's prohibition on selling shares bought in the same session as a strategy restriction. All exits are evaluated on completed bars, as in the source; there are no intrabar stops.

Faithful port of FutureNeutralGridStrategy by 51bitquant, MIT-licensed, from https://github.com/51bitquant/howtrader/blob/fcad8e2d86825506b9d33441ab78be853c2c3bdd/howtrader/app/cta_strategy/strategies/future_neutral_grid_strategy.py. Preserve its bid-centered limit grid and order-event replenishment, including its asymmetric short-side replenishment formula. Source defaults are high_price=0.0, low_price=0.0, grid_count=100, order_volume=0.05, max_open_orders=2, and trade_count=0. The two zero price bounds are unconfigured placeholders: executing them literally divides by zero. Explicit platform configuration sets a static range once per evaluation window using only its first valid bid P0: low_price=0.75*P0 and high_price=1.25*P0. These effective bounds never recenter. Trade BNBUSDT.BINANCE on a margin netting account, using actual quote ticks and completed one-minute bars. Adapt only sizing: replace the source's fixed 0.05 units with a constant window-specific quantity representing 0.10% of initial window equity at P0, rounded down to instrument precision. Buy and sell orders change a single net position; they do not require independent opposing positions. No stop, target bracket, trend filter, inventory cap, or out-of-range liquidation is added. The author's performance claims remain unverified.

Derived by the Research Lead from Guang Zhang's paper: test Strategy C, which enters a hedged pair when a filtered spread returns inside its trading boundaries and exits at equilibrium or renewed divergence. Use PEP and KO daily bars on USEQ, retaining the paper's empirical Model II: linear mean-reverting drift with state-dependent Gaussian innovation variance. This is a distilled implementation, not a reproduction of the reported returns. Research Lead additions are a causal 756-session estimation window, explicitly defined volatility-dependent boundaries, equity-relative sizing and protective exits. Replace the paper's simulation-selected boundaries with the platform's training-only, net-of-cost walk-forward selection; do not introduce a second simulation-based parameter search. Execute market orders after both completed daily bars arrive, at the modeled session close. No next-session-open execution is required. Fit model coefficients using only the pre-evaluation training window, then freeze them throughout each evaluation segment. Deployment freezes coefficients and trading parameters. USEQ prices are split-adjusted rather than dividend-adjusted, so this tests price convergence and does not reproduce the paper's adjusted-price economics.

Derived by the Research Lead from Duong Hien Chi Kien and Huynh Thanh Trung's PPO-HRAP: test the interpretable trend-and-VIX exposure prior independently of the learned actor, using the paper's explicitly described rho=1 endpoint. This is an operationalized component experiment, not a reproduction of the reported PPO results. Trade SPY.USEQ on completed daily bars, retaining the stated trend lookbacks, stress thresholds and exposure table. Research Lead choices are conservative lagging of VIX, resolution of the incompletely specified neutral regimes, an exposure-change threshold to limit turnover, and an intrabar protective stop. Market orders execute at the signal session's close; no next-session-open execution is required. The supplied paper provides concrete rules beyond its abstract, but exact PPO reproduction would additionally require the authors' implementation, architecture, preprocessing and simulator timing. No claimed performance is accepted as verified.

Derived by the Research Lead from Bruno P. C. Levy and Hedibert F. Lopes: adapt the paper's dynamic logistic classifier and momentum-model selection to a long-short basket of LTC, BCH and ADA perpetuals. Preserve calendar-month forecasts, lookbacks of 1, 2, 4, 6, 8, 10 and 12 months, monthly holding periods, cutoff 0.5, coefficient-discount grid {0.98, 0.99, 1}, model-forgetting grid {0.99, 1}, and 36 labeled training months. Use daily bars to construct completed UTC calendar-month closes. This is an adaptation of the supplied full paper, not reproduction of its 56-contract results. Added implementation choices are zero-mean Gaussian coefficient priors with identity covariance, deterministic tie-breaking, and conservative exposure caps. Source volatility scaling is retained with a 40% annualized target before caps; retrospective scaling to 10% realized volatility is excluded from executable sizing. Monthly decisions execute at the completed signal bar's close, the platform's supported equivalent of next-bar-open execution on a continuous market. No exchange-hosted protection or selectable same-bar policy is required.

Faithful translation of Myquant's Apache-2.0 MACD_STOCK strategy to MSFT.USEQ, using daily bars and long-only, unleveraged exposure. Preserve the source's signal inequalities, cumulative entry counter, rolling daily price array, and bar-close exit checks. The companion configuration specifies short_term=12, long_term=26, macd_term=9, hist_size=60, openlong_signal=2, is_fixation_stop=0, is_movement_stop=1, stop_fixation_profit=0.20, stop_fixation_loss=0.068 and stop_movement_profit=0.068. These values were recovered from the [author's companion configuration](https://github.com/myquant/strategy/blob/master/MACD-STOCK/python/macd_stock.ini); its identity with the requested pinned revision remains unverified. Venue, instrument, sizing and costs are platform adaptations: replace open_vol=2000 shares with 20% of current equity, rounded down to whole shares and capped by available cash after estimated costs; use $100,000 initial capital, leverage 1, zero commissions and platform spread/impact. The original Chinese-stock restriction to selling yesterday's shares is adapted to USEQ's sellable position quantity. This tests the source mechanism on MSFT, not its original stock pool or claimed performance. All exits execute at the completed bar's close; the source does not implement intrabar protective orders.

Faithful reproduction of Myquant's Apache-2.0 MA trading example, preserving executable behavior rather than repairing its apparent intent. Source: https://github.com/myquant/strategy/blob/17595e6bf4a118e1fa87c90bfb0fd78afa69a60b/MA/python/ma_stock.py. The source computes cross as a Boolean, then tests cross < 0 in both trading branches; that comparison is always false. Moreover, window_size=20 and timeperiod=20 leave ma[-3] undefined under TA-Lib SMA semantics. Consequently, the exact fallback baseline submits no orders. This is a trading strategy with unreachable branches, not a utility and not an economically rejected strategy. The supplied source reads its market and parameters from an INI file whose contents were not supplied or successfully retrieved; its actual configured instrument and overrides remain unknown. AAPL.USEQ is an explicitly selected platform instrument, not an asserted original instrument. Preserve the 60-second fallback cadence as 1-MINUTE bars. Do not silently repair the crossover, extend the rolling buffer, introduce stops, or optimize a corrected version.

Faithful reproduction of DualMA by Myquant, Apache-2.0, at commit 17595e6bf4a118e1fa87c90bfb0fd78afa69a60b. This is a trading strategy, but its supplied Python fallback configuration cannot produce entries: close_buffer has maximum length 20 while algo_action returns whenever its length is below life_timeperiod=30. Preserve that behavior; do not silently enlarge the buffer or shorten the lifetime average. Several required configuration values have no Python defaults, and the accompanying INI was not supplied or successfully retrieved. Proposed platform market mapping is LTCUSDT.BYBIT, with equity-relative sizing and real platform fees; this mapping is an adaptation, not an original source setting. Coding a trade-producing version requires a separately recorded configuration resolution. No performance study was performed.

Faithful translation of Hans123 by Myquant, supplied as Apache-2.0, from https://github.com/myquant/strategy/blob/17595e6bf4a118e1fa87c90bfb0fd78afa69a60b/Hans123/python/Hans123.py. Preserve the actual implementation rather than repairing it into a conventional daily opening-range breakout. The exact source constants are OPEN_VOL=5 and MAX_TRADING_TIMES=3. trade_symbol, open_time, hans_time and ex_time come from Hans123.ini and have no defaults in the supplied Python. The configured bar interval and local timezone are also unspecified. These inputs must be recovered before implementation; do not invent source defaults. Platform instrument mapping is LINKUSDT.OKX, a linear perpetual permitting both directions. Orders are market orders submitted at the signal bar's close. Preserve repeated same-direction additions, strict time comparisons, the effective four-opening limit, and the absence of daily resets. on_tick only stores last_price, which the trading logic never uses.

A testable adaptation of the author's bullish moving-average alignment and pullback entry mechanism to LINKUSDT.BINANCE_SPOT using 4-HOUR bars. Buy a pullback that touches the middle moving average and closes back above it while the bullish stack remains intact. The source leaves periods, sizing and exits unspecified; the explicit rules below complete those elements. Long-only, unleveraged, one position, no pyramiding. Entries execute as market orders at the completed signal bar's close; no historical fill at the moving-average touch is assumed. Protective stops trigger intrabar through platform matching. Source performance claims are not adopted.

Test the article's single concrete mechanism: buy or sell a signed, lowest-variance PCA basket when its standardized-price spread deviates, then close on convergence. Adapt the FX example to LTC, BCH and ETC Hyperliquid perpetuals using synchronized 4-HOUR bars. These related proof-of-work assets provide a testable shared-factor basket without using BTC. Replace broker-lot allocation with equity-relative quantities that reproduce the spread's price sensitivity. Fit statistics using preceding observations, excluding the signal bar, and freeze the component and its normalization during each trade. These are explicit adaptations drawn from the article's exposure-balancing and separate-training-window extensions. Market orders execute at the completed signal bar's close. Execution is sequential, with compensating closures after incomplete entry; atomic execution is not assumed. No profitability claim from the source is adopted.

Adapt the article's single demonstration trading mechanism to DOGEUSD.HYPERLIQUID on 4-HOUR bars: follow the latest large return only when a reliable Hawkes fit indicates self-excitation and current intensity is unusually high. Preserve the causal event extractor, exponential kernel, deterministic maximum-likelihood fit and time-based exit. Add equity-relative sizing and an intrabar protective stop because the source demonstration omits protection. Entries execute at the signal bar's close. The branching ratio measures historical clustering; directional continuation is the hypothesis to test, not a mathematical consequence of Hawkes intensity. No source performance claim is accepted as evidence.

Adapt VLT_TRADER's smallest-range candle mechanism to LINK perpetuals using 4-HOUR bars. After a qualifying compression candle closes, arm buy-stop and sell-stop entries for subsequent intrabar breakouts. Interpret the source's ten points as ten instrument price increments. Explicitly correct its printed sell trigger from low plus ten points to low minus ten points, producing a symmetric downside breakout. Complete the excerpt's unspecified exits and sizing with volatility-based protection, a time exit and equity-relative risk. Stops are platform-matched intrabar orders under the supplied execution registry; offline exchange-hosted protection is not required. Only one net position is allowed.

Test the source's concrete RSI–moving-average reversal mechanism on LINKUSD.HYPERLIQUID using completed 4-HOUR bars, with completed daily bars as confirmation. Preserve symmetric long and short entries. Define the source's ambiguous divergence causally through opposing slopes of equally long price and RSI moving averages; this is an explicit adaptation, not a faithful reproduction of the indicator code. Bollinger bands on RSI remain descriptive and are not an additional entry filter. Complete the source's unspecified exits and sizing with an intrabar protective stop, oscillator-recovery exit, time limit, and equity-relative sizing. Submit market entries at the signal bar's close. No exchange-hosted protection, independent opposing positions, or selectable same-bar fill policy is required. Source performance claims are unverified; no pre-study was performed.

Test whether altcoins with unusually high downside market sensitivity earn compensation over subsequent eight-week holds. Use synchronized 4-HOUR bars for six Hyperliquid perpetuals, buying two high-downside-beta assets and shorting two low-downside-beta assets while neutralizing estimated unconditional market beta. This is a risk-premium hypothesis, not spread convergence or momentum selection. The library's discussion of asymmetric market risk motivated the proposal; the primary reference is [Ang, Chen and Xing, Downside Risk](https://www.nber.org/papers/w11824). Its equity evidence motivates a crypto experiment but does not establish crypto profitability. Entries and scheduled exits use market orders at completed signal-bar closes. Intrabar protective stops are platform-matched, as supported by the capability registry. No next-session fills, opposing positions on one instrument, exchange-hosted protection or selectable same-bar policy are required.

Test whether altcoins with unusually large recent upside outliers subsequently underperform otherwise eligible altcoins with smaller upside outliers. Trade a dollar-neutral long/short pair selected from four fixed Hyperliquid perpetuals using synchronized 4-HOUR bars aggregated into UTC daily closes. Inspiration: Bali, Cakici and Whitelaw document a negative relationship between extreme positive past returns and subsequent stock returns in [Maxing Out: Stocks as Lotteries](https://www.nber.org/papers/w14804). Applying this mechanism to these crypto perpetuals is an untested extension, not a reproduction or an established crypto result. Execute market orders at the signal bar's close. Protective stops trigger intrabar in the platform; exchange-hosted protection is not required. No preliminary performance study is used.

Trade LINK, AVAX and SUI Hyperliquid perpetuals long or short on synchronized 4-HOUR bars after a high-volume range breach fails on both the perpetual and the same coin's Binance spot market. Binance spot instruments are signal-only; all positions trade on Hyperliquid. This is an independently specified adaptation inspired by secretmorina's Post-Absorption VWAP Reversal Engine, not a faithful port: https://www.tradingview.com/script/j6iKZmCf-Post-Absorption-VWAP-Reversal-Engine-V1-6/. OHLCV rejection is an absorption proxy, not direct evidence of resting liquidity. Market entries execute at the confirmation bar's close; protective stops trigger intrabar in the platform. No next-session fills, exchange-hosted protection, opposing positions on one instrument or selectable same-bar policy are required. Use each asset's actual available common history, capped by Hyperliquid's approximately 5000-candle retrieval window; do not assume equal listing dates or a full 2.3 years for every asset.

Test whether persistent differences in spot volume-weighted closing pressure predict several days of relative performance. Trade FILUSD.HYPERLIQUID against NEARUSDT.BYBIT using synchronized 4-HOUR bars; Binance spot bars provide signals only. Buy the asset showing accumulation and short the asset showing distribution, with equal dollar notionals. Closing pressure is an OHLCV proxy, not observed aggressor flow. Submit market orders at the synchronized signal close; this uses the registry's faithful equivalent for next-bar-open execution on continuous markets. No atomic cross-venue execution or exchange-hosted protection is assumed.

Test whether demand for amplified crypto-market exposure overprices high-beta altcoins relative to low-beta altcoins. On synchronized 4-HOUR bars, buy two low-beta perpetuals and short two high-beta perpetuals from a fixed six-name universe, balancing estimated market beta and holding each basket for 28 days. This adapts the leverage-constraint premise of [Frazzini and Pedersen, Betting Against Beta](https://www.aqr.com/Insights/Research/Journal-Article/Betting-Against-Beta), encountered through the local knowledge library's AQR factor references. The paper's evidence does not establish an edge in crypto; that extension is this experiment's falsifiable premise. Execute market orders at the signal bar's close, the registry's faithful equivalent to next-bar-open execution on a continuous venue. Protective stops trigger intrabar through platform matching. No independent opposing positions on the same instrument, exchange-hosted protection, or selectable same-bar policy is required.

This is a cross-sectional return-seasonality strategy. Sources: Heston & Sadka (2008, JFE), and Keloharju, Linnainmaa & Nyberg (2016, JF), who find the effect in country indices, sectors and other asset classes. The universe is a FIXED, rule-defined panel of 18 US-listed ETFs that are in the USEQ catalog with long daily history: the 9 original Select Sector SPDRs (XLB, XLE, XLF, XLI, XLK, XLP, XLU, XLV, XLY; from 1998-12) and 9 country/region iShares (EWJ, EWG, EWU from 1996; EWZ, EWT, EWY from 2000; EFA 2001; EEM 2003; FXI 2004). Because these are ETFs, there is no point-in-time membership problem (L190). An ETF becomes eligible once it has at least min_years (5) complete calendar years of history. Signal for ETF i and upcoming month m: seasonal_excess = mean return of i in calendar month m over the last lookback_years years, minus its mean return in the other months of those years (the KLN seasonal-minus-non-seasonal form, which removes the ETF's general drift). On the last trading session of each month (from src/data/market_calendar.py, half-days included), rank the eligible ETFs. Go long the top_k and short the bottom_k, dollar-neutral. Each leg is sized by inverse 60-day volatility, and gross exposure is 100% of equity on the 2x Reg-T account. Orders fill at the month-end close. There is no next-session-open hook, so the strategy trades at that close and holds through the whole next month. Each leg also has a 15% intrabar protective stop (a platform-matched reduce-only stop_market). This mechanism has not been run in the corpus. Prior seasonality runs were single-asset day-of-week and turn-of-month timers on BTC, QQQ and SPY. The strategy is pure OHLCV with no BTC and no Binance.