Soomario Strategies is a systematic trading platform operated by a single principal, with all production logic running on dedicated infrastructure across three execution venues — Hyperliquid for perpetual futures (cryptocurrency and HIP-3 equity perpetuals), Deribit for crypto options, and OKX for copy-trading distribution. The platform comprises nine strategies designed around a common thesis: most edge in retail-accessible markets is already in public data — observable in liquidation books, unlock calendars, options chains, and price action — and the structural inefficiency lies almost entirely in how few participants apply that data systematically, across many assets, with discipline, in the face of regimes that punish discretion.
The platform addresses two distinct audiences. Eight strategies are vault products: capital is contributed to an on-chain or exchange-side vault, the strategy executes against pooled equity, and a 10% performance fee is taken on net profits — no management fee, no lockup, no aggregator. The ninth, Accumulator, is a subscription signal product at $7 per month that delivers daily quantitative buy recommendations to retail investors who execute the trades themselves. Both audiences receive the same standard of analytical rigour. They differ only in custody, capital model, and degree of automation.
This document is a unified credential for the platform. It describes how the strategies think, what they look at, where their respective edges sit, and — equally important — what we do not claim. We do not publish exact parameters. We do not publish backtest summary statistics in the body of this paper. We do not promise returns. What follows is a full description of the analytical vocabulary the platform speaks, the structural reasoning behind each strategy, and the controls that bound risk at every layer.
Vault Products — 10% performance fee, no management fee
| Strategy | Design Principle | Venue | Asset Class | Status |
|---|---|---|---|---|
| Alpha | Relative-value spread anchored on a quality long, paired with unlock-driven shorts | Hyperliquid | Crypto perpetuals | ● Live · April 2026 |
| Max Pain | Multi-asset mean reversion at exhausted liquidation cascades | Hyperliquid | 18 crypto perpetuals | ● Live · Late 2025 |
| Elite | Concentrated momentum capture on two highest-conviction perp markets | Hyperliquid | HYPE / AVAX perpetuals | ● Live · January 2026 |
| Aphelion | Multi-signal long-bias accumulator with pre-placed limit defense | OKX (copy) + Hyperliquid | Crypto + US equity perpetuals | ● Live · April 2026 |
| Premia | Volatility-harvesting covered call engine across major crypto holdings | Deribit | Crypto options | ○ Mainnet pending |
| Rotation | Three structurally decoupled sleeves: equity rotation, crypto core, hedged shorts | Hyperliquid | Equity + crypto perpetuals | ◐ Paper · 90-day validation |
| Zones | Bi-directional DCA at cross-exchange liquidation max-pain zones | Hyperliquid | 21 crypto perpetuals | ● Live · March 2026 |
| Farms | Liquidation-zone grid with Pionex-style seed runner, long-only by construction | Hyperliquid | ~50 crypto perpetuals | ◐ Paper · validation |
Subscription Product — $7/month, 7-day free trial via Whop
| Product | Design Principle | Delivery | Asset Coverage | Status |
|---|---|---|---|---|
| Accumulator | Quantitative signals to scale DCA buys by oversold opportunity, personalised to budget | Telegram + Web Push | 14 curated equities & crypto + unlimited watchlist | ● Live · February 2026 |
The eight vault strategies are decoupled by design. They use different signal categories, target different inefficiencies, and reach their drawdowns at different times. A subscriber to the Accumulator subscription is also a qualified prospect for the vault products — and the vault products' transparency (every position visible on-chain, in real time) is itself the cleanest reference any subscription buyer could ask for.
The data is public. The systematic application is rare.
The platform's commitments are not aspirations — they are constraints encoded into every strategy on the platform. The list below is what we do not change in any individual strategy's design without an explicit reason and a documented review.
Every signal that opens or closes a position is generated by code. The operator's discretion is reserved for parameter review, universe additions and removals, and strategy-level governance — not for individual trade decisions. Discretionary intervention introduces a class of failure that is invisible in backtest and impossible to audit in production. The platform avoids it by structure.
Most retail-accessible automated strategies run the same settings across every asset they touch. One RSI period. One stop band. One leverage tier. This is computationally cheap and produces a product that can be deployed anywhere — and it leaves real edge unclaimed on every asset that doesn't match the implicit reference. Soomario strategies that span multiple markets calibrate per asset. A high-velocity meme-style coin runs on different settings than an institutionally-traded major. The discipline is expensive in research time. It is cheap in capital.
Several strategies place their primary risk controls — stops, defense limits, take-profit triggers — directly on the exchange's matching engine, where they fire whether the bot is alive or not. A stop-loss that depends on the bot's heartbeat has a single point of failure between signal and exit. Whenever the venue allows it (Hyperliquid does, Deribit does), our stops live there. The bot's monitoring loop is then a redundancy layer, not a sole one.
Seven of the eight vault products run on Hyperliquid's native vault contract, where depositor capital is custodied at the protocol level and every entry, exit, and balance change is verifiable on-chain in real time. The eighth, Premia, runs through Deribit's exchange-side sub-account model — the closest analogue available for crypto options. Investors do not rely on an LP statement, a quarterly report, or our word for anything. The system is its own audit.
We publish vault addresses, dashboards, infrastructure stack, design principles, and the full categorical description of every analytical input the platform uses. We do not publish exact RSI periods, threshold values, weighting matrices, leverage tiers, DCA percentages, hard-stop bands, or scoring formulas. Sophisticated investors do not need these to evaluate a strategy. Less-sophisticated investors are not protected by them. And the platform's edge is durable only to the extent it is not handed away. The rule is consistent: describe the discipline, not the digits.
Position-size caps, leverage ceilings, no-stacking rules, kill-switch thresholds, capital caps per sleeve — these are not knobs the operator turns optimistically based on recent performance. They are constraints encoded into the system that bound the worst possible outcome on any single trade and on the portfolio in aggregate. A strategy that would be more profitable with looser constraints, but is engineered to refuse to loosen them, is a strategy whose worst-case behaviour is computable in advance.
Every strategy that automates anything important automates it twice. Signal generation is decoupled from execution. Bot-side stops back exchange-side stops. State is reconciled against ground-truth exchange data on every cycle. Two independent components must both fail before a position runs unmanaged. This adds moving parts, and it is worth every line of code that implements it.
It is not novelty-seeking. None of the building blocks the platform uses are new. RSI, moving averages, Z-scores, liquidation maps, unlock calendars, supertrend, and farmability detection have all existed for years. What is new is not the parts but the discipline of combining them, applying them across many assets, encoding the constraints in production, and refusing to tune them retroactively to flatter recent performance. The edge is operational, not informational.
This section is the platform's vocabulary. It defines every category of analytical input the strategies use, what each reveals about a market, and which strategies actually use it. The categories are not weighted equally across strategies — some strategies use one category as their dominant signal and the others as confirmation; some use four or five categories layered as gates that all must agree before capital deploys.
What this section does not do is publish exact indicator periods, weights, thresholds, or scoring formulas. The categorical description below is sufficient for a sophisticated investor to evaluate whether the platform's analytical stance fits their understanding of the markets it trades. The exact numerical implementation is not.
If you are reading this section to find a parameter table, you will not find one. You will find every category of analysis the platform applies, and the rationale for applying it.
The Relative Strength Index is the platform's most universally deployed indicator. RSI measures the magnitude of recent gains against recent losses on a 0–100 scale, identifying when an asset is statistically stretched in one direction. The platform uses Wilder's recursive smoothing method (RMA) — the same calculation native to TradingView — to ensure that any signal that fires on the live engine fires on a chart the operator can manually re-derive.
RSI is used differently across strategies. Mean-reversion strategies (Max Pain, Zones) treat extreme RSI readings as evidence that the move is exhausted. Momentum strategies (Elite) treat RSI crossovers rather than absolute levels — a transition through a threshold is treated as a directional re-anchoring after consolidation, not a contrarian fade. Accumulators (Aphelion, the Accumulator product) treat low RSI on confirmed daily candles as part of a multi-signal oversold filter. Period lengths and threshold values are calibrated per asset; the platform does not run one RSI on every market.
Volume tells the platform what level of conviction a price move is happening on. A move on average volume is more likely to be a normal mean-reverting fluctuation; the same percentage move on volume two or three standard deviations above the recent baseline is more likely to be a flow event — capitulation, forced liquidation, or a regime-changing shift in positioning. The platform measures volume anomaly through a Z-score normalisation, which compares the current bar against the asset's own recent history rather than against a universal scale.
Aphelion specifically tracks up-volume versus down-volume separately — a directional money flow read — to flag capitulation candles where down-volume dominates the bar even at oversold prices. Max Pain uses a custom volume-weighted price excursion Z-score on its three-indicator confluence. Zones incorporates volume anomalies into its conviction gate alongside open-interest signals. The platform does not use traditional named "money flow" indicators (CMF, MFI) directly; the Z-score formulation is more flexible and produces fewer false signals across heterogeneous assets.
A Z-score is the number of standard deviations a current observation sits from the mean of a chosen lookback window. The platform uses Z-scores extensively because they are the most disciplined way to ask: "is what's happening right now actually unusual?" A 5% drop on an asset that routinely moves 8% in a day is a normal move; the same 5% drop on an asset that rarely moves more than 2% is a regime event. Absolute percentage thresholds cannot make this distinction. Z-scores can.
The platform applies Z-scores to volume (as described above), to price excursions over short windows, and to longer-horizon price distributions over multi-month windows. Rotation's hedged-shorts validator specifically requires statistical overextension on a multi-month Z-score before capital deploys against an unlock or weakness candidate — this filters out cases where price looks weak on a short timeframe but is well within the asset's longer-term distribution. The Accumulator product applies a price-distribution Z-score to crypto signals, where the wider volatility regime makes statistical normalisation more informative than for stocks.
The platform reads trend direction through two distinct lenses. Long-period moving averages (typically 50-period for crypto, 300-period for equities) define an asset's secular position — is current price extended below the long trend (an accumulation opportunity) or above it (a distribution context)? Aphelion and the Accumulator product use moving-average extension as a third gate alongside RSI and Z-score; they will not signal an accumulation unless the asset is meaningfully below its long-period reference.
Supertrend is a volatility-adaptive directional indicator that draws a moving stop line which flips when price crosses it decisively. Supertrend alone whipsaws in sideways markets, which is why the platform never uses it as a sole signal — but as a confirmation layer on top of momentum and volume conditions, it provides directional clarity. Max Pain demands a Supertrend flip in addition to RSI and Z-score conditions before any trade fires. Premia uses Supertrend as a directional gate for call selling — a fresh bullish flip will block a call sell entirely, regardless of how attractive the premium looks. Trend direction is a context filter, not a primary entry signal anywhere on the platform.
In perpetual futures markets, open interest reveals the size of leveraged positioning at every venue, and funding rates reveal which side of the book is paying the other to hold their positions. These are direct readings of crowdedness — they tell the platform whether longs or shorts are the structurally fragile side of any given market.
Alpha's short-selection engine reads positioning crowdedness directly: assets where longs are paying shorts at unusually high rates are structurally favourable to short, since the funding flow itself pays the trade while the engine waits for the unlock catalyst to materialise. Zones, Farms, and Rotation's crypto core sleeve all use the long/short open-interest skew as a secondary input — a short-heavy book is structurally favourable for accumulation entries, since shorts covering against a bounce supply the buying pressure that translates a DCA fill into realised P&L. These are not primary signals on any of these strategies; they are confirmation layers that flag when a primary signal is structurally well-supported.
This is the platform's most distinctive analytical category, and it is the one that several strategies are explicitly built around. Liquidation density data — derived from aggregated open-interest readings across multiple major centralised exchanges — maps the price levels at which clusters of leveraged positions would be forcibly closed if hit. These clusters are not theoretical; they are real positions held by real traders, and when price reaches the cluster, the resulting forced flow creates a predictable price magnet.
The platform computes a Long Max Pain level (the price below current market where leveraged longs are most concentrated) and a Short Max Pain level (the corresponding cluster above). Zones uses these as the primary anchor for its bi-directional DCA grid. Farms uses them to place its long-only oscillation grids. Rotation's crypto core sleeve uses Long Max Pain as the anchor for its multi-level entry grid. Premia incorporates the same data to place call strikes above the upside cascade levels — placing a strike beyond where forced liquidations would accelerate a rally is a simple, profound improvement on placing strikes purely by percentage OTM. Even Max Pain and Alpha, which do not use Max Pain zones as direct entry anchors, use the underlying liquidation data to inform asset selection — Max Pain (the strategy) is named for the broader thesis that exhausted cascades reverse.
The data itself is publicly observable on services like Coinglass. The platform's edge is in computing the zones from primary exchange feeds rather than scraping aggregators, in tracking 21+ assets in parallel, and in trading the zones as a portfolio rather than as anecdotal individual setups.
The Alternative.me Fear & Greed index is a daily synthetic reading of broad crypto market sentiment, computed from volatility, momentum, social media volume, dominance, and survey inputs. It is publicly available, updated daily, and meaningfully predictive of regime behaviour even when it is poor at predicting next-day price.
Premia uses the index as a regime filter: when fear is elevated, options premiums inflate as hedgers pay up for protection, which is precisely when the strategy's volatility-overpricing edge is largest. The engine is more willing to sell calls during fear regimes than greed regimes — not because the directional thesis changes, but because the premium harvested per unit of risk is structurally better. Zones uses the index as one input to its conviction gate, where extreme fear readings combined with deep liquidation-zone touches produce higher-conviction long entries than either signal alone would justify.
Distinct from a simple moving-average crossover, price-vs-trend extension measures the magnitude by which current price sits below (or above) its long-period reference line. The reading is normalised to give an apples-to-apples score across assets with different volatility profiles. A Bitcoin sitting 8% below its 50-day moving average is a different signal from a low-volatility large-cap equity sitting 8% below its 300-day reference.
Aphelion uses extension below the long MA as a third confirmation layer — RSI oversold and capitulation volume and meaningful below-MA extension are required before any signal fires. The Accumulator product uses extension below the long MA as one of the two or three weighted inputs into its 0–100 strength score. Rotation's Fastest Horse equity sleeve uses moving-average context as part of its technical timing layer to identify favourable entries on otherwise-fundamentally-screened candidates. Across all uses, the principle is consistent: extension is a measure of how far the asset is from its trend, not just which side of the trend it sits on.
Volatility is not a signal the platform trades on directly — it is a context every strategy adapts to. The implementations vary. Max Pain calibrates per-coin RSI periods and hard-stop bands against each asset's measured volatility profile; a high-velocity meme-style asset gets faster, tighter parameters than an institutionally-traded major. Farms uses Average True Range as the basis for its trailing-stop on the seed runner — wider-zone assets get wider trails. Rotation's crypto core sleeve spaces its DCA levels using a volatility-adjusted multiple of the asset's recent range rather than fixed percentages.
The platform does not trade volatility expansion or compression as a primary signal. It trades the underlying inefficiencies through the lens of each asset's volatility, which is why per-asset tuning is one of the platform's core philosophical commitments. A single set of parameters across heterogeneous volatility regimes is, by definition, suboptimal on every asset. The platform refuses that shortcut.
A signal that fires on a single timeframe — say, a 1-hour RSI extreme — is more vulnerable to noise than the same signal that fires when shorter, medium, and longer timeframes all agree. The platform reads multi-timeframe confluence in two ways. First, by demanding that range structures align: Premia's farmability analysis specifically requires that an asset's short-term, medium-term, and long-term ranges converge before the engine considers selling calls against it — a coin with a clean range on the 1-hour but no defined structure on the daily is a worse candidate than a coin where every timeframe agrees on where support and resistance sit.
Second, by running asymmetric cycle cadences across sleeves of the same strategy. Rotation's three sleeves each operate on their own cadence — equity rotation on a weekly cycle, crypto core on a multi-hour cycle, hedged shorts on a faster scan — so that no single market condition triggers all three sleeves to act simultaneously. This is the timing-side analogue of position-sizing diversification. Confluence is treated as evidence that a signal is durable; absence of confluence is treated as a reason to wait.
Some strategies need an asset to oscillate rather than trend. Grid trading is the canonical example — every round trip realised by a grid is profit, and a grid that opens on a strongly trending asset will see only one-sided fills, accumulating an unbalanced inventory. Farmability detection is the platform's term for the composite measurement that identifies which assets are currently range-trading, with what amplitude, and with how much liquidity available at the boundaries of the range.
Farms scores ~50 perpetual markets on a multi-factor farmability composite — zone width relative to spot, 24-hour volume, open-interest depth, range alignment across timeframes, and long/short skew — and refuses to deploy a grid unless price is also positioned safely within the zone (specifically, near the lower boundary, not the upper). Premia uses farmability as a secondary input on its strike-selection engine: a coin trading in a tight, well-defined range is a better candidate for selling calls than a coin breaking out of consolidation, even if both have rich premium. The discipline is: farmability is necessary, not sufficient. Other gates must also clear.
The platform treats certain calendar events as primary signals on the strategies that are designed to exploit them. Token unlocks — the scheduled release of previously-locked supply to teams, investors, or ecosystems — are publicly disclosed in every project's tokenomics, but most participants either ignore them or trade them anecdotally. Alpha treats unlocks as the dominant signal in its short-selection scoring, weighted by recipient class (team and investor unlocks score higher than ecosystem ones), proximity (near-term unlocks score higher than distant ones), and size (large unlocks relative to circulating supply score higher than small ones). The strategy demands corroboration from at least one technical or positioning signal before deploying capital, but the unlock calendar is the prior.
Rotation's hedged-shorts sleeve uses the same unlock calendar as one of two primary candidate sources, alongside the technical-weakness screener. Options expiries are a separate event-driven category that informs Premia's roll mechanics — the strategy favours 14–45 day expirations specifically to capture the steepest section of the time-decay curve while staying clear of gamma-driven volatility near expiry. Calendar events are treated as deterministic context the strategy plans around, not as lagging indicators.
This is the most distinctive analytical category on the platform, and it is used by exactly one sleeve of one strategy — by design. Rotation's Fastest Horse equity rotation engine is built on a deliberate premise: the highest-return equity allocation over a three-to-five-year window is rarely the stock with the best recent momentum or the cheapest current multiple — it is the stock with the largest credibly-modelled upside to a long-horizon thesis target.
The dominant scoring axis is therefore "visionary upside" — sourced from credible thesis-driven research (specialist analysts publishing multi-year price targets attached to a documented thesis, profit-growth aggregates from Wall Street consensus over multi-year windows). Visionary targets are the heaviest score axis specifically because they are the hardest to fake. Wall Street consensus drifts with the news cycle. Technical indicators flip on any multi-day swing. A genuine three-to-five-year price target — sourced, dated, attached to a published thesis — changes slowly and reflects real conviction. The category is mentioned here because a sophisticated reader of this whitepaper would notice its absence from a purely technical analytical toolkit and reasonably ask whether the platform handles long-horizon equity exposure at all. It does. It does so on exactly one sleeve, with explicit rebalancing hysteresis to prevent thrashing on small score changes. The information about this sleeve's exact scoring weights, hysteresis threshold, and slot count are not published.
Every strategy on the platform reconciles its internal position state against ground-truth exchange data on every cycle. This is not an analytical input to a trading decision — it is the substrate that makes any other analytical input trustworthy. In algorithmic trading, the bug that destroys an account is rarely a bad signal. It is the bug where the bot believes it has a position the exchange does not recognise, or believes it does not have one when it does.
Aphelion's reconciliation loop runs every five minutes against Hyperliquid and OKX. Premia cross-checks its journal against Deribit positions every 30-minute scan and bidirectionally adopts orphan exchange positions or auto-cancels phantom journal entries. Zones reconciles against three independent sources every cycle (clearinghouse state, fills history, local positions file) and excludes equity readings outside a sanity band as glitches. Alpha's reconcile guard requires a force-refreshed exchange read before any state-changing close action is taken; if the exchange contradicts the close decision, the close is aborted and the system waits for a clean read. State integrity is a category because we treat it as one. It deserves the prominence.
The fourteen categories above are the full vocabulary of the platform. No strategy uses all of them; no category is used by every strategy. What every strategy shares is the principle that multiple categorical signals must align before capital deploys — single-category signals are noise, and the discipline of demanding agreement across categories is what converts public observations into a tradeable edge.
The next nine chapters describe each strategy on the platform in turn — its identity, status, venue, asset universe, the structural inefficiency it targets, the analytical categories from Section III that it actually uses, the lifecycle by which positions are opened and closed, the risk controls bounding worst-case behaviour, and the honest limits we acknowledge for each.
The chapters are deliberately uniform in structure to make cross-strategy comparison easy. Reading them in sequence is the fastest way to build an integrated picture of the platform; reading any one in isolation is sufficient to evaluate that strategy on its own terms.
Each strategy is a complete answer to a different question. Together they form a portfolio whose drawdowns are not synchronised.
The order of the chapters is roughly thematic rather than chronological — Alpha and Max Pain (the two most operationally-mature crypto strategies), then Elite (the concentrated momentum capture), then Aphelion (the equity-and-crypto accumulator), then Premia (the only options product), then Rotation (the multi-sleeve composite), then Zones and Farms (the two zone-anchored strategies), and finally Accumulator (the subscription product, treated separately because its mechanics and audience differ).
Alpha pairs a long position in HYPE — Hyperliquid's native token, the platform's strongest standalone perpetual exchange — with a basket of systematically selected short positions on weakening altcoins. The thesis underneath the spread is that token unlocks create predictable selling pressure, and most of the market does not trade them systematically. Recipients of newly unlocked tokens — teams, investors, ecosystems — sell some portion of their allocation, and that supply event is structurally bearish for any project whose current holders are not aggressively absorbing it. Alpha identifies these setups, sizes them against confirming positioning and technical signals, and pairs them with a long anchor that profits from quality outperformance.
From the toolkit in Section III, Alpha uses event-driven signals (token unlocks) as its dominant input, with the unlock calendar curated and refreshed weekly. Momentum oscillators identify candidates that have rallied beyond reasonable mean-reversion bounds — assets where the unlock-driven supply event has somewhere to land. Open interest and funding rates read positioning crowdedness directly: assets where longs are paying shorts at unusually elevated rates are structurally favourable to short, since the funding flow itself pays the trade while the engine waits. Liquidation density maps where forced liquidations would fire below current price — assets with heavy long-side liquidation potential are structurally fragile.
The composite score weights toward unlock proximity but requires corroboration from at least one secondary signal. A high score alone is not enough; the engine demands either a near-term unlock or strong technical overextension before opening a position.
The long anchor in HYPE is established through a layered DCA approach — an initial seed at the time of opening, with reserve capital deployed if price tests lower zones around a longer-term mean. Once profitable, the position transitions through a trail-and-defend lifecycle: peaks are tracked, partial exits trim the position as profit grows, and a buy-back marker re-enters opportunistically if a defended floor holds.
The short side operates as a slot-based sleeve. A small fixed number of slots is available; each represents a fraction of equity. New positions enter in two stages — an initial probe when a candidate clears the score threshold, and a conviction add only if the unlock catalyst becomes imminent or price has rallied further into overextension territory. The conviction add has an explicit rejection condition: if price has rallied too far past the initial entry, the second tranche is cancelled. Excessive pump suggests the short thesis may be breaking.
Every position has a reduce-only stop-market trigger placed directly on Hyperliquid at the moment of entry. This is the most consequential design choice in the system. Stops on the exchange fire whether the bot is alive, reachable, rate-limited, or restarting. The bot independently monitors every position every five minutes for the same threshold — two layers, same target. A reconcile guard requires a fresh, force-refreshed exchange read before any state-changing close action is taken, preventing a common failure mode where API drift triggers erroneous closes. Profitable shorts are exited in tranches at successive drawdown levels, leaving a small residual position open to capture extended downtrends.
Alpha is not a high-frequency strategy — the pipeline scans every four hours. It is not a directional macro bet. It is not capacity-unlimited; the slot-based approach naturally constrains size, and there is a ceiling lower than most discretionary strategies. The edge is statistical, not deterministic. Some unlock theses do not play out. Drawdowns happen.
In leveraged perpetual markets, forced liquidation flow has a finite supply at any price zone, and once exhausted, the asymmetry that drove the cascade disappears and price reverts. When over-leveraged longs cluster at a level and that level breaks, forced selling accelerates the move briefly and then exhausts — long-side liquidations finish, organic flow re-balances, and the asset reverts off its overshoot. The same logic applies symmetrically to upside squeezes. Most participants either chase the cascade direction or trade reversals anecdotally on a single chart. Max Pain demands triple confirmation across momentum, volume anomaly, and trend reversal, applied across eighteen assets every bar, with parameters tuned per asset rather than uniformly.
Max Pain uses momentum oscillators (Wilder-smoothed RSI, period and thresholds tuned per asset), volume anomaly (a custom Z-Score measuring volume-weighted price excursion against recent baseline — Z-scores of three or more typically mark forced-flow events rather than organic positioning), and trend direction confirmation (Supertrend flip on the same bar). The three indicators are combined as a hard AND, not a weighted sum — all three must align for a signal to fire. Each indicator alone produces too many false signals to trade profitably; the intersection is rare, and the rare bars are the set the strategy trades.
The hybrid design — TradingView for signal, Python bot for execution — is deliberate. Earlier versions ran indicator math locally and produced live trades that did not match the Pine-script backtest the strategy was sized against. Even small implementation differences in RSI smoothing or Supertrend lookback compounded into materially different signal sets. The current architecture uses the same Pine Script that produced the multi-year backtest, bit-identical, no translation layer. Two independent components must both fail before a position runs unmanaged: a TradingView dropped webhook does not matter if the bot's hard stop is functional; a bot crash does not matter if a TradingView exit signal fires on the next bar close.
The eighteen coins are grouped into three tiers by volatility, liquidity, and historical drawdown profile. Tier 1 trades most aggressively — higher allocation, full leverage cap. Tier 3 carries smaller allocation and reduced leverage. Maximum leverage anywhere in the system is 3x. If a coin holds an open position, no second same-direction position opens; a new opposing signal closes the existing position and opens the reverse in one atomic transaction. There is no stacking and no averaging into losers.
Every forty-five seconds, the bot queries every position and computes unrealised P&L. If any position has crossed its per-coin hard-stop threshold, the bot closes immediately at market — without waiting for TradingView, without waiting for the next bar close. The hard stop is structural, not a signal. Per-coin thresholds are calibrated to each asset's volatility profile and sit well inside Hyperliquid's liquidation buffer; the hard stop fires before liquidation does, always. Two-path exit architecture means TradingView exit signals fire at bar close, and the bot's hard stop fires inside the bar — either path terminates the position.
Not high-frequency — signal evaluation is on bar close, average hold roughly two and a half days. Not a long-only beta play; the strategy makes money in both directions in the historical record. Not parameter-stable indefinitely — markets change, parameters that worked for one regime are not guaranteed to work in the next, and per-asset performance is monitored continuously for degradation. Not foolproof.
Elite trades two assets — HYPE and AVAX — through an RSI-crossover engine. The strategy does not predict prices. It waits for a specific, repeatable condition to appear in the data, takes a position, and lets a trailing stop decide when to exit. Most of the time the engine is doing nothing, and that inactivity is part of the design. The edge it exploits is structural inattention to crossover-confirmed momentum on liquid perp markets — most participants act on RSI levels rather than crossovers, and they get chopped up in trending markets where RSI stays overbought or oversold for days. Crossover-based trading with bar-close confirmation ignores absolute levels and waits for the directional re-anchoring that happens after consolidation.
Elite uses a single primary signal — momentum oscillators — calibrated per asset (HYPE on a 4-hour bar, AVAX on a 1-hour bar). The Wilder RMA smoothing matches TradingView's native implementation to four decimal places, which matters because retail RSI implementations vary and the levels that work in backtest only work in live trading if the smoothing is identical. Crossover detection runs on the most recent two confirmed bars only; the engine never acts intra-bar.
Elite runs as two parallel position streams managed independently. HYPE carries the larger allocation share at 2x leverage; AVAX carries a smaller allocation at 1x leverage. Position size is computed as a fixed percentage of vault equity at the moment of entry, so position scales with vault growth and shrinks during drawdowns without intervention. The engine never adds to a winner and never averages down on a loser. When an opposing signal fires while a position is open, the engine closes and reverses in one execution — partial participation is treated as more dangerous than full reversal.
Every position carries a hard stop-loss at a fixed percentage from entry, computed at order placement and tracked in the bot's internal state. The stop exists outside the entry signal logic and cannot be overridden by a contradictory RSI reading. The primary exit mechanism is a percentage-based trailing stop that ratchets with favourable price action and triggers an exit when price retraces by the trail amount. The trail does not arm immediately on entry — it activates only after the bar following entry has closed. The first bar of post-entry price action is allowed to develop before the trail starts ratcheting; this single design choice eliminates a class of immediate-trail stop-outs that plague other momentum systems. State is reconciled to disk on every cycle so external position changes — manual close, forced liquidation — are detected within the next polling interval.
Not market-neutral — Elite takes directional positions. Not high-frequency — bar-close evaluation produces a few signals per week per asset. Not capacity-unlimited — HYPE and AVAX have deep but finite order books. Not foolproof. Any month where momentum compresses across both assets simultaneously will produce losses.
Aphelion buys oversold assets in scaled increments, holds them through normal volatility, and exits via layered partial profits with a daily-evaluated trailing stop. It is a long-bias accumulator that treats drawdowns as opportunities to lower its average entry rather than reasons to flinch — a pre-positioned limit order sits on the book ready to add to any position that approaches its liquidation zone, then resets the take-profit ladder against the new lower basis. The structural inefficiency it exploits is the gap between what is statistically oversold and what most traders find emotionally tradeable. Most retail traders avoid these setups because they look terrible on the chart; most institutional accumulators do not run leveraged perpetuals on stocks. Aphelion runs the math on 15 assets and acts on every confirmed signal with the same systematic sizing.
Aphelion combines three independent signal categories into a weighted strength score. Momentum oscillators (RSI(2) Wilder-smoothed) identify short-term oversold conditions, with thresholds calibrated separately for stocks and crypto. Volume anomaly (a directional money-flow Z-score that measures up-volume against down-volume separately) flags capitulation candles where down-volume dominates the bar even at oversold prices. Price-vs-trend extension (price below the long-period moving average, MA-300 for stocks, MA-50 for crypto) confirms the asset is meaningfully extended below its secular trend. Only assets crossing a strength threshold generate a signal; everything else is logged and skipped. On most days, zero signals fire.
Each signal opens a long position at 2x isolated leverage. Position size comes from the Accumulator sizing formula — daily allowance divided across the universe, multiplied by days since the asset last received a signal, scaled by the signal's strength multiplier. The longer it has been since the strategy bought a particular asset, the larger the next signal's allocation. Every position carries three predetermined take-profit levels at meaningful gains above the volume-weighted average cost basis, closing fixed fractions of the original size at each level and letting a residual portion ride.
The defense layer is the strategy's distinguishing feature. As soon as a position opens, the engine places a passive limit BUY order at a fixed buffer above the position's calculated liquidation price. If price ever drops to that trigger, the order fills automatically — the position averages down to a new lower basis, the take-profit ladder resets, and a fresh defense limit goes back on the book at the new (deeper) trigger. A single position can absorb up to three defense fills before defense ammo is exhausted. The defense fills automatically with maker pricing and zero slippage even if the bot is offline; that is the point.
The pre-placed limit defense is the primary risk control — a resting order on the exchange book, not a polling-based market trigger. A reconciliation loop runs every five minutes and resolves any drift between bot state and exchange ground truth. An account-level kill switch halts new entries if intraday losses exceed a threshold; existing positions continue to be managed by trail and defense logic. Daily and per-trade notional caps provide additional pre-position guardrails. The trailing stop arms only after a position has touched a positive threshold above entry, and the trail trigger evaluates once per day at a fixed UTC time — letting winners run through normal intraday volatility without ripping out on shallow wicks.
Not a market-timing strategy. Not high-frequency — most days zero signals fire. Not infinite-capacity. Not a replacement for diversification — long-bias on 15 risk assets at 2x is correlated to broad equity and crypto risk. Not foolproof. Defense limits get filled. Some positions trail-out at small losses.
Soomario Premia sells call options against crypto holdings. The structural edge is volatility overpricing: crypto options markets consistently price implied volatility above realised volatility, particularly during fear-driven regimes. Covered call sellers harvest this gap. Most participants either sell calls too aggressively — picking strikes too close to spot in pursuit of premium — or avoid the strategy entirely because managing rolls, expirations, and strike selection across multiple assets requires constant attention. Premia automates the discipline that makes the strategy work: wide strikes, systematic timing, and mechanical profit capture.
Premia is the platform's most multi-signal strategy. Momentum oscillators and statistical anomaly detection form a conviction score: the ideal setup is a coin trading in the upper portion of its recent range — high enough that call premiums are rich, but not so elevated that a breakout is imminent. Trend direction (Supertrend) acts as a directional gate; a fresh bullish flip blocks a call sell entirely, regardless of premium attractiveness. Range and farmability detection measures how well a coin trades within a defined range — coins with tight, aligned zones across timeframes score higher because their price behaviour is more predictable. Liquidation density (zone proximity) uses the same cross-exchange cascade data as Zones to place strikes above the upside cascade levels, plus an additional ATR volatility buffer. Market regime (Fear & Greed) identifies regimes where premiums are structurally inflated, raising the engine's willingness to sell.
Each position is a short call option on a coin the account already holds. The engine selects strikes in the 7–12% out-of-the-money sweet spot. Strikes below 5% OTM are hard-filtered and never considered, regardless of premium. The engine prefers USDC-settled linear options where available, falling back to inverse options for BTC and ETH. Position sizing matches the account's holdings — fully covered, no naked exposure. Maximum one open position per asset prevents over-concentration in a single expiration. The strategy favours 14–45 day expirations to capture the steepest section of the time-decay curve while staying clear of gamma-driven volatility near expiry.
When an open position reaches the profit capture threshold, the engine buys back the current call and immediately sells a new one at a fresh strike. This locks in gains without waiting for expiration. The engine verifies both the buy-back fill and the new sell fill before updating the journal. If either leg fails to fill, the operation is aborted and retried next cycle.
The 5% OTM floor is the single most important risk control, enforced at both the timing-engine recommendation and the screener's signal scoring. Every order placed on Deribit is polled for fill confirmation; if a limit order remains unfilled after timeout, it is cancelled and retried as a market order, and if that also fails, no journal entry is created. This prevents the phantom-trade problem. Every cycle the engine cross-checks its journal against Deribit's actual positions and bidirectionally reconciles any discrepancy — adopting orphan exchange positions, auto-cancelling phantom journal entries.
Not a directional bet — Premia profits from time decay and volatility overpricing, not from predicting whether any given coin will go up or down. Not a hedge — selling calls generates income during flat or declining markets but does not protect against large drawdowns in the underlying asset. Not high-frequency — the engine scans every 30 minutes. Not unlimited capacity — income scales linearly with holdings. Not foolproof. Assignment happens. Premiums vary. Some cycles produce no actionable signals.
Most retail algorithmic strategies concentrate their bet on one edge and rely on that edge persisting through every regime. A pure trend-following system gets chopped to pieces in mean-reverting markets. A pure value system bleeds for years through extended growth regimes. A long-only crypto strategy is leveraged beta wearing an algorithmic disguise. Rotation deliberately holds three uncorrelated edges in parallel — long-horizon equity conviction, microstructure-driven crypto accumulation, and event-driven short selling. When one sleeve is in drawdown, the others are usually not. The combined equity curve does not depend on any single regime cooperating.
The dominant scoring axis is visionary upside — long-horizon analyst conviction targets sourced from credible thesis-driven research. The engine maintains a small concentrated universe of mega-cap US equities with legible disruption theses, all tradeable on Hyperliquid's HIP-3 DEX. Each candidate is scored on three weighted layers — long-horizon visionary upside, near-term Wall Street consensus upside, and technical entry timing. Visionary is heaviest by design, because it is the hardest to fake. The composite score ranks the universe; top names fill a fixed number of rotation slots, plus a permanent core position in the highest-conviction long-term holding. Rebalances run weekly. An eight-point hysteresis prevents churn.
Permanent core positions in three highest-conviction major assets — a digital-store-of-value anchor, a high-throughput L1 with broad ecosystem exposure, and HYPE itself. These holdings are not rotated. They are accumulated. The sleeve uses liquidation density data from three centralised exchanges to compute a Long Max Pain anchor, with a five-level DCA grid placed at progressively deeper liquidation zones below it. Levels are spaced using a volatility-adjusted multiple of the asset's recent range, not fixed percentages. The sleeve also reads the open-interest ratio as a secondary input — a short-heavy book is structurally favourable for accumulation entries.
The riskiest sleeve, hard-capped at a small fraction of total capital and a low maximum number of simultaneous positions — neither cap can be raised by the bot, only by an explicit operator change. Candidates come from the token unlock calendar and a technical-weakness screener; a multi-gate validator requires confluence — at least two independent confirmations including statistical overextension on a multi-month Z-score and a momentum exhaustion check. Position management uses a back-loaded ten-level take-profit grid that trims less at shallow levels and more at deep ones, with a runner held mechanically on every position. The grid solves the two failure modes of retail short selling — exiting too early on an early bounce and overstaying past a profitable resolution.
The three sleeves are not three bets on the same underlying. In a sustained risk-on regime, equities and crypto longs rally together; the short sleeve typically idles or takes small losses. In a coordinated risk-off, equity rotation is in drawdown and crypto cores are being filled at deeper DCA levels — exactly the conditions under which the short sleeve realises its largest profits. The portfolio has at least one sleeve generating positive return in most regimes the strategy has been stressed against. This is not a hedge in the formal sense — the short sleeve is too small to neutralise anything. What it is, instead, is a sleeve-level diversifier whose timing is structurally counter-correlated with the long sleeves' drawdown periods.
Each sleeve has its own hard capital cap; the equity sleeve carries the largest cap, the short sleeve the smallest by a wide margin. Crypto longs and shorts both carry per-position hard stops sized to each asset's volatility profile. No stacking within sleeve. Asymmetric cycle cadences mean no single market condition triggers all three sleeves simultaneously. Rotation is not currently a live-capital product — it runs in paper-trading on production infrastructure and will transition to live capital only after at least 90 trading days of continuous validation. It is not market-neutral; it is net long equities and net long crypto cores. Not regime-proof — a coordinated risk-off where the short sleeve has no qualifying candidates would produce drawdown across all three sleeves.
Zones positions where forced sellers — and forced buyers — create temporary mispricings. When leveraged longs cascade into liquidation, prices overshoot fair value to the downside before reverting; the engine enters long into that overshoot, averages down across four price levels, and scales out across three profit targets as the market normalises. When leveraged shorts cascade in a squeeze, the symmetric logic applies in reverse. The strategy is bi-directional by construction. Liquidation clusters are observable across exchange order books, but most participants either lack the multi-asset infrastructure to track 21 markets in parallel, or trade them anecdotally on individual setups rather than as a portfolio of statistical entries.
Zones uses liquidation density (cross-exchange max-pain zones) as its primary anchor — every cycle, the engine pulls open interest, liquidation density, and 24-hour high-low ranges from Binance, Bybit, and OKX, then computes a Long Max Pain band below current price and a Short Max Pain band above. A composite DCA Score ranks coins by zone width, proximity, and OI depth. Before any DCA-Score-ranked coin gets capital, it must independently clear a conviction gate combining momentum oscillators (RSI extremes), volume anomalies, open interest imbalance, market regime (Fear & Greed), and trend alignment. The conviction gate also resolves direction — if conditions favour a downside snap-back, long; if upside fade, short. The gate filters for setups where the snap-back is statistically more likely, not just mechanically possible.
When a coin clears both the DCA Score ranking and the conviction gate, the engine deploys capital across four graduated price levels within the liquidation zone — one immediate market entry at the zone edge, three resting limit orders at progressively deeper prices. Allocations are weighted toward the deeper levels because deeper fills represent better entry prices. If price never reaches the deepest levels, the orders simply do not fill — no additional risk is taken, and the position stays smaller. Each coin's leverage is calibrated to its historical volatility band, capped at 8x; higher-volatility coins (HYPE, TAO, ZEC) trade at the lower end, lower-volatility coins (BTC, ETH, PAXG) at the higher end.
Three profit targets and a trailing-stop runner. Each target closes a portion of the position; with each close, the trailing stop on the remainder tightens. Targets are computed from the position's actual average entry price, not from the original zone midpoint — as deeper DCA levels fill and the average entry improves, profit targets adjust closer in price terms. The math rewards averaging into the dislocation.
Every position carries a hard stop computed at a fixed buffer beyond the deepest configured DCA level. A second, wider catastrophic stop sits at a multiple of the per-level distance, intended to fire only in true black-swan conditions. State integrity is reconciled against three independent sources every cycle — clearinghouse state, fills history, and the local positions file — and equity readings outside a sanity band are flagged as glitches and excluded from the equity log without halting trading. An unconditional 48-hour time exit closes any position regardless of P&L; locked capital is dead capital, and a thesis that has not played out in 48 hours is statistically less likely to play out at all. The portfolio-level circuit breaker pauses all new entries once trailing drawdown crosses a threshold; existing positions continue to be managed normally, and the breaker resets when equity recovers above the prior peak.
Not a trend-follower — Zones is mean-reversion. In sustained directional markets without liquidation cascades, the engine simply sits on its hands. Not an alpha generator across all market conditions — opportunities thin out in long quiet markets. Not unlimited capacity — bounded by Hyperliquid order book depth. Not foolproof.
Farms is a long-only grid trading engine that places its grids at liquidation zones identified through aggregated open-interest data from three centralised exchanges. When price approaches a major long-side liquidation cluster, Farms opens a multi-tier grid spanning that zone — automatically buying dips, selling bounces, and riding any sustained upside through a Pionex-style seed position that holds until the top of the range. The structural inefficiency it exploits is the predictable mean-reversion pattern around liquidation zones. Most grid bots place ranges based on historical volatility or moving averages — Farms places them where the order book actually breaks. The grid harvests every oscillation; the seed catches the trend.
Farms uses liquidation density as its primary input, with cross-exchange aggregation producing a Long Max Pain and Short Max Pain that define the natural gravitational range for each asset. Range and farmability detection scores ~50 markets on a multi-factor composite — zone width, 24-hour volume, open-interest depth, range alignment across timeframes, and long/short skew. Crucially, the two largest weights are price position (relative to the Long MP) and reversal setup (24-hour move plus zone position) — these were specifically engineered to prevent the strategy from chasing pumps. Volatility regimes set adaptive grid spacing: a tight-zone asset like BTC gets ~15 levels while a wide-zone asset like DOT gets ~75, all targeting consistent spacing per grid.
Even after scoring identifies a top candidate, Farms refuses to open a grid unless price is positioned safely within the zone — within a small window above the Long MP or below it. This is the strategy's most important rule. An asset can be the highest-scoring opportunity in the universe and still get rejected at the entry gate. The bot waits. When an asset clears the gate, Farms builds a grid spanning from below the Long MP up to the Short MP — extending below the zone to catch typical liquidation-cascade overshoot before mean reversion. Each level is a persistent buy/sell pair; levels reset and re-arm automatically.
At grid creation, the engine immediately opens a single long position at current market — the seed runner — sized to one grid level. Its target is the top of the grid. Once price reaches grid_high, the seed transitions into an ATR-style trailing stop, allowing it to capture continuation moves beyond the zone. Even if price never dips into the grid, the seed alone produces meaningful profit on a directional move — that is the Pionex-style mechanic.
When a grid is created, a hard stop is computed at a fixed buffer below the lowest grid level — and that stop is never updated. Earlier versions refreshed the stop alongside zone refreshes, which created an expanding gap as zones drifted upward in pumping markets. The locked stop ensures that maximum theoretical loss on any single grid is bounded at creation time. A portfolio-level kill switch monitors live equity inclusive of all unrealised P&L; when equity falls a defined percentage below peak, the halt fires, all grids close at market, and no new positions open until operator manual reset. The engine cannot open shorts. There is no toggle, no edge case, no override — the vast majority of failure cases in earlier Farms versions involved short positions accumulating into trending moves, and removing the capability removes the risk class entirely.
Not high-frequency — round trips fire every few minutes to hours, not seconds. Not directional speculation — beta to BTC is statistically near zero. Not capacity-unlimited — at meaningful capital deployment per asset, per-level notional starts moving alt-coin order books. Not a perpetual machine — grids do close at breakout stops. The 8% drawdown halt exists because it is expected to fire occasionally.
Accumulator is the only Soomario product that is not a vault. Subscribers receive daily quantitative buy recommendations and execute the trades themselves on whatever brokerage or exchange they prefer. The product retains no custody of subscriber funds, executes nothing on subscribers' behalf, and is positioned as market commentary and educational content — not as an investment adviser. The audience is retail investors who want to time their dollar-cost-averaging purchases with quantitative discipline rather than buying fixed amounts on fixed schedules.
Pure dollar-cost averaging works mechanically — volatility smooths out, emotional timing mistakes are avoided, compound growth does the heavy lifting. But it leaves money on the table. It buys the same amount whether the asset is 50% above its long-term average or 30% below. A mathematically better approach — buy more when oversold, less or nothing when overextended — is widely understood but almost never implemented, because doing so correctly requires reliable price data across multiple asset classes, consistent calculation of technical indicators, and a disciplined scoring model that rolls indicators into a single actionable number, delivered daily without the user having to open a charting platform. Most retail investors either do not have time, do not have the skill, or do not trust themselves to follow through. The result is a market of disciplined investors executing an undisciplined version of a disciplined strategy.
Accumulator computes three indicators per asset every market close. Momentum oscillators (RSI(2) Wilder-smoothed) detect short-term oversold conditions, with thresholds calibrated separately for stocks and crypto. Price-vs-trend extension (price below the long-period moving average — MA-300 for stocks, MA-50 for crypto) detects mean-reversion opportunities when price is significantly below its long-term trend. Statistical anomaly detection (price-distribution Z-score) flags statistical outliers; applied to crypto where the wider volatility regime makes statistical normalisation more informative, de-emphasised for stocks. The three indicators are combined using per-asset weights into a 0–100 strength score. Signals below a threshold are suppressed; signals above fire through the notification pipeline. Signal strength then maps to a buy multiplier between 1.0× and 5.0×, which scales the user's accumulated daily-buy allowance — a stronger signal recommends a larger buy.
Subscribers receive daily signals for 14 curated assets ("Mag14") spanning AI infrastructure, semiconductors, EV, and major cryptocurrencies, plus unlimited personal watchlist tickers scored using the same framework and delivered as private DMs. During onboarding, each subscriber selects a monthly budget tier; every signal computes the recommended buy amount at that tier, so the same quantitative opportunity is rendered as a different dollar amount for each subscriber. Signals arrive as branded PNG cards rendered with Pillow — sector badges, price, RSI, trend deviation, signal strength with strength pills, and a gold-trimmed callout with the personalised buy amount. Two-channel delivery (Telegram + web push) ensures signal delivery even when one channel is unavailable.
A scheduled job fires daily at 4:01 PM Eastern — immediately after US market close. The scheduler fetches OHLC data from Yahoo Finance with CoinGecko fallback, computes indicators for all Mag14 assets and every ticker on any user's watchlist, scores each asset, renders a personalised PNG per subscriber at their own budget tier, dispatches notifications, and logs every signal to an audit table with full reasoning — allowing dashboard-side performance attribution. User isolation is enforced at the database query layer; personal watchlist signals never surface on another subscriber's dashboard. Telegram bindings are established through 10-minute HMAC-signed deep links to prevent account hijacking.
Not a trading bot — the product recommends; the user executes. Slippage and order timing on the user's brokerage are outside the product's control. Not a hedge — DCA assumes long-term mean reversion and growth; in a persistent bear market, DCA losses compound just as DCA gains compound in a bull market. Not personalised investment advice — the product does not know a subscriber's full financial picture and is structured as market commentary, not as an investment adviser. Not infallible — every signal is logged and trackable; some will resolve favourably and some will not. The product is transparent about this.
Accumulator is the front door to the broader Soomario ecosystem. The signal logic it uses — RSI(2), MA extension, Z-score, weighted scoring — is a subset of the analytical toolkit the vault products use. A subscriber who finds the product useful, follows the signals over a quarter, and develops a sense of how the platform thinks is a qualified prospect for the higher-ticket vault products. The $7/month price point is deliberately below the threshold where users evaluate financial products carefully; it functions as an introduction to the discipline and a continuous demonstration of how the platform applies it.
Risk management on the platform is the same framework reading differently for each strategy. The constraints below are universal — encoded in different forms across the eight vault strategies and (where applicable) in the subscription product's signal-pipeline integrity controls.
Every leveraged position on the platform has a hard stop, sized to the asset's volatility and the strategy's risk budget for that position. Where the venue supports it, the stop is a reduce-only stop-market trigger placed directly on the exchange's matching engine — Hyperliquid for the perp strategies, Deribit for Premia. Stops on the exchange fire whether the bot is alive or not. Where the strategy's design also calls for it (Max Pain, Alpha, Aphelion), an independent bot-side monitoring loop checks the same thresholds at a fixed interval and force-closes any position that has crossed without the exchange-side stop having fired. Two independent components must both fail before a position runs unmanaged.
Aphelion specifically pre-positions a passive limit BUY order on the exchange book at a fixed buffer above the calculated liquidation price. If price ever drops to that trigger, the order fills automatically with maker pricing and zero slippage — even if the bot is offline. The position averages down, the take-profit ladder resets, and a fresh defense limit goes back on the book at the new trigger. This is the only platform mechanism designed specifically to convert a near-liquidation event into an improved cost basis. It is bounded — three defense fills per position, after which the position is held to trail or take-profit without further defense.
Aphelion's account-level kill switch halts new entries if intraday losses exceed a threshold or if total unrealised P&L drops below a defined percentage. Zones' portfolio-level circuit breaker pauses all new entries once trailing drawdown crosses its threshold. Farms' unrealised drawdown halt fires when live equity (inclusive of all unrealised P&L) falls below peak by a defined amount, closing all grids at market and preventing new positions until operator reset. Each is sticky — once tripped, manual review is required to resume. Existing positions continue to be managed by trail and defense logic; only new entries are blocked.
Position-size caps, leverage ceilings, no-stacking rules, slot counts on event-driven sleeves, capital allocations per sleeve in Rotation — all are encoded as constants the strategy code cannot override based on optimistic signal readings. A strategy that would be more profitable with looser constraints, but is engineered to refuse to loosen them, has a worst-case behaviour that is computable in advance. Max Pain caps leverage at 3x. Aphelion at 2x. Elite at 2x on the primary, 1x on the secondary. Rotation's short sleeve cannot grow into a meaningful share of the portfolio even if every short signal looked great that week.
The eight vault strategies are decoupled by design. They use different signal categories, target different inefficiencies, and reach their drawdowns at different times. A coordinated risk-off across all crypto might pull Zones, Farms, and Max Pain into drawdown simultaneously — but it is the regime in which Alpha's short sleeve and Rotation's hedged shorts are typically most productive. A sustained risk-on rally would diminish Premia's volatility-overpricing edge but is the exact regime in which Elite's directional momentum capture and Aphelion's long-bias accumulator perform best. The strategies are not chosen by AUM contribution; they are chosen by inefficiency targeted, and the resulting portfolio is what diversification across edges (rather than across assets) actually looks like.
The bug that destroys an algorithmic trading account is rarely a bad signal. It is the bug where the bot believes it has a position the exchange does not recognise, or believes it does not have one when it does. Every strategy on the platform reconciles internal state against ground-truth exchange data on every cycle — Aphelion every five minutes, Premia every 30-minute scan, Zones against three independent sources, Alpha with a force-refreshed read before any state-changing close. State integrity is treated as a category of risk control with the same prominence as position-level stops. It is the substrate that makes any other control trustworthy.
| Execution venues | Hyperliquid (perpetuals + HIP-3 stock perpetuals); Deribit (crypto options); OKX (Aphelion copy trading and previous experiments) |
| Hosting | Railway — dedicated containers per strategy, persistent volumes, Singapore region for execution-sensitive workloads |
| Languages | Python 3.13 across all production strategies; Pine Script (Max Pain signal layer); JavaScript (Accumulator dashboard, signal-card rendering pipeline) |
| Frameworks | Flask for HTTP/dashboard layer; APScheduler for cron and interval cycles; gunicorn for production serving; Hyperliquid Python SDK |
| Persistence | SQLite on persistent volumes for state, positions, fills, and signal audit logs; JSON for ephemeral state files |
| Front-end | React via Babel CDN (no build step), Tailwind utility CSS, PWA-installable; marketing site on GitHub Pages |
| Auth & payments | Whop OAuth 2.0 with PKCE; Whop subscriptions; constant-time admin key comparison |
| Notifications | Telegram Bot API; Web Push VAPID; Discord channels for live trade broadcast (Max Pain) |
| Image rendering | Pillow with bundled Poppins typography for Accumulator signal cards |
| Monitoring | Per-strategy live dashboards on soomariostrategies.com subdomains; on-chain vault verification for HL-vault strategies; structured JSONL logs |
The platform uses CEX-primary data feeds for price, candles, funding, and open interest — Binance, Bybit, and OKX bulk endpoints — because they are higher-throughput, free, and designed for steady-state monitoring. Hyperliquid is used for state queries, vault data, and execution only, never for steady-state monitoring (this is a deliberate choice against rate-limit pressure). Yahoo Finance and CoinGecko provide equity and crypto OHLC for accumulator-style strategies. Deribit's options chain API is the primary data source for Premia. DeFiLlama provides emissions, protocol metrics, and tokenomic context for Alpha. Coinglass-equivalent computations of liquidation density are performed natively by the platform from primary exchange OI feeds rather than scraped from aggregators. The Alternative.me Fear & Greed index provides daily regime context for the strategies that use it.
Every live vault product publishes its on-chain address. Vault performance, deposit history, position state, and realised P&L are visible on Hyperliquid's public vault page in real time. There is no LP statement, no quarterly attestation, no aggregated performance summary — the system is its own audit. The dashboards at soomariostrategies.com refresh on the order of seconds and show the same state any depositor could reconstruct from the public chain themselves. Every entry, exit, and risk-control trigger is logged and, for several strategies, broadcast to a public Telegram channel as it happens.
The vault contracts are Hyperliquid native — depositor capital is custodied at the protocol level. The operator does not custody depositor funds. Withdrawals are processed through the protocol's standard withdrawal flow on the depositor's own schedule. There are no lockups, no minimum hold periods, no deposit or withdrawal fees imposed by the operator. Hyperliquid's standard 0.02% taker fee applies to all trades and is paid to the protocol, not to the operator. Funding payments flow through the vault as normal perpetual mechanics.
The platform is operated by a single principal — MLO — based in Incheon, South Korea. Background includes a B.S. in Finance (Metropolitan State University of Denver, 2008), former FINRA Series 7/63 licenses, an M.Ed. in Curriculum & ELL (Concordia, 2019), and several years of self-directed quantitative trading research preceding the launch of Soomario Strategies in late 2025. Strategy logic is designed by the operator; implementation is AI-assisted, with all production code reviewed before deployment. There is no team. There is no fund administrator. The platform's operational simplicity is a deliberate constraint — it is part of why the worst-case behaviours described in this document are computable in advance.
The platform runs on Railway. A Railway outage suspends all execution while the outage persists; positions managed by exchange-side stops continue to be protected by those stops; positions reliant on bot-side monitoring would fall back to the exchange-side hard stops described in Section V. Multi-source price feeds include defined fallbacks (Yahoo → CoinGecko, Binance → Bybit → OKX), but a coordinated outage across primary and fallback sources would suspend signal generation. Telegram and web push are best-effort delivery; the Accumulator product runs two-channel redundancy and audits delivery, but neither channel offers SLA guarantees. Database backups for the subscription product are not yet automated as of this document; current mitigation is that database state is rebuildable from source signal logs in minutes.
The operator is a single person. Operational risk includes the operator being unavailable due to illness, travel, or other circumstance. The strategies are designed to run unattended for extended periods (exchange-side stops, automated reconciliation, kill switches), but materially novel events — exchange policy changes, protocol upgrades, regulatory developments — may require operator action that cannot occur during an unavailability window. This is an honest limit.
The vault products are custodied by Hyperliquid's native vault contract. The platform does not control this contract; smart-contract risk in the protocol's vault architecture is borne by depositors in the same way it would be by any direct Hyperliquid user. Premia uses Deribit's exchange-side sub-account model and inherits Deribit's exchange-platform risk. The operator does not custody funds, and the platform's operational design specifically avoids any architecture in which depositor funds could be mishandled by a bug in platform code — the strategies place orders against the exchange/protocol's API; they do not move depositor balances directly.
The Accumulator subscription is structured as market commentary and educational content, with explicit "Not financial advice. Trade at your own risk." disclaimers on every signal. This positioning aligns with established frameworks for financial newsletters and signal services in jurisdictions including the United States (where publishers are protected under the Lowe v. SEC precedent for impersonal, non-fiduciary market commentary), the United Kingdom, the European Union, and South Korea. The vault products operate as systematic, on-chain perpetual trading vaults — a category for which regulatory frameworks vary significantly by jurisdiction. Depositors are responsible for their own jurisdictional compliance regarding leveraged perpetual futures trading.
Past performance does not guarantee future results. Soomario Strategies' vault products trade leveraged perpetual futures, leveraged options, and equity-perpetual instruments, all of which carry substantial risk of loss including the possibility of losing the entire allocated capital. The Accumulator subscription product provides quantitative market commentary; it does not execute trades or hold subscriber funds. This document describes the platform as currently implemented and is provided for informational purposes only. Nothing in this document constitutes financial, legal, tax, or investment advice. Conduct your own research and consult appropriate professionals before allocating capital to any vault product or acting on any signal.
| Hyperliquid | On-chain perpetual exchange. All vault products (except Premia) execute here. Native vault contract provides depositor self-custody. Standard 0.02% taker fee. HIP-3 enables stock perpetuals alongside crypto. |
| Deribit | Crypto-native options exchange. Primary venue for Premia. Sub-account custody model. |
| OKX | Centralised exchange supporting copy trading. Aphelion's crypto subset deployed via OKX copy-trading product. Used for additional data feeds (OI, candles). |
| Binance · Bybit | Used as data sources only — bulk price, candle, funding, and OI feeds for steady-state monitoring of the broader crypto universe. Not used for execution. |
| Analytical Category (Section III) | Strategies Using It |
|---|---|
| Momentum oscillators (RSI) | Max Pain · Elite · Aphelion · Zones · Premia · Rotation · Accumulator |
| Volume anomaly & money flow | Aphelion · Max Pain · Zones |
| Statistical anomaly (Z-scores) | Max Pain · Aphelion · Accumulator · Rotation · Premia |
| Trend direction (MA, Supertrend) | Aphelion · Accumulator · Max Pain · Premia · Rotation |
| Open interest & funding rates | Alpha · Zones · Farms · Rotation |
| Liquidation density & max-pain zones | Zones · Farms · Premia · Rotation · Alpha · Max Pain |
| Market regime (Fear & Greed) | Premia · Zones |
| Price-vs-trend extension | Aphelion · Accumulator · Rotation |
| Volatility regimes & adaptive sizing | All strategies |
| Multi-timeframe confluence | Premia · Rotation · Max Pain |
| Range/farmability detection | Farms · Premia |
| Event-driven (token unlocks, expiries) | Alpha · Rotation · Premia (expiries) |
| Visionary & long-horizon conviction | Rotation (Fastest Horse sleeve) |
| State integrity & reconciliation | All strategies |
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. The original definition of the Relative Strength Index and the recursive moving average smoothing method.
Hyperliquid Documentation. hyperliquid.gitbook.io/hyperliquid-docs. Vault contract architecture, perpetual mechanics, HIP-3 specification.
Deribit Documentation. docs.deribit.com. Options chain API, sub-account custody, settlement mechanics.
DeFiLlama. defillama.com. Tokenomic and emissions data; protocol metrics used in Alpha's universe construction.
Alternative.me Fear & Greed Index. alternative.me/crypto/fear-and-greed-index. Daily synthetic crypto market sentiment reading.
Lowe v. SEC, 472 U.S. 181 (1985). Foundational US precedent on First Amendment protection for non-fiduciary market commentary by financial publishers.
Platform · soomariostrategies.com
Twitter / X · @SoomarioStrat
Operator · MLO · Incheon, South Korea
Subscription · Soomario Accumulator — $7/month, 7-day free trial via Whop
This document represents the Soomario Strategies platform as of April 2026. Strategy implementations evolve as markets evolve. Where individual strategy whitepapers conflict with this document on operational detail, the individual whitepapers — kept current per strategy — are authoritative. The principles in this document do not change between revisions.
SOOMARIO STRATEGIES