Detect Hanging Man with explicit scale-aware geometry, equality, warm-up, and context diagnostics.
The decision this tutorial makes visible
Hanging Man is widely named but platform thresholds differ. A builder needs one frozen contract that explains every match and rejection without turning the shape into a performance claim.
The precise question is: When does Hammer-like lower-shadow geometry after an uptrend qualify as a Hanging Man?
A practitioner can see exactly which geometry or context rule failed. A builder can reproduce the same decision from frozen OHLC, scales, tick policy, and prior-only state in Python, TypeScript, fixtures, visuals, and the guided lab.
Intuition before notation
The candle geometry is intentionally the same as Hammer; only prior context changes the catalog interpretation.
The package teaches one auditable convention and names nearby variants. It does not imply that platforms sharing the same label use identical thresholds.
Scope and nearby methods
One validated closed candle plus prior median body/range scales. The geometry must be matched and prior context must equal uptrend.
| Variant | Definition | Best use | Main limitation |
|---|---|---|---|
| Package median-scale rule | context=uptrend AND body <= 0.75B_ref AND lower >= 2max(body,tick) AND upper <= 0.25max(body,tick) | Auditable teaching and cross-language parity | Not vendor parity |
| TA-Lib configurable averages | Prior average ranges and factors | TA-Lib compatibility | Different thresholds and lookbacks |
| Fixed percentage of current range | Within-candle proportions | Simple chart screening | No historical scale |
What is sourced, selected, synthetic, and derived
| Role | Material claim | Evidence | Boundary |
|---|---|---|---|
| Sourced fact | Candlesticks encode OHLC as a real body and shadows, and established pattern names are interpreted under configurable conventions and market context. | S1-S4 | Sources support terminology and convention/context roles, not this package's thresholds or a forecast. |
| Implementation choice | One validated closed candle plus prior median body/range scales. The geometry must be matched and prior context must equal uptrend. | Frozen package definition | Other books and software can use different averages, factors, equality, gaps, or trend filters. |
| Synthetic teaching input | Every OHLC value, scale history, trend input, tick, tolerance, and scenario path in the package is repository-authored. | datasets/canonical-input.json and scenario-results.json | No value is an observed security, exchange session, provider bar, or return. |
| Author-derived calculation | The structured matched output follows from the printed formula and synthetic fixture. | expected-output.json, independent arithmetic, and parity tests | Definition fidelity is not empirical or predictive validation. |
The sources support candle construction, established names, configurable relative thresholds, and the need for context. The numeric fixture, medians, thresholds, equality operators, and outputs are repository-authored synthetic choices and author-derived calculations.
Formula, symbols, and numerical policy
context=uptrend AND body <= 0.75B_ref AND lower >= 2max(body,tick) AND upper <= 0.25max(body,tick)
| Symbol | Meaning | Unit | Policy |
|---|---|---|---|
| B | Current real body | price | Absolute open-close distance |
| U,D | Upper and lower shadows | price | Non-negative |
| B_ref,R_ref | Prior median body and range | price | Latest ten eligible prior closed bars |
| tick | Tick size | price | Positive denominator floor |
- Calculate with unrounded finite OHLC differences in one declared price unit and adjustment basis.
- Use an explicit positive tick size; threshold equality follows the operator printed in the formula.
- Reference medians use at most the latest ten eligible prior closed bars and never include a candidate candle in its own baseline.
- Round only for display; preserve null, warm-up, zero-body, wrong-context, and failed-check states instead of coercing them to a match.
Read the formula in the same order as the algorithm. Validate identity, ordering, units, and supported state first. Apply the selected equality and window rules second. Calculate with unrounded numeric values. Round only at the declared presentation boundary, and preserve null as a diagnostic rather than coercing it to zero.
Build the algorithm
- Validate the candle, tick, context, and aligned prior scales.
- Calculate body, upper shadow, lower shadow, and prior medians.
- Evaluate the Hanging Man Boolean checks in formula order.
- Apply required context when applicable and return every failed check.
Production-minded operational checklist
- Require a closed candle
- Freeze prior scales
- Freeze trend before candidate
- Inspect every failed check
Reject a plausible-looking label when the bar is provisional, reference state is insufficient, session/basis is mixed, or required prior context is unavailable.
Worked synthetic example
The canonical fixture is synthetic teaching data, not an observed control
event, customer order, or broker execution. Its primary author-derived output,
matched, is true. The complete input and output
are in datasets/canonical-input.json and datasets/expected-output.json.
The synthetic candle is constructed so the material Hanging Man threshold is met at the declared equality boundary. The prior-body median is 2 and prior-range median is 5; all values are author-created and the structured output exposes every check.
Counterfactual checkpoint
Exact decision boundary. Move the teaching driver through the printed threshold. The output changes because The detector compares unrounded geometry with an explicit scale-aware threshold.
The structured result retains state and diagnostics in addition to the primary number. That makes the calculation independently reviewable and prevents a partial, null, rejected, or venue-bounded outcome from being mistaken for an unqualified value.
Boundary and counterexample workbook
The playground computes every scenario at 61 deterministic parameter states.
The table uses the declared focus step and states whether that focus reproduces
the canonical fixture. The full state ledger and compressed transition
segments are in datasets/scenario-results.json.
| Scenario | Review focus | Purpose | State | Primary output | Diagnostic | Decision segments |
|---|---|---|---|---|---|---|
| Canonical threshold sweep | Step 30 · canonical fixture | Move the defining synthetic geometry through the exact canonical boundary; state 31 reproduces the complete fixture. | matched | pattern matched | matched; failed: none | 2 |
| Exact equality boundary | Step 30 · canonical fixture | Hold the canonical synthetic fixture at its material equality rule. | matched | pattern matched | matched; failed: none | 1 |
| Geometry rejection | Step 30 · comparison focus | Break a material body, shadow, direction, containment, midpoint, gap, or tolerance condition. | not-matched | no pattern match | not-matched; failed: compact_body, long_lower_shadow | 1 |
| Opposite prior context | Step 30 · comparison focus | Keep the geometry but reverse the causal prior trend to expose wrong-context behavior. | wrong-context | no pattern match | wrong-context; failed: none | 1 |
| Reference warm-up | Step 30 · comparison focus | Use only three prior bars so the scale-aware detector cannot initialize. | warmup | no pattern match | warmup; failed: minimum_history | 1 |
| Larger prior scale | Step 30 · comparison focus | Keep current geometry but double prior body and range baselines. | matched | pattern matched | matched; failed: none | 1 |
| Sideways-context control | Step 30 · comparison focus | Retain valid geometry and scales while withholding reversal context. | wrong-context | no pattern match | wrong-context; failed: none | 1 |
These rows are not backtest observations. They are controlled counterexamples that expose how one driver changes the state, output, or reason code while the rest of the contract stays fixed.
Visualize the boundary
Open this SVG at full size, or use the guided playground to compare the seven topic-specific canonical, boundary, policy, and failure scenarios.
The Mermaid flow answers where the selected calculation sits in the processing sequence. The SVG keeps the formula, output, decision boundary, and invariant visible together. The lab lets the reader step through the same structured states without changing the underlying definition.
Implementation walkthrough
The references validate OHLC first, derive body and shadows once, calculate causal scales or context, evaluate named Boolean checks in formula order, then expose match, state, diagnostics, and every failure reason.
The main implementation branches are:
- history < 5 — Return warmup, because Reference scale is not initialized.
- geometry fails — Return not-matched plus failures, because At least one declared condition fails.
- geometry passes but context fails — Return wrong-context, because Shape and interpretation remain separate.
- all required rules pass — Return matched, because Selected contract is satisfied.
Neither reference silently fetches data, mutates caller-owned inputs outside the declared engine behavior, guesses hidden state, or substitutes a provider default. Shared JSON fixtures make value, null, state, and reason-code drift visible across languages.
Testing and validation
Definition tests compare every canonical field, reject malformed state, and exercise the material boundary. Family validation recomputes every playground state from the reference function. Independent arithmetic is recorded beside the fixture rather than inferred only from implementation output.
The audit must preserve these invariants:
- Current anatomy and effective denominator
- Prior body/range medians and history count
- Required context, every Boolean check, and failed-check list
Passing these checks proves that both implementations match the selected detector contract on the fixture and scenarios. It does not validate a market forecast.
Failure modes and misuse
- A geometry match is not confirmation, a directional forecast, an order, or investment advice.
- Thresholds and lookbacks are explicit teaching choices, not universal market standards or calibrated parameters.
- Feed construction, sessions, corporate actions, contract rolls, ticks, missing bars, provisional candles, and later corrections can change a result.
- Passing definition and parity tests does not establish historical association, cost-adjusted performance, robustness, or profitability.
Debugging order
When a result looks surprising, inspect the state in this order:
- Validate OHLC ordering, closure, interval, session, price basis, and tick size.
- Recalculate body, shadows, body endpoints, and the prior median scales.
- Confirm prior trend was frozen before the pattern began and inspect equality/tolerance rules.
- Compare every named Boolean check before interpreting the final match label.
Evidence and historical boundary
Historical decision: deferred. Historical examples are deferred. A reproducible case would require licensed point-in-time OHLC bars, an exact interval and session, adjustment and roll policy, tick size, parameter vintage, prior-only context, revision history, and an explicit separation between detection correctness and any later return study.
The primary sources are Nison candlestick reference, CMT 2026 program guide, TA-Lib candle settings, TA-Lib Hanging Man. They support the source roles listed in the research ledger, not a redistributable historical observation, a universal candlestick definition, a licensed historical market event, a predictive edge, a trading recommendation, or a profitability claim.
Summary and next topic
You can now calculate Hanging Man under an explicit definition and carry its structured evidence into Inverted Hammer. The learning flow is: Hammer → Hanging Man → Inverted Hammer. Carry the result forward only with its scope, clock, state, and evidence label.
Rendered from the canonical Mermaid sources linked by this article.
Hanging Man calculation flow
This flow identifies the selected calculation stages and the structured output.
Takeaway: Hanging Man is a conjunction of measurable geometry and prior uptrend context.
ReferencesPrimary sources and evidence notesExpand the source trail, evidence role, and limitations behind the engineering choices.
Expand the source trail, evidence role, and limitations behind the engineering choices.
S1 — Japanese Candlestick Charting Techniques, Second Edition
- Organization or authors: Steve Nison
- Source type: Authoritative practitioner book
- Publication or effective date: 2001-11-01
- Version: Second edition; ISBN 9780735201811
- URL or DOI: https://www.penguinrandomhouse.com/books/350650/japanese-candlestick-charting-techniques-by-steve-nison/
- Accessed: 2026-08-01
- Jurisdiction: General technical-analysis literature
- Supports: The established candlestick vocabulary and the importance of reading formations in market context.
- Limitations: A charting reference is not a machine contract, universal threshold specification, or empirical performance proof.
S2 — CMT Program Guide 2026
- Organization or authors: CMT Association
- Source type: Official professional curriculum guide
- Publication or effective date: 2025
- Version: 2026 program guide
- URL or DOI: https://cmtassociation.org/wp-content/uploads/2025/12/CMT-PROGRAM-GUIDE-2026-1.pdf
- Accessed: 2026-08-01
- Jurisdiction: Professional technical-analysis education
- Supports: The curriculum distinguishes candle construction, doji variants, reversal patterns, gaps, and the strengths and weaknesses of candlestick interpretation.
- Limitations: Curriculum objectives do not prescribe these numeric thresholds or prove trading performance.
S3 — Candlestick Settings
- Organization or authors: TA-Lib project
- Source type: Official maintained technical documentation
- Publication or effective date: 2026
- Version: Core API documentation accessed 2026-08-01
- URL or DOI: https://ta-lib.org/api/candle-settings/
- Accessed: 2026-08-01
- Jurisdiction: Cross-platform software convention
- Supports: Maintained candlestick software evaluates body, shadow, and near/far characteristics against configurable prior averages and factors.
- Limitations: This package uses prior medians and transparent factors and does not claim TA-Lib parity.
S4 — Hanging Man recognizer
- Organization or authors: TA-Lib project
- Source type: Official maintained function catalog
- Publication or effective date: 2026
- Version: Function catalog accessed 2026-08-01
- URL or DOI: https://ta-lib.org/functions/cdlhangingman
- Accessed: 2026-08-01
- Jurisdiction: Cross-platform software convention
- Supports: A maintained software convention for the Hanging Man geometry and its named neighboring patterns.
- Limitations: The repository contract is intentionally explicit and scale-aware but is not a claim of bit-for-bit TA-Lib behavior.
Evidence boundary
Sources establish the chart geometry, names, configurable-convention context, and contextual interpretation named in their records. They do not certify repository thresholds, synthetic bars, matches, or future returns.
Full dependency-light reference implementations in both supported languages.
/** Transparent, repository-selected D06 candle and pattern contracts. */
type AnyMap = Record<string, any>;
const F02_TITLES: Record<string, string> = {
"D06-F02-A01": "Doji", "D06-F02-A02": "Dragonfly Doji", "D06-F02-A03": "Gravestone Doji",
"D06-F02-A04": "Marubozu", "D06-F02-A05": "Spinning Top", "D06-F02-A06": "Hammer",
"D06-F02-A07": "Hanging Man", "D06-F02-A08": "Inverted Hammer", "D06-F02-A09": "Shooting Star",
};
const F03_TITLES: Record<string, string> = {
"D06-F03-A01": "Bullish Engulfing", "D06-F03-A02": "Bearish Engulfing", "D06-F03-A03": "Bullish Harami",
"D06-F03-A04": "Bearish Harami", "D06-F03-A05": "Piercing Line", "D06-F03-A06": "Dark Cloud Cover",
"D06-F03-A07": "Tweezer Top", "D06-F03-A08": "Tweezer Bottom",
};
function finite(value: unknown, name: string): number {
if (typeof value !== "number" || !Number.isFinite(value)) throw new TypeError(`${name} must be a finite number`);
return value;
}
function positive(value: unknown, name: string): number { const n = finite(value, name); if (n <= 0) throw new RangeError(`${name} must be positive`); return n; }
function nonnegative(value: unknown, name: string): number { const n = finite(value, name); if (n < 0) throw new RangeError(`${name} must be non-negative`); return n; }
function candle(value: unknown, name: string): AnyMap {
if (!value || typeof value !== "object" || Array.isArray(value)) throw new TypeError(`${name} must be an object`);
const source = value as AnyMap;
const result = {open: finite(source.open, `${name}.open`), high: finite(source.high, `${name}.high`), low: finite(source.low, `${name}.low`), close: finite(source.close, `${name}.close`)};
if (result.high < Math.max(result.open, result.close, result.low)) throw new RangeError(`${name}.high is below another OHLC value`);
if (result.low > Math.min(result.open, result.close, result.high)) throw new RangeError(`${name}.low is above another OHLC value`);
return result;
}
function anatomy(c: AnyMap): AnyMap {
const bodyHigh = Math.max(c.open, c.close), bodyLow = Math.min(c.open, c.close);
return {body_high: bodyHigh, body_low: bodyLow, body: bodyHigh-bodyLow, upper_shadow: c.high-bodyHigh, lower_shadow: bodyLow-c.low, range: c.high-c.low, direction: c.close>c.open?"bullish":c.close<c.open?"bearish":"neutral"};
}
function numberList(value: unknown, name: string): number[] {
if (!Array.isArray(value)) throw new TypeError(`${name} must be an array`);
return value.map((item,index)=>nonnegative(item, `${name}[${index}]`));
}
function median(values: number[]): number { const ordered=[...values].sort((a,b)=>a-b), n=ordered.length; return n%2?ordered[(n-1)/2]:(ordered[n/2-1]+ordered[n/2])/2; }
function referenceScales(data: AnyMap): [number|null,number|null,number] {
const bodies=numberList(data.prior_bodies,"prior_bodies"), ranges=numberList(data.prior_ranges,"prior_ranges");
if(bodies.length!==ranges.length) throw new RangeError("prior_bodies and prior_ranges must have equal length");
if(bodies.some((body,index)=>body>ranges[index])) throw new RangeError("a prior body cannot exceed its range");
if(bodies.length<5) return [null,null,bodies.length];
return [median(bodies.slice(-10)),median(ranges.slice(-10)),Math.min(10,bodies.length)];
}
function context(data: AnyMap): string { if(!["uptrend","downtrend","sideways"].includes(data.trend_context)) throw new RangeError("trend_context must be uptrend, downtrend, or sideways"); return data.trend_context; }
function patternResult(args: AnyMap): AnyMap {
let state:string, matched:boolean;
if(args.bodyScale===null||args.rangeScale===null){state="warmup";matched=false;}
else if(args.matchedGeometry&&args.requiredContext!==null&&args.actualContext!==args.requiredContext){state="wrong-context";matched=false;}
else {matched=args.matchedGeometry;state=matched?"matched":"not-matched";}
return {topic_id:args.topicId,pattern:args.title,matched,state,direction:args.a.direction,trend_context:args.actualContext,required_context:args.requiredContext,body:args.a.body,upper_shadow:args.a.upper_shadow,lower_shadow:args.a.lower_shadow,range:args.a.range,body_scale:args.bodyScale,range_scale:args.rangeScale,history_count:args.historyCount,geometry_score:args.geometryScore,checks:args.checks,failed_checks:Object.keys(args.checks).filter(key=>!args.checks[key])};
}
function shadowToBody(data: AnyMap): AnyMap {
const c=candle(data.candle,"candle"), tick=positive(data.tick_size,"tick_size"), a=anatomy(c), body=a.body, effective=Math.max(body,tick);
const upperRaw=body===0?null:a.upper_shadow/body, lowerRaw=body===0?null:a.lower_shadow/body, upperEffective=a.upper_shadow/effective, lowerEffective=a.lower_shadow/effective;
const dominant=upperEffective>lowerEffective?"upper":lowerEffective>upperEffective?"lower":"balanced";
return {state:body===0?"zero-body":"calculated",body,upper_shadow:a.upper_shadow,lower_shadow:a.lower_shadow,range:a.range,tick_size:tick,effective_body:effective,upper_to_body:upperRaw,lower_to_body:lowerRaw,upper_to_effective_body:upperEffective,lower_to_effective_body:lowerEffective,upper_to_range:a.range===0?null:a.upper_shadow/a.range,lower_to_range:a.range===0?null:a.lower_shadow/a.range,dominant_shadow:dominant,dominant_shadow_ratio:Math.max(upperEffective,lowerEffective)};
}
function gapClassification(data: AnyMap): AnyMap {
const previous=candle(data.previous,"previous"), current=candle(data.current,"current"), tick=positive(data.tick_size,"tick_size"), minimumTicks=nonnegative(data.minimum_gap_ticks,"minimum_gap_ticks"), threshold=tick*minimumTicks;
const pa=anatomy(previous), ca=anatomy(current);
const candidates:AnyMap={up:{open:current.open-previous.close,body:ca.body_low-pa.body_high,"full-range":current.low-previous.high},down:{open:previous.close-current.open,body:pa.body_low-ca.body_high,"full-range":previous.low-current.high}};
const qualifies=(distance:number)=>distance>0&&distance>=threshold;
let direction="none", gapType="overlap", distance=0;
outer: for(const candidateDirection of ["up","down"]) for(const candidateType of ["full-range","body","open"]){const d=candidates[candidateDirection][candidateType];if(qualifies(d)){direction=candidateDirection;gapType=candidateType;distance=d;break outer;}}
return {state:direction!=="none"?"gap":"overlap",gap_direction:direction,gap_type:gapType,gap_distance:distance,threshold_distance:threshold,open_gap_up:qualifies(candidates.up.open),open_gap_down:qualifies(candidates.down.open),body_gap_up:qualifies(candidates.up.body),body_gap_down:qualifies(candidates.down.body),full_range_gap_up:qualifies(candidates.up["full-range"]),full_range_gap_down:qualifies(candidates.down["full-range"]),signed_open_gap:current.open-previous.close,previous_close:previous.close,current_open:current.open};
}
function trendContext(data: AnyMap): AnyMap {
const closes=numberList(data.prior_closes,"prior_closes"), ranges=numberList(data.prior_ranges,"prior_ranges"), lookback=data.lookback;
if(!Number.isInteger(lookback)||lookback<3) throw new RangeError("lookback must be an integer of at least 3");
const efficiencyFloor=nonnegative(data.minimum_efficiency,"minimum_efficiency"), moveFloor=nonnegative(data.minimum_move_scale,"minimum_move_scale");
if(efficiencyFloor>1) throw new RangeError("minimum_efficiency cannot exceed 1");
if(closes.length!==ranges.length) throw new RangeError("prior_closes and prior_ranges must have equal length");
if(closes.length<lookback) return {state:"warmup",trend_context:"warmup",history_count:closes.length,net_move:null,path_move:null,efficiency:null,range_scale:null,normalized_move:null};
const sample=closes.slice(-lookback), rangeSample=ranges.slice(-lookback), net=sample.at(-1)!-sample[0];
let path=0;for(let i=1;i<sample.length;i++)path+=Math.abs(sample[i]-sample[i-1]);
const efficiency=path===0?0:Math.abs(net)/path, scale=median(rangeSample);
if(scale===0)return {state:"zero-scale",trend_context:"sideways",history_count:lookback,net_move:net,path_move:path,efficiency,range_scale:scale,normalized_move:null};
const normalized=net/scale, trend=efficiency>=efficiencyFloor&&normalized>=moveFloor?"uptrend":efficiency>=efficiencyFloor&&normalized<=-moveFloor?"downtrend":"sideways";
return {state:"ready",trend_context:trend,history_count:lookback,net_move:net,path_move:path,efficiency,range_scale:scale,normalized_move:normalized};
}
function singlePattern(topicId:string,data:AnyMap):AnyMap{
const c=candle(data.current,"current"),tick=positive(data.tick_size,"tick_size"),actualContext=context(data),[bodyScale,rangeScale,count]=referenceScales(data),a=anatomy(c);
if(bodyScale===null||rangeScale===null)return patternResult({topicId,title:F02_TITLES[topicId],a,matchedGeometry:false,requiredContext:null,actualContext,bodyScale:null,rangeScale:null,historyCount:count,geometryScore:0,checks:{minimum_history:false}});
const effectiveBody=Math.max(a.body,tick),dojiLimit=.10*rangeScale,shortShadowLimit=.05*rangeScale;let checks:Record<string,boolean>,requiredContext:string|null=null,score:number;
switch(topicId){
case"D06-F02-A01":checks={near_zero_body:a.body<=dojiLimit};score=1-a.body/Math.max(dojiLimit,tick);break;
case"D06-F02-A02":checks={doji_body:a.body<=dojiLimit,short_upper_shadow:a.upper_shadow<=shortShadowLimit,long_lower_shadow:a.lower_shadow>=.60*rangeScale};score=a.lower_shadow/Math.max(rangeScale,tick);break;
case"D06-F02-A03":checks={doji_body:a.body<=dojiLimit,long_upper_shadow:a.upper_shadow>=.60*rangeScale,short_lower_shadow:a.lower_shadow<=shortShadowLimit};score=a.upper_shadow/Math.max(rangeScale,tick);break;
case"D06-F02-A04":checks={long_body:a.body>=1.20*bodyScale,short_upper_shadow:a.upper_shadow<=shortShadowLimit,short_lower_shadow:a.lower_shadow<=shortShadowLimit};score=a.body/Math.max(bodyScale,tick);break;
case"D06-F02-A05":checks={short_body:a.body<=.60*bodyScale+1e-12,material_upper_shadow:a.upper_shadow+1e-12>=Math.max(a.body,.20*rangeScale),material_lower_shadow:a.lower_shadow+1e-12>=Math.max(a.body,.20*rangeScale)};score=Math.min(a.upper_shadow,a.lower_shadow)/effectiveBody;break;
case"D06-F02-A06":case"D06-F02-A07":requiredContext=topicId.endsWith("A06")?"downtrend":"uptrend";checks={compact_body:a.body<=.75*bodyScale,long_lower_shadow:a.lower_shadow>=2*effectiveBody,short_upper_shadow:a.upper_shadow<=.25*effectiveBody,body_near_high:a.upper_shadow<=.25*Math.max(a.range,tick)};score=a.lower_shadow/effectiveBody;break;
case"D06-F02-A08":case"D06-F02-A09":requiredContext=topicId.endsWith("A08")?"downtrend":"uptrend";checks={compact_body:a.body<=.75*bodyScale,long_upper_shadow:a.upper_shadow>=2*effectiveBody,short_lower_shadow:a.lower_shadow<=.25*effectiveBody,body_near_low:a.lower_shadow<=.25*Math.max(a.range,tick)};score=a.upper_shadow/effectiveBody;break;
default:throw new RangeError(`unsupported single-candle topic ${topicId}`);
}
return patternResult({topicId,title:F02_TITLES[topicId],a,matchedGeometry:Object.values(checks).every(Boolean),requiredContext,actualContext,bodyScale,rangeScale,historyCount:count,geometryScore:score,checks});
}
function twoPattern(topicId:string,data:AnyMap):AnyMap{
const first=candle(data.first,"first"),second=candle(data.second,"second"),tick=positive(data.tick_size,"tick_size"),tolerance=tick*nonnegative(data.price_tolerance_ticks,"price_tolerance_ticks"),actualContext=context(data),[bodyScale,rangeScale,count]=referenceScales(data),a=anatomy(first),b=anatomy(second);
if(bodyScale===null||rangeScale===null){const result=patternResult({topicId,title:F03_TITLES[topicId],a:{body:b.body,upper_shadow:b.upper_shadow,lower_shadow:b.lower_shadow,range:b.range,direction:b.direction},matchedGeometry:false,requiredContext:null,actualContext,bodyScale:null,rangeScale:null,historyCount:count,geometryScore:0,checks:{minimum_history:false}});Object.assign(result,{first_direction:a.direction,second_direction:b.direction,first_body:a.body,second_body:b.body,price_tolerance:tolerance});return result;}
const longFirst=a.body>=1.20*bodyScale,shortSecond=b.body<=.60*bodyScale,midpoint=(first.open+first.close)/2;let requiredContext:string,checks:Record<string,boolean>,score:number;
switch(topicId){
case"D06-F03-A01":requiredContext="downtrend";checks={first_bearish:a.direction==="bearish",second_bullish:b.direction==="bullish",lower_end_engulfed:b.body_low<=a.body_low,upper_end_engulfed:b.body_high>=a.body_high,strictly_larger_body:b.body>a.body};score=b.body/Math.max(a.body,tick);break;
case"D06-F03-A02":requiredContext="uptrend";checks={first_bullish:a.direction==="bullish",second_bearish:b.direction==="bearish",lower_end_engulfed:b.body_low<=a.body_low,upper_end_engulfed:b.body_high>=a.body_high,strictly_larger_body:b.body>a.body};score=b.body/Math.max(a.body,tick);break;
case"D06-F03-A03":requiredContext="downtrend";checks={first_bearish:a.direction==="bearish",long_first:longFirst,second_bullish:b.direction==="bullish",short_second:shortSecond,contained_low:b.body_low>=a.body_low,contained_high:b.body_high<=a.body_high,strict_containment:b.body_low>a.body_low||b.body_high<a.body_high};score=1-b.body/Math.max(a.body,tick);break;
case"D06-F03-A04":requiredContext="uptrend";checks={first_bullish:a.direction==="bullish",long_first:longFirst,second_bearish:b.direction==="bearish",short_second:shortSecond,contained_low:b.body_low>=a.body_low,contained_high:b.body_high<=a.body_high,strict_containment:b.body_low>a.body_low||b.body_high<a.body_high};score=1-b.body/Math.max(a.body,tick);break;
case"D06-F03-A05":requiredContext="downtrend";checks={first_bearish:a.direction==="bearish",long_first:longFirst,second_bullish:b.direction==="bullish",gap_below_first_low:second.open<=first.low-tick,close_above_midpoint:second.close>midpoint,close_below_first_open:second.close<first.open};score=(second.close-midpoint)/Math.max(a.body,tick);break;
case"D06-F03-A06":requiredContext="uptrend";checks={first_bullish:a.direction==="bullish",long_first:longFirst,second_bearish:b.direction==="bearish",gap_above_first_high:second.open>=first.high+tick,close_below_midpoint:second.close<midpoint,close_above_first_open:second.close>first.open};score=(midpoint-second.close)/Math.max(a.body,tick);break;
case"D06-F03-A07":requiredContext="uptrend";checks={first_bullish:a.direction==="bullish",second_bearish:b.direction==="bearish",highs_within_tolerance:Math.abs(first.high-second.high)<=tolerance,first_upper_rejection:a.upper_shadow>=.50*Math.max(a.body,tick),second_upper_rejection:b.upper_shadow>=.50*Math.max(b.body,tick)};score=Math.max(0,1-Math.abs(first.high-second.high)/Math.max(tolerance,tick));break;
case"D06-F03-A08":requiredContext="downtrend";checks={first_bearish:a.direction==="bearish",second_bullish:b.direction==="bullish",lows_within_tolerance:Math.abs(first.low-second.low)<=tolerance,first_lower_rejection:a.lower_shadow>=.50*Math.max(a.body,tick),second_lower_rejection:b.lower_shadow>=.50*Math.max(b.body,tick)};score=Math.max(0,1-Math.abs(first.low-second.low)/Math.max(tolerance,tick));break;
default:throw new RangeError(`unsupported two-candle topic ${topicId}`);
}
const result=patternResult({topicId,title:F03_TITLES[topicId],a:{body:b.body,upper_shadow:b.upper_shadow,lower_shadow:b.lower_shadow,range:b.range,direction:b.direction},matchedGeometry:Object.values(checks).every(Boolean),requiredContext,actualContext,bodyScale,rangeScale,historyCount:count,geometryScore:score,checks});
Object.assign(result,{first_direction:a.direction,second_direction:b.direction,first_body:a.body,second_body:b.body,first_body_low:a.body_low,first_body_high:a.body_high,second_body_low:b.body_low,second_body_high:b.body_high,first_high:first.high,second_high:second.high,first_low:first.low,second_low:second.low,midpoint,price_tolerance:tolerance});return result;
}
export function calculate(topicId:string,inputs:AnyMap):AnyMap{
if(!inputs||typeof inputs!=="object"||Array.isArray(inputs))throw new TypeError("inputs must be an object");
if(topicId==="D06-F01-A03")return shadowToBody(inputs);
if(topicId==="D06-F01-A04")return gapClassification(inputs);
if(topicId==="D06-F01-A05")return trendContext(inputs);
if(topicId in F02_TITLES)return singlePattern(topicId,inputs);
if(topicId in F03_TITLES)return twoPattern(topicId,inputs);
throw new RangeError(`unsupported topic id ${topicId}`);
}
The embedded lab now expands to its full document height, keeping the article as the only scroll surface.