Calculate raw and tick-floored shadow-to-body ratios, preserve null ratios for a true zero body, and identify the dominant shadow without pretending the ratio is a pattern signal.
The decision this tutorial makes visible
Downstream pattern rules often say a shadow is long or short relative to the body. A stable foundation must expose both the literal ratio and a finite tick-floored diagnostic while keeping zero-body semantics visible.
The precise question is: How large is each shadow relative to the real body without hiding the exact zero-body case?
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
A zero body is not a tiny positive denominator. Returning null preserves that fact, while the tick-floored diagnostic gives downstream teaching rules a finite comparison without rewriting the literal ratio.
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 candle. Raw ratios divide by the literal body and are null at body zero; finite diagnostics divide by max(body, tick). Range ratios remain separate. No long-shadow threshold or pattern name is selected.
| Variant | Definition | Best use | Main limitation |
|---|---|---|---|
| Literal ratio | shadow/body | Exact geometry | Undefined at zero body |
| Tick-floored diagnostic | shadow/max(body,tick) | Finite downstream checks | Not the literal ratio |
| Range ratio | shadow/range | Within-candle share | Answers a different question |
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 candle. Raw ratios divide by the literal body and are null at body zero; finite diagnostics divide by max(body, tick). Range ratios remain separate. No long-shadow threshold or pattern name is selected. | 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 dominant_shadow_ratio 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
B=|C-O|, U=H-max(O,C), D=min(O,C)-L; U/B and D/B are null when B=0; finite diagnostics use U/max(B,tick) and D/max(B,tick).
| Symbol | Meaning | Unit | Policy |
|---|---|---|---|
| B | Real body | price | Literal denominator |
| U,D | Upper and lower shadows | price | Non-negative |
| tick | Minimum tradable increment | price | Positive floor for finite diagnostic |
- 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 OHLC and positive tick size.
- Calculate body, shadows, and range.
- Return literal ratios only when body is positive.
- Calculate tick-floored diagnostics and dominant shadow.
Production-minded operational checklist
- Validate one construction basis
- Record tick size
- Preserve null at zero body
- Carry raw components with ratios
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,
dominant_shadow_ratio, is 1.5. The complete input and output
are in datasets/canonical-input.json and datasets/expected-output.json.
The synthetic candle has body 2, upper shadow 3, and lower shadow 3. Both literal and tick-floored shadow ratios equal 1.5, so dominance is balanced and 3+2+3 equals the range 8.
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. | calculated | dominant ratio 1.5000 | calculated; balanced | 1 |
| Zero-body boundary | Step 30 · comparison focus | Show null literal ratios with finite tick diagnostics. | zero-body | dominant ratio 40.0000 | zero-body; balanced | 1 |
| Upper-shadow dominance | Step 30 · comparison focus | Make the upper shadow dominant. | calculated | dominant ratio 3.0000 | calculated; upper | 1 |
| Lower-shadow dominance | Step 30 · comparison focus | Make the lower shadow dominant. | calculated | dominant ratio 4.0000 | calculated; lower | 1 |
| One-tick body | Step 30 · comparison focus | Exercise the exact tick floor. | calculated | dominant ratio 30.0000 | calculated; lower | 1 |
| Flat candle | Step 30 · comparison focus | Show zero range and zero body. | zero-body | dominant ratio 0.0000 | zero-body; balanced | 1 |
| Large-body control | Step 30 · comparison focus | Compare short shadows with a large body. | calculated | dominant ratio 0.2000 | calculated; balanced | 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:
- body > 0 — Return literal and effective ratios, because Division is defined.
- body = 0 — Return null literal ratios and finite tick diagnostics, because Do not fabricate infinity or zero.
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:
- Body/shadow/range partition
- Literal and effective denominators
- Dominant-shadow tie handling
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: not useful. A named historical bar is not useful for defining deterministic candle geometry. The packages use synthetic, fully redistributable OHLC fixtures; a production substitution requires licensed point-in-time bars with instrument, venue, interval, session, price basis, retrieval time, revision state, and tick size.
The primary sources are CME candlestick chart lesson, Nasdaq candlestick glossary, TA-Lib candle settings. 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 Shadow-to-Body Ratio under an explicit definition and carry its structured evidence into Gap Classification. The learning flow is: Scale-Aware Body Classification → Shadow-to-Body Ratio → Gap Classification. Carry the result forward only with its scope, clock, state, and evidence label.
Rendered from the canonical Mermaid sources linked by this article.
Shadow-to-Body Ratio calculation flow
This flow identifies the selected calculation stages and the structured output.
Takeaway: A true zero body changes ratio semantics; a tick floor is an explicit diagnostic choice, not a hidden repair.
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 — Chart Types: Candlestick, Line, Bar
- Organization or authors: CME Group
- Source type: Official exchange education
- Publication or effective date: current
- Version: Accessed 2026-08-01
- URL or DOI: https://www.cmegroup.com/education/courses/technical-analysis/chart-types-candlestick-line-bar
- Accessed: 2026-08-01
- Jurisdiction: Global futures education
- Supports: Candles encode open, high, low, and close; body and wick sizes describe bar geometry, and gaps compare adjacent bars.
- Limitations: Educational interpretation does not define the repository thresholds or prove a forecast.
S2 — Candlestick chart
- Organization or authors: Nasdaq
- Source type: Official market glossary
- Publication or effective date: current
- Version: Accessed 2026-08-01
- URL or DOI: https://www.nasdaq.com/glossary/b/candlestick-chart
- Accessed: 2026-08-01
- Jurisdiction: General market terminology
- Supports: The real body spans open and close while the vertical lines reach the period high and low.
- Limitations: Does not standardize pattern thresholds, trend filters, or empirical meaning.
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.
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.