qualify

name

qualify

description

Qualify a stock for entry by evaluating three complementary perspectives: Momentum Breakout (SEPA), Range Trading, and Oversold Bounce. Works in two modes: Data mode (primary) — give a ticker and the skill computes all indicators from OHLCV data; Screenshot mode — paste any chart image and the skill extracts what it can visually. Use this skill when the user says "qualify NVDA", "does AAPL qualify", "is this buyable", "check this setup", "analyze this chart", shares a chart screenshot, or any variation that implies they want to know if a stock meets entry criteria.

Qualify

Qualify a stock for entry from three independent perspectives, then synthesize a single verdict. Each perspective covers a different market condition — they complement, never conflict when applied correctly.

Perspective	What it buys	Market condition	Hold period
Momentum Breakout	Strength at pivotal points	Stage 2 uptrend	Weeks to months
Range Trading	Support within horizontal range	Sideways (Stage 1/3)	Days to 2 weeks
Oversold Bounce	Excessive decline recovery	Sharp pullback in bull market	3-10 days

Mode detection

Determine the mode before anything else:

• Data mode — the user names a ticker (e.g., "analyze NVDA", "read AAPL"). Compute all indicators numerically from OHLCV CSVs. This is the primary mode: precise, no external tool needed.
• Screenshot mode — the user pastes a chart image. Extract values visually from the image. Platform-agnostic (TradingView, Webull, Thinkorswim, Yahoo Finance, or any charting tool).

If the user provides both a ticker and a screenshot, prefer data mode for indicator values and use the screenshot for visual pattern assessment (VCP shape, candle patterns, trendline slopes).

Before analysis

Gather system context

Read these files to personalize the analysis. Skip any that don't exist. All paths are relative to ~/.lafmm/.

1. profile.md — account size and risk tolerance for position sizing. If account size is specified, use it. If not, ask the user.
2. accounts/*/capital/*.csv — latest capital value (last row of most recent file). This is the actual number for position sizing.
3. cache/ — if the ticker appears in any group, read its Livermore column state. Cross-reference: does the engine agree with your momentum assessment?
4. accounts/*/journal/ — search for the ticker. If the user has traded it before, note their history: win rate, avg hold, last trade.
5. insights/ — read the current year. If the agent has noted a pattern relevant to this setup (e.g., "tends to chase overextended entries"), flag it during analysis.
6. data/_meta/{TICKER}.json — if it exists, read sector, market cap, beta. Provides fundamental context without requiring the user to say anything.
7. accounts/*/scorecard.md — if it exists, check for ticker-specific edge ("user reads SNXX exceptionally well"), behavioral warnings ("user tends to FOMO into this setup type"), and hypothesis track record. Weight the user's conviction higher when their observation accuracy for this ticker/setup is strong.

Check market context

Read cache/market.md for the broad market trend. This affects confidence in every perspective:

• Market BULLISH → full confidence in momentum, higher bounce success
• Market NEUTRAL → reduced position sizes, prefer range setups
• Market BEARISH → oversold bounces are much riskier, momentum rarely works

If VIX/VIX3M data exists in data/us-indices/_ref/, check for regime: RISK_OFF (VIX/VIX3M > 1.10 for 7+ days) means elevated caution across all perspectives.

Step 1: Load data and compute

This step loads all data and computes all technical indicators. Perspective-specific values (support levels, panic lows) are identified in Step 2 — those are analytical judgments, not indicator computations. No subsequent step should re-read CSV files.

Data mode

Find the ticker's OHLCV CSV in data/{group}/{TICKER}/. If the ticker exists in multiple groups, use --group context or pick the primary one. Concatenate year CSVs to build the full price series.

Render charts for visual pattern assessment (VCP shape, base structure, trendline slopes):

lafmm chart candle <TICKER> --period 90d
lafmm chart overlay <TICKER> --ma sma:50 --ma sma:150 --ma sma:200 --ma ema:21
lafmm chart rsi <TICKER>
lafmm chart bollinger <TICKER>
lafmm chart volume <TICKER>

Compute all indicators numerically from the CSV:

Indicator	Computation
Price (OHLC)	Last row of CSV
SMA 50, 150, 200	Last value of sma(close, N)
EMA 21	Last value of ema(close, 21)
Bollinger Bands	Last values of bollinger(close, 20, 2.0) → (mid, upper, lower)
RSI	Last value of rsi(close, 14)
ATR	Last value of atr(high, low, close, 14)
Volume today	Last row volume
Volume 50-day avg	Last value of sma(volume, 50)
52-week high	max(high[-252:])
52-week low	min(low[-252:])
Recent peak (3-month)	max(high[-63:])
SMA 200 (20 days ago)	sma(close, 200)[-20] — for trend slope

Every number is exact — no estimation, no "UNREADABLE" fields.

Staleness check: read the date of the last CSV row. If it is more than 1 trading day old (accounting for weekends and holidays), warn: "Data through {date} — {N} trading days stale. Run daily-update before analysis for current values."

Relative strength

Measures whether this stock is leading or lagging its peers and the broad market. Computed as the ratio of cumulative returns:

RS = (stock_close / stock_close[N days ago])
   / (benchmark_close / benchmark_close[N days ago])

RS > 1.0 means the stock outperformed the benchmark over the period. RS < 1.0 means it underperformed.

Compute two benchmarks, two lookbacks — four RS values total:

Benchmark	Source	50-day RS	200-day RS
SPY (broad market)	data/us-indices/SPY/	—	—
Sector median	all tickers in data/{group}/	—	—

For sector RS: compute the RS ratio for every ticker in the group against SPY, then rank this ticker's RS within the group.

Sector rank interpretation:

Percentile	Label
≥ 75th	LEADER
25th–75th	IN_LINE
≤ 25th	LAGGARD

Cross-timeframe interpretation:

50-day	200-day	Reading
LEADER	LEADER	Sustained strength — highest quality
LEADER	LAGGARD	Accelerating — recent turnaround, promising but unproven
LAGGARD	LEADER	Decelerating — was strong, losing momentum now
LAGGARD	LAGGARD	Sustained weakness — avoid for momentum setups

Weekly trend confirmation

Checks whether the higher timeframe agrees with the daily picture. Resample daily bars to weekly (group by ISO week, use last trading day's close per week).

The weekly periods mirror the daily trend template for consistency: SMA 10w approximates daily SMA 50, SMA 40w approximates daily SMA 200.

Three checks:

1. Price > SMA 10w (weekly short-term trend intact)
2. SMA 10w > SMA 40w (weekly MA alignment)
3. SMA 40w today > SMA 40w 4 weeks ago (weekly long-term trend rising)

Score	Label
3/3	WEEKLY_CONFIRMED
2/3	WEEKLY_NEUTRAL
0–1/3	WEEKLY_DIVERGENT

Earnings proximity

Read data/_meta/_earnings.json. If the ticker has a cached date:

Condition	Label
Earnings within 5 trading days (forward)	EARNINGS_IMMINENT
Earnings passed within 10 calendar days	POST_EARNINGS
Otherwise	EARNINGS_CLEAR

If the ticker is not in the cache, run the earnings-calendar script to fetch it. If it still returns nothing, label EARNINGS_UNKNOWN and note that the date could not be determined.

POST_EARNINGS context: signals that form after an earnings report are higher quality because the market has already repriced E[CF]. Note this in the synthesis.

EARNINGS_IMMINENT context: the trend-based verdict may be overridden by the earnings outcome in either direction. This is a risk factor, not an automatic sizing reduction — the user decides whether to hold through earnings. Flag it prominently.

Screenshot mode

Extract indicator values visually from the chart image. Read from legends, axis labels, and indicator panels — not from eyeballing line positions.

Tips for different platforms:

• TradingView — values in the top-left legend next to indicator names (e.g., "SMA (50, close) 679.48")
• Webull/Thinkorswim — values often in a data panel or tooltip
• Yahoo Finance — limited indicators; may only have price + volume
• Any platform — axis scales give price range; volume bars have their own axis

Extract what you can. Mark any value that is not readable as "UNREADABLE" and note which checks are affected. The minimum for analysis is: price + SMA 50 + SMA 200.

Screenshot mode limitations: relative strength, weekly trend confirmation, and earnings proximity cannot be computed from a screenshot alone. State this explicitly in the output:

"RS, weekly trend, and earnings proximity are unavailable in screenshot mode. These factors are not reflected in the confidence assessment below."

If the ticker is identifiable from the image AND exists in data/, offer to switch to data mode: "I can see this is NVDA. Want me to run data mode for exact values including RS and weekly trend?"

Step 2: Run all three perspectives

Read the relevant reference files and evaluate:

1. Momentum — read references/momentum.md. Always run this first. The trend template score determines whether Range is applicable.
2. Range — read references/range.md. Only applicable if momentum trend template < 6/8 or SMA 50 is flat.
3. Oversold — read references/oversold.md. Only applicable if the stock has declined ≥ 20% from a recent peak.

Evaluate each perspective independently. Do not let one perspective's conclusion influence another's evaluation. Then synthesize.

Step 3: Assess the candlestick

Data mode: read the last 3–5 rows of the CSV. Compute body size (|close - open|), upper shadow (high - max(open, close)), lower shadow (min(open, close) - low), body as % of range (|close - open| / (high - low) × 100). Identify patterns from the numbers: hammer (lower shadow ≥ 2× body, small upper shadow), doji (body < 5% of range), engulfing (today's body envelops yesterday's).

Screenshot mode: read the rightmost candle visually. Calculate body size, shadows, body as % of range. Identify significant patterns only when clearly visible.

If the candle is unremarkable, say so and move on.

Step 4: Synthesize

Perspective verdict

The decision tree for the final action:

1. If Momentum = BUY_NOW → final action follows momentum (strongest)
2. If Momentum = WATCH_FOR_BREAKOUT → set alert at pivot, prepare
3. If Momentum = WAIT_FOR_PULLBACK → check if Oversold applies
4. If Momentum = NO_SETUP or AVOID → check Range, then Oversold
5. If nothing applies → WAIT (no trade, hold cash)

Confidence assessment

After determining the perspective verdict, assess conviction using the cross-cutting factors from Step 1. These modify how much confidence to place in the verdict — they do not change the verdict itself.

Factor	Effect
RS LEADER (sector) + RS > 1.0 vs SPY	Higher conviction
RS IN_LINE + RS ≈ 1.0 vs SPY	Neutral — no adjustment
RS LAGGARD (sector) + RS < 1.0 vs SPY	Lower conviction
WEEKLY_CONFIRMED	Higher conviction
WEEKLY_NEUTRAL	Neutral — no adjustment
WEEKLY_DIVERGENT	Lower conviction — daily and weekly disagree
POST_EARNINGS	Higher conviction if signal formed after the report
EARNINGS_IMMINENT	Risk flag — user decides, do not auto-adjust

Synthesize the factors into one of three conviction levels:

• High conviction — RS leader, weekly confirmed, no earnings risk. Full position per perspective rules.
• Normal conviction — mixed or neutral factors. Standard sizing.
• Low conviction — RS laggard or weekly divergent. Reduce to 50% of calculated position, or skip if multiple negatives compound.

Present the reasoning explicitly: which factors support the verdict, which argue against, and how they net out.

Conflict safeguards

• Momentum ≥ 6/8 trend → Range is automatically NO_RANGE
• Momentum BUY_NOW and Oversold BUY cannot coexist (one buys highs, the other buys lows — if both seem to apply, re-evaluate)
• WEEKLY_DIVERGENT with Momentum BUY_NOW → downgrade to WATCH_FOR_BREAKOUT (weekly trend must confirm for immediate entry)
• When uncertain → default to the more conservative action

Step 5: Position sizing

Read references/position-sizing.md for formulas. Use account size from profile.md or latest capital CSV — never hardcode a default.

Apply the conviction level from Step 4:

• High conviction → full position per perspective rules
• Normal conviction → standard sizing (no adjustment)
• Low conviction → 50% of calculated shares

If EARNINGS_IMMINENT, note the gap risk alongside the position size. Do not auto-halve — present the risk and let the user decide. Example: "Calculated position: 150 shares ($X). Note: earnings in 3 days — gap risk exists. Consider reducing if you do not intend to hold through the report."

Step 6: Cross-reference with LAFMM

If the ticker exists in cache/:

• Does the Livermore column agree with your momentum assessment?
• Are there active signals (BUY, SELL, DANGER)?
• Where are the pivotal points relative to current price?

If the user's genome data (from lafmm stats --json) shows this setup type is a leak pattern for them, flag it clearly.

Output format

Structure the response in this order:

1. Data Extraction — table of all indicator values. Data mode: exact numbers including RS, weekly trend, earnings status. Screenshot mode: extracted values + "UNREADABLE" notes + degradation notice for RS/weekly/earnings.
2. Perspective A: Momentum — trend template, stretch, VCP, volume, RSI, verdict
3. Perspective B: Range — identification, position, entry signals, verdict
4. Perspective C: Oversold — prerequisites, decline, signals, confirmation, verdict
5. Synthesis & Final Action — perspective verdict, confidence assessment (RS + weekly + earnings), conviction level, position sizing, action items, risks, what would change the verdict
6. K-Line Note — today's candle
7. LAFMM Cross-Reference — if data exists

Show all math explicitly. Write the formula, plug in numbers, show the result. The user makes real money decisions from these numbers — precision matters.

Present evidence, not predictions. The user decides what to do.