feat: add Citations and WatchlistChat components, integrate with API
- Implemented Citations component to display citation data. - Created WatchlistDrawer and ChatSidebar components for managing watchlists and AI chat functionality. - Integrated API calls for watchlist management and chat interactions. - Updated index.tsx to include new components in the main application layout. - Added API client in lib/api.ts for structured API interactions. - Developed Alerts, Dashboard, Portfolio, Routines, Screener, and Report pages with relevant data fetching and UI components. - Introduced styles in tokens.css for consistent theming across the application. - Configured TypeScript and Vite for project setup and development.
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asepharyana
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package agents
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import (
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"context"
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"encoding/json"
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"fmt"
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"sort"
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"strings"
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"flowsight/internal/model"
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"flowsight/internal/sectors"
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)
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// AnalyzeTechnical (A5) scores momentum + volume anomaly + liquidity.
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// Anomaly: volume > 2x 20d avg. Liquidity grade from free-float %.
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func AnalyzeTechnical(ctx context.Context, d Deps, ticker string) model.AgentResult {
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_ = ctx
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ticker = strings.ToUpper(ticker)
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res := model.AgentResult{Summary: "no technical snapshots available"}
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var daily []sectors.DailyBar
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dailyDate, dailyOK := payload(d.DB, ticker, "daily", &daily)
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var movers struct {
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TopGainers map[string][]sectors.MoverRow `json:"top_gainers"`
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TopLosers map[string][]sectors.MoverRow `json:"top_losers"`
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}
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moverDate, moverOK := payload(d.DB, "IDX", "top-changes", &movers)
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if !dailyOK && !moverOK {
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if vols, dates, err := d.DB.DailyVolumes(ticker, 25); err == nil && len(vols) > 0 {
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return technicalFromStored(ticker, vols, dates)
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}
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return res
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}
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if dailyOK {
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res.Citations = append(res.Citations, model.Cite("v2/daily/"+ticker+"/", ticker, dailyDate))
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}
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if moverOK {
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res.Citations = append(res.Citations, model.Cite("v2/companies/top-changes/", "IDX", moverDate))
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}
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momentum := "flat"
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moverRank := ""
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if moverOK {
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for period, rows := range movers.TopGainers {
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for i, r := range rows {
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if strings.HasPrefix(strings.ToUpper(r.Symbol), ticker) {
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momentum = "up"
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moverRank = fmt.Sprintf("top-gainer #%d (%s)", i+1, period)
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}
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}
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}
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for period, rows := range movers.TopLosers {
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for i, r := range rows {
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if strings.HasPrefix(strings.ToUpper(r.Symbol), ticker) {
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momentum = "down"
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moverRank = fmt.Sprintf("top-loser #%d (%s)", i+1, period)
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}
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}
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}
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}
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volMult, volDate, lastVol := 0.0, "", 0.0
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if len(daily) >= 21 {
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win := daily
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if len(win) > 60 {
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win = win[len(win)-60:]
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}
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base := avgVol(win[:len(win)-1], 20)
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last := win[len(win)-1]
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lastVol = float64(last.Volume)
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if base > 0 {
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volMult = lastVol / base
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volDate = last.Date
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}
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}
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// Price momentum over the window: last close vs first close.
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priceChg := 0.0
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if len(daily) >= 2 {
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first, last := daily[0], daily[len(daily)-1]
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if first.Close > 0 {
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priceChg = float64(last.Close-first.Close) / float64(first.Close)
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}
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}
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if momentum == "flat" {
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switch {
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case priceChg > 0.05:
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momentum = "up"
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case priceChg < -0.05:
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momentum = "down"
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}
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}
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if momentum == "up" && priceChg > 0.15 {
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momentum = "strong"
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}
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// Relative volume vs market: ticker's latest volume against the
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// most-traded median for the same session.
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relVol := 0.0
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if med, mtDate, ok := mostTradedMedian(d.DB); ok {
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res.Citations = append(res.Citations, model.Cite("v2/most-traded/", "IDX", mtDate))
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if med > 0 && lastVol > 0 {
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relVol = lastVol / med
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res.Values = append(res.Values, model.Value{
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Label: "relative volume",
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Display: fmt.Sprintf("%.1fx most-traded median", relVol),
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Citations: res.Citations,
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})
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}
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}
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liquidity := "unknown"
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var ff []sectors.FreeFloatRow
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if ffDate, ok := payload(d.DB, "IDX", "free-float", &ff); ok {
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res.Citations = append(res.Citations, model.Cite("v2/free-float/", "IDX", ffDate))
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for _, r := range ff {
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if strings.HasPrefix(strings.ToUpper(r.Symbol), ticker) {
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switch {
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case r.FreeFloat >= 0.4:
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liquidity = "A"
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case r.FreeFloat >= 0.25:
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liquidity = "B"
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case r.FreeFloat >= 0.1:
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liquidity = "C"
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default:
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liquidity = "D"
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}
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res.Values = append(res.Values, model.Value{
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Label: "free float",
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Display: fmt.Sprintf("%.0f%% (grade %s)", r.FreeFloat*100, liquidity),
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Citations: res.Citations,
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})
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}
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}
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}
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if volMult > 2 {
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res.Flags = append(res.Flags, "volume-anomaly")
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}
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score := priceChg * 300
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if volMult > 1 {
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score += (volMult - 1) * 10
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}
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switch momentum {
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case "strong":
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score += 15
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case "up":
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score += 8
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case "down":
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score -= 8
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}
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res.Score = clampScore(score, -100, 100)
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res.Values = append([]model.Value{{
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Label: "momentum",
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Display: fmt.Sprintf("%s (%+.1f%% window)", momentum, priceChg*100),
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Citations: res.Citations,
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}}, res.Values...)
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if volMult > 0 {
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res.Values = append(res.Values, model.Value{
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Label: "volume anomaly",
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Display: fmt.Sprintf("%.1fx 20d avg on %s", volMult, volDate),
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Citations: res.Citations,
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})
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}
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if moverRank != "" {
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res.Values = append(res.Values, model.Value{Label: "mover rank", Display: moverRank, Citations: res.Citations})
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}
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res.Summary = fmt.Sprintf("%s momentum %+.1f%%, volume %.1fx, liquidity %s",
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momentum, priceChg*100, volMult, liquidity)
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res.Extra = map[string]any{
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"momentum": momentum, "price_change": priceChg,
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"volume_mult": volMult, "volume_date": volDate, "liquidity": liquidity,
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"rel_volume": relVol,
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}
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return res
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}
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// mtRow is one most-traded entry (volume in shares).
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type mtRow struct {
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Symbol string `json:"symbol"`
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Volume float64 `json:"volume"`
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}
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// mostTradedMedian returns the median volume across the cached most-traded
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// snapshot plus its snapshot date. Accepts both stored shapes: the wrapped
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// {results:[...]} form and the bare array the scheduler persists.
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func mostTradedMedian(db interface {
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LatestSnapshot(ticker, source string) (string, string, error)
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}) (med float64, date string, ok bool) {
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raw, d, err := db.LatestSnapshot("IDX", "most-traded")
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if err != nil || raw == "" {
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return 0, "", false
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}
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var vols []float64
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var wrapped struct {
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Results []mtRow `json:"results"`
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}
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if json.Unmarshal([]byte(raw), &wrapped) == nil && len(wrapped.Results) > 0 {
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for _, r := range wrapped.Results {
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if r.Volume > 0 {
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vols = append(vols, r.Volume)
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}
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}
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} else {
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var rows []mtRow
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if json.Unmarshal([]byte(raw), &rows) != nil {
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return 0, "", false
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}
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for _, r := range rows {
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if r.Volume > 0 {
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vols = append(vols, r.Volume)
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}
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}
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}
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if len(vols) == 0 {
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return 0, "", false
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}
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sort.Float64s(vols)
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m := vols[len(vols)/2]
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if len(vols)%2 == 0 {
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m = (vols[len(vols)/2-1] + vols[len(vols)/2]) / 2
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}
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return m, d, true
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}
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func avgVol(bars []sectors.DailyBar, n int) float64 {
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if len(bars) < n {
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n = len(bars)
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}
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if n == 0 {
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return 0
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}
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sum := 0.0
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for _, b := range bars[len(bars)-n:] {
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sum += float64(b.Volume)
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}
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return sum / float64(n)
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}
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// technicalFromStored derives momentum from stored snapshot volumes.
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func technicalFromStored(ticker string, vols []float64, dates []string) model.AgentResult {
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res := model.AgentResult{}
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last := vols[len(vols)-1]
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base := avg(vols[:len(vols)-1])
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mult := 0.0
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if base > 0 {
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mult = last / base
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}
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res.Score = clampScore((mult-1)*20, -100, 100)
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res.Citations = []model.Citation{model.Cite("v2/daily/"+ticker+"/", ticker, "stored")}
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date := ""
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if len(dates) > 0 {
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date = dates[len(dates)-1]
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}
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res.Values = []model.Value{{
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Label: "volume anomaly",
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Display: fmt.Sprintf("%.1fx 20d avg on %s", mult, date),
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Citations: res.Citations,
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}}
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if mult > 2 {
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res.Flags = append(res.Flags, "volume-anomaly")
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}
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res.Summary = fmt.Sprintf("stored-volume momentum %.1fx", mult)
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res.Extra = map[string]any{"volume_mult": mult, "volume_date": date}
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return res
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}
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