Implement EMOS scan terminal with REST-only market data
This commit is contained in:
2569718930@qq.com
@@ -0,0 +1,65 @@
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import { NextRequest, NextResponse } from "next/server";
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import {
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applyAuthResponseCookies,
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buildBackendRequestHeaders,
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} from "@/lib/backend-auth";
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const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
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export async function GET(req: NextRequest) {
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if (!API_BASE) {
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return NextResponse.json(
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{ error: "POLYWEATHER_API_BASE_URL is not configured" },
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{ status: 500 },
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);
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}
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const params = new URLSearchParams();
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for (const key of [
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"scan_mode",
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"min_price",
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"max_price",
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"min_edge_pct",
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"min_liquidity",
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"high_liquidity_only",
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"market_type",
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"time_range",
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"limit",
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"force_refresh",
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]) {
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const value = req.nextUrl.searchParams.get(key);
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if (value != null && value !== "") {
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params.set(key, value);
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}
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}
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const url = `${API_BASE}/api/scan/terminal?${params.toString()}`;
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try {
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const auth = await buildBackendRequestHeaders(req);
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const res = await fetch(url, {
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headers: auth.headers,
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cache: "no-store",
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});
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if (!res.ok) {
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const raw = await res.text();
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const response = NextResponse.json(
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{ error: `Backend returned ${res.status}`, detail: raw.slice(0, 300) },
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{ status: 502 },
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);
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return applyAuthResponseCookies(response, auth.response);
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}
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const data = await res.json();
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const response = NextResponse.json(data, {
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headers: {
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"Cache-Control": "no-store",
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},
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});
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return applyAuthResponseCookies(response, auth.response);
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} catch (error) {
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return NextResponse.json(
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{ error: "Failed to fetch scan terminal data", detail: String(error) },
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{ status: 500 },
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);
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}
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}
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@@ -3,10 +3,10 @@
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import dynamic from "next/dynamic";
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import { DashboardShellSkeleton } from "@/components/dashboard/DashboardShellSkeleton";
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const PolyWeatherDashboard = dynamic(
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const ScanTerminalDashboard = dynamic(
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() =>
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import("@/components/dashboard/PolyWeatherDashboard").then(
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(module) => module.PolyWeatherDashboard,
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import("@/components/dashboard/ScanTerminalDashboard").then(
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(module) => module.ScanTerminalDashboard,
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),
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{
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ssr: false,
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@@ -15,5 +15,5 @@ const PolyWeatherDashboard = dynamic(
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);
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export function DashboardEntry() {
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return <PolyWeatherDashboard />;
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return <ScanTerminalDashboard />;
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}
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@@ -1,160 +1,63 @@
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"use client";
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import React, { useMemo } from "react";
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import Image from "next/image";
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import React from "react";
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import { Star } from "lucide-react";
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import { useI18n } from "@/hooks/useI18n";
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import { useDashboardStore } from "@/hooks/useDashboardStore";
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import type {
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CityDetail,
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CityListItem,
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CitySummary,
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} from "@/lib/dashboard-types";
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import { getLocalizedCityDisplay } from "@/lib/dashboard-home-copy";
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import type { ScanOpportunityRow } from "@/lib/dashboard-types";
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import { getLocalizedCityName } from "@/lib/dashboard-home-copy";
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interface OpportunityRow {
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rank: number;
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city: CityListItem;
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summary?: CitySummary | null;
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detail?: CityDetail | null;
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score: number;
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tradable: boolean;
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}
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function getTodayHighLabel(detail?: CityDetail | null, symbol = "°C"): string {
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const current = detail?.current;
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if (current?.temp != null) return `${current.temp.toFixed(1)}${symbol}`;
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return "--";
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}
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function getTimeLabel(detail?: CityDetail | null): string {
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if (!detail?.local_time) return "--";
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return detail.local_time;
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}
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function getTimezone(detail?: CityDetail | null): string {
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if (detail?.utc_offset_seconds != null) {
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const hours = Math.round(detail.utc_offset_seconds / 3600);
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return `UTC${hours >= 0 ? "+" : ""}${hours}`;
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function getStatusMeta(
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row: ScanOpportunityRow,
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locale: string,
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): { label: string; tone: "green" | "amber" | "purple" | "neutral" } {
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const phase = String(row.window_phase || "").toLowerCase();
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if (phase === "active_peak" || phase === "setup_today") {
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return { label: locale === "en-US" ? "Tradable" : "可交易", tone: "green" };
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}
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return "";
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}
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function getMarketVolume(detail?: CityDetail | null): string {
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const vol =
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detail?.market_scan?.volume ?? detail?.market_scan?.primary_market?.volume;
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if (vol == null) return "--";
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if (vol >= 1_000_000) return `$${(vol / 1_000_000).toFixed(1)}M`;
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if (vol >= 1_000) return `$${Math.round(vol / 1_000)}K`;
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return `$${vol.toFixed(0)}`;
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}
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function getScanStatus(
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detail?: CityDetail | null,
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locale = "zh-CN",
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): { label: string; tone: string } {
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const scan = detail?.market_scan;
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if (!scan?.available)
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return {
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label: locale === "en-US" ? "No Market" : "无盘口",
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tone: "neutral",
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};
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const pa = scan?.price_analysis;
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const bestSide = pa?.best_side;
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const sideView = bestSide === "no" ? pa?.no : pa?.yes;
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const edgeVal =
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sideView?.edge != null
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? Math.abs(Number(sideView.edge)) > 1
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? Number(sideView.edge) / 100
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: Number(sideView.edge)
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: null;
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if (edgeVal != null && edgeVal >= 0.05)
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return {
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label: locale === "en-US" ? "Touch Play" : "触达博弈",
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tone: "amber",
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};
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if (edgeVal != null && edgeVal >= 0.02)
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return {
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label: locale === "en-US" ? "Tradable" : "即时确认",
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tone: "green",
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};
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if (edgeVal != null && edgeVal > 0)
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if (phase === "tomorrow" || phase === "week_ahead") {
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return { label: locale === "en-US" ? "Early" : "早期机会", tone: "purple" };
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return { label: locale === "en-US" ? "Market" : "市场", tone: "neutral" };
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}
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function getBestAction(detail?: CityDetail | null, locale = "zh-CN"): string {
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const scan = detail?.market_scan;
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if (!scan?.available) return "--";
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const pa = scan?.price_analysis;
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const side = pa?.best_side;
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const topBucket = scan?.top_buckets?.[0];
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const label = topBucket?.label || topBucket?.slug || "";
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if (side === "yes" && label)
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return `${locale === "en-US" ? "Buy" : "买入"} Yes ${label}`;
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if (side === "no" && label)
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return `${locale === "en-US" ? "Buy" : "买入"} No ${label}`;
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return "--";
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}
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function getEdge(detail?: CityDetail | null): number | null {
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const pa = detail?.market_scan?.price_analysis;
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const bestSide = pa?.best_side;
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const sideView = bestSide === "no" ? pa?.no : pa?.yes;
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const edge = sideView?.edge;
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if (edge == null) return null;
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const val = Number(edge);
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return Math.abs(val) > 1 ? val / 100 : val;
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}
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function getProbabilityBuckets(
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detail?: CityDetail | null,
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): Array<{ label: string; probability: number }> {
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const view = detail?.probabilities?.distribution || [];
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if (!Array.isArray(view)) return [];
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return view.slice(0, 8).map((b) => ({
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label: String(b.label || b.value || ""),
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probability: Number(b.probability || 0),
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}));
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}
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function MiniProbabilityChart({
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buckets,
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}: {
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buckets: Array<{ label: string; probability: number }>;
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}) {
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if (!buckets.length) return <span className="scan-no-data">--</span>;
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const maxProb = Math.max(...buckets.map((b) => b.probability), 0.01);
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return (
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<div className="scan-mini-chart">
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{buckets.map((bucket, i) => (
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<div key={i} className="scan-mini-bar-col">
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<div
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className="scan-mini-bar"
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style={{
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height: `${Math.max((bucket.probability / maxProb) * 100, 4)}%`,
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}}
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title={`${bucket.label}: ${(bucket.probability * 100).toFixed(1)}%`}
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/>
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<span className="scan-mini-bar-label">
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{bucket.label.replace(/[°CF]/g, "")}
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</span>
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</div>
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))}
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</div>
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);
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}
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function getDisplayScore(rawScore: number): number {
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if (rawScore <= 0) return Math.max(0, 70 + rawScore / 1000);
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if (rawScore > 1000) {
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return Math.min(100, 80 + (rawScore - 1000) / 30);
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}
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return Math.min(80, Math.max(0, (rawScore / 1000) * 80));
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if (phase === "post_peak") {
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return { label: locale === "en-US" ? "Post Peak" : "峰后确认", tone: "amber" };
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}
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return { label: locale === "en-US" ? "Watching" : "观察中", tone: "neutral" };
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}
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function ScoreRing({ score }: { score: number }) {
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const displayScore = getDisplayScore(score);
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function formatPercent(value?: number | null, signed = false) {
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if (value == null || Number.isNaN(Number(value))) return "--";
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const numeric = Number(value);
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if (!signed) return `${numeric.toFixed(1)}%`;
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return `${numeric >= 0 ? "+" : ""}${numeric.toFixed(1)}%`;
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}
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function formatTimeBlock(row: ScanOpportunityRow, locale: string) {
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const parts: string[] = [];
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if (row.local_time) {
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parts.push(row.local_time);
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}
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if (row.selected_date) {
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parts.push(row.selected_date);
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}
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return parts.join(" · ") || (locale === "en-US" ? "No time" : "暂无时间");
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}
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function formatAction(row: ScanOpportunityRow, locale: string) {
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if (row.side === "yes") {
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return `${locale === "en-US" ? "Buy" : "买入"} Yes ${row.target_label || ""}`.trim();
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}
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if (row.side === "no") {
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return `${locale === "en-US" ? "Buy" : "买入"} No ${row.target_label || ""}`.trim();
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}
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return row.action || "--";
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}
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function formatProbability(value?: number | null) {
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if (value == null || Number.isNaN(Number(value))) return "--";
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return `${(Number(value) * 100).toFixed(0)}%`;
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}
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function ScoreRing({ score }: { score?: number | null }) {
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const displayScore = Math.max(0, Math.min(100, Number(score || 0)));
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const radius = 20;
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const circumference = 2 * Math.PI * radius;
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const progress = (displayScore / 100) * circumference;
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@@ -191,27 +94,33 @@ function ScoreRing({ score }: { score: number }) {
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);
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}
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export function OpportunityTable({ rows }: { rows: OpportunityRow[] }) {
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export function OpportunityTable({
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rows,
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selectedRowId,
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onSelectRow,
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}: {
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rows: ScanOpportunityRow[];
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selectedRowId?: string | null;
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onSelectRow?: (row: ScanOpportunityRow) => void;
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}) {
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const { locale } = useI18n();
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const store = useDashboardStore();
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const isEn = locale === "en-US";
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return (
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<div className="scan-table-container">
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{/* Header */}
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<div className="scan-table-header">
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<span className="scan-th scan-th-rank">#</span>
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<span className="scan-th scan-th-city">
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{isEn ? "City / Market" : "城市 / 市场"}
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</span>
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<span className="scan-th scan-th-time">
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{isEn ? "Local Time / Status" : "当前时间 / 阶段"}
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{isEn ? "Time / Phase" : "时间 / 阶段"}
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</span>
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<span className="scan-th scan-th-prob">
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{isEn ? "Prob. Dist. vs Market" : "模型分布 vs 市场分布"}
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{isEn ? "EMOS vs Market" : "EMOS vs 市场"}
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</span>
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<span className="scan-th scan-th-action">
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{isEn ? "Best Action" : "最佳机会"}
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{isEn ? "Best Action" : "最佳动作"}
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</span>
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<span className="scan-th scan-th-edge">
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{isEn ? "Edge" : "边际优势"}
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@@ -222,90 +131,99 @@ export function OpportunityTable({ rows }: { rows: OpportunityRow[] }) {
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<span className="scan-th scan-th-fav" />
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</div>
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{/* Rows */}
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{rows.map((row) => {
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const cityName = getLocalizedCityDisplay(
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{rows.map((row, index) => {
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const status = getStatusMeta(row, locale);
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const localizedCityName = getLocalizedCityName(
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row.city,
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row.city_display_name || row.display_name || row.city,
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locale,
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row.summary,
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row.detail,
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);
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const status = getScanStatus(row.detail, locale);
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const edge = getEdge(row.detail);
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const buckets = getProbabilityBuckets(row.detail);
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const isSelected = store.selectedCity === row.city.name;
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const symbol = row.detail?.temp_symbol || "°C";
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const selected = selectedRowId === row.id;
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const finalScore = Number(row.final_score || 0);
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return (
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<div
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key={row.city.name}
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className={`scan-table-row ${isSelected ? "selected" : ""} ${row.tradable ? "tradable" : ""}`}
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onClick={() => store.focusCity(row.city.name)}
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<button
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key={row.id}
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type="button"
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className={`scan-table-row ${selected ? "selected" : ""} ${row.tradable ? "tradable" : ""}`}
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onClick={() => onSelectRow?.(row)}
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>
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{/* Rank */}
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<span className={`scan-td scan-td-rank rank-${status.tone}`}>
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<span className="scan-rank-circle">{row.rank}</span>
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<span className="scan-rank-circle">{row.rank || index + 1}</span>
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</span>
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{/* City */}
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<span className="scan-td scan-td-city">
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<span className="scan-city-thumb">
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<span className="scan-city-img-placeholder" />
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</span>
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<span className="scan-city-info">
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<span className="scan-city-name">{cityName}</span>
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<span className="scan-city-name">{localizedCityName}</span>
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<span className="scan-city-sub">
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{isEn ? "Today's high" : "今日最高温"} ·{" "}
|
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{getMarketVolume(row.detail)}
|
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{row.target_label || row.market_question || "--"}
|
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</span>
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</span>
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</span>
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{/* Time + Status */}
|
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<span className="scan-td scan-td-time">
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<span className="scan-time-text">
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{getTimeLabel(row.detail)} ({getTimezone(row.detail)})
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</span>
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<span className="scan-time-text">{formatTimeBlock(row, locale)}</span>
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<span className={`scan-status-badge tone-${status.tone}`}>
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{status.label}
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</span>
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</span>
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{/* Probability Chart */}
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<span className="scan-td scan-td-prob">
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<MiniProbabilityChart buckets={buckets} />
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<div className="scan-city-info">
|
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<span className="scan-city-name">
|
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{formatProbability(row.model_event_probability)} /{" "}
|
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{formatProbability(row.market_event_probability)}
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</span>
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<span className="scan-city-sub">
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{isEn ? "Bias" : "偏移"}{" "}
|
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{row.distribution_bias_direction || "--"} ·{" "}
|
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{formatPercent(row.distribution_bias_score)}
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</span>
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</div>
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</span>
|
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|
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{/* Best Action */}
|
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<span className="scan-td scan-td-action">
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<span className="scan-action-text">
|
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{getBestAction(row.detail, locale)}
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{formatAction(row, locale)}
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</span>
|
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</span>
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|
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{/* Edge */}
|
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<span className="scan-td scan-td-edge">
|
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<span
|
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className={`scan-edge-value ${edge != null && edge > 0 ? "positive" : "neutral"}`}
|
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className={`scan-edge-value ${finalScore > 0 ? "positive" : "neutral"}`}
|
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>
|
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{edge != null
|
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? `${edge > 0 ? "+" : ""}${(edge * 100).toFixed(1)}%`
|
||||
: "--"}
|
||||
{formatPercent(row.edge_percent, true)}
|
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</span>
|
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</span>
|
||||
|
||||
{/* Score */}
|
||||
<span className="scan-td scan-td-score">
|
||||
<ScoreRing score={row.score} />
|
||||
<ScoreRing score={row.final_score} />
|
||||
</span>
|
||||
|
||||
{/* Favorite */}
|
||||
<span className="scan-td scan-td-fav">
|
||||
<Star size={16} className="scan-fav-icon" />
|
||||
</span>
|
||||
</div>
|
||||
</button>
|
||||
);
|
||||
})}
|
||||
|
||||
{!rows.length ? (
|
||||
<div className="scan-detail-empty">
|
||||
<div>
|
||||
<div className="scan-detail-section-title">
|
||||
{isEn ? "No primary signal" : "当前无主信号"}
|
||||
</div>
|
||||
<p className="scan-city-sub">
|
||||
{isEn
|
||||
? "No opportunity passed the price, spread, liquidity, and edge filters."
|
||||
: "当前没有机会同时满足价格、点差、流动性和 edge 过滤。"}
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
) : null}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,6 +1,6 @@
|
||||
"use client";
|
||||
|
||||
import React, { useState } from "react";
|
||||
import React from "react";
|
||||
import {
|
||||
Crosshair,
|
||||
Clock,
|
||||
@@ -10,16 +10,9 @@ import {
|
||||
Search,
|
||||
} from "lucide-react";
|
||||
import { useI18n } from "@/hooks/useI18n";
|
||||
import styles from "./Dashboard.module.css";
|
||||
import type { ScanTerminalFilters } from "@/lib/dashboard-types";
|
||||
|
||||
interface FilterState {
|
||||
mode: "tradable" | "early" | "touch" | "trend";
|
||||
priceRange: [number, number];
|
||||
minEdge: number;
|
||||
highLiquidityOnly: boolean;
|
||||
marketType: "maxtemp" | "all";
|
||||
timeRange: "today" | "tomorrow" | "week";
|
||||
}
|
||||
export interface FilterState extends ScanTerminalFilters {}
|
||||
|
||||
const SCAN_MODES = [
|
||||
{
|
||||
@@ -57,29 +50,27 @@ const SCAN_MODES = [
|
||||
] as const;
|
||||
|
||||
export function ScanFilterPanel({
|
||||
value,
|
||||
onChange,
|
||||
onScan,
|
||||
isScanning,
|
||||
}: {
|
||||
value: FilterState;
|
||||
onChange?: (filters: FilterState) => void;
|
||||
onScan?: (filters: FilterState) => void;
|
||||
isScanning?: boolean;
|
||||
}) {
|
||||
const { locale } = useI18n();
|
||||
const isEn = locale === "en-US";
|
||||
|
||||
const [filters, setFilters] = useState<FilterState>({
|
||||
mode: "tradable",
|
||||
priceRange: [0.05, 0.95],
|
||||
minEdge: 2,
|
||||
highLiquidityOnly: false,
|
||||
marketType: "maxtemp",
|
||||
timeRange: "today",
|
||||
});
|
||||
|
||||
const updateFilter = <K extends keyof FilterState>(
|
||||
key: K,
|
||||
value: FilterState[K],
|
||||
nextValue: FilterState[K],
|
||||
) => {
|
||||
setFilters((prev) => ({ ...prev, [key]: value }));
|
||||
onChange?.({
|
||||
...value,
|
||||
[key]: nextValue,
|
||||
});
|
||||
};
|
||||
|
||||
return (
|
||||
@@ -92,12 +83,12 @@ export function ScanFilterPanel({
|
||||
<div className="scan-mode-tabs">
|
||||
{SCAN_MODES.map((mode) => {
|
||||
const Icon = mode.icon;
|
||||
const isActive = filters.mode === mode.key;
|
||||
const isActive = value.scan_mode === mode.key;
|
||||
return (
|
||||
<button
|
||||
key={mode.key}
|
||||
className={`scan-mode-tab ${isActive ? "active" : ""}`}
|
||||
onClick={() => updateFilter("mode", mode.key)}
|
||||
onClick={() => updateFilter("scan_mode", mode.key)}
|
||||
title={isEn ? mode.descEn : mode.descZh}
|
||||
>
|
||||
<Icon size={16} />
|
||||
@@ -124,17 +115,17 @@ export function ScanFilterPanel({
|
||||
{isEn ? "Price Range" : "价格范围"}
|
||||
</span>
|
||||
<div className="scan-range-display">
|
||||
<span>{filters.priceRange[0].toFixed(2)}</span>
|
||||
<span>{value.min_price.toFixed(2)}</span>
|
||||
<input
|
||||
type="range"
|
||||
min={0}
|
||||
max={100}
|
||||
value={filters.priceRange[0] * 100}
|
||||
value={value.min_price * 100}
|
||||
onChange={(e) =>
|
||||
updateFilter("priceRange", [
|
||||
Number(e.target.value) / 100,
|
||||
filters.priceRange[1],
|
||||
])
|
||||
updateFilter(
|
||||
"min_price",
|
||||
Math.min(Number(e.target.value) / 100, value.max_price),
|
||||
)
|
||||
}
|
||||
className="scan-range-slider"
|
||||
/>
|
||||
@@ -142,16 +133,16 @@ export function ScanFilterPanel({
|
||||
type="range"
|
||||
min={0}
|
||||
max={100}
|
||||
value={filters.priceRange[1] * 100}
|
||||
value={value.max_price * 100}
|
||||
onChange={(e) =>
|
||||
updateFilter("priceRange", [
|
||||
filters.priceRange[0],
|
||||
Number(e.target.value) / 100,
|
||||
])
|
||||
updateFilter(
|
||||
"max_price",
|
||||
Math.max(Number(e.target.value) / 100, value.min_price),
|
||||
)
|
||||
}
|
||||
className="scan-range-slider"
|
||||
/>
|
||||
<span>{filters.priceRange[1].toFixed(2)}</span>
|
||||
<span>{value.max_price.toFixed(2)}</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -161,13 +152,15 @@ export function ScanFilterPanel({
|
||||
{isEn ? "Min Edge" : "最小边际优势"}
|
||||
</span>
|
||||
<div className="scan-range-display">
|
||||
<span>{filters.minEdge}%</span>
|
||||
<span>{value.min_edge_pct}%</span>
|
||||
<input
|
||||
type="range"
|
||||
min={0}
|
||||
max={20}
|
||||
value={filters.minEdge}
|
||||
onChange={(e) => updateFilter("minEdge", Number(e.target.value))}
|
||||
value={value.min_edge_pct}
|
||||
onChange={(e) =>
|
||||
updateFilter("min_edge_pct", Number(e.target.value))
|
||||
}
|
||||
className="scan-range-slider"
|
||||
/>
|
||||
</div>
|
||||
@@ -179,10 +172,17 @@ export function ScanFilterPanel({
|
||||
{isEn ? "High Liquidity Only" : "只看高流动性"}
|
||||
</span>
|
||||
<button
|
||||
className={`scan-toggle ${filters.highLiquidityOnly ? "active" : ""}`}
|
||||
onClick={() =>
|
||||
updateFilter("highLiquidityOnly", !filters.highLiquidityOnly)
|
||||
}
|
||||
className={`scan-toggle ${value.high_liquidity_only ? "active" : ""}`}
|
||||
onClick={() => {
|
||||
const nextValue = !value.high_liquidity_only;
|
||||
onChange?.({
|
||||
...value,
|
||||
high_liquidity_only: nextValue,
|
||||
min_liquidity: nextValue
|
||||
? Math.max(value.min_liquidity, 5000)
|
||||
: 500,
|
||||
});
|
||||
}}
|
||||
>
|
||||
<span className="scan-toggle-knob" />
|
||||
</button>
|
||||
@@ -195,11 +195,11 @@ export function ScanFilterPanel({
|
||||
</span>
|
||||
<select
|
||||
className="scan-select"
|
||||
value={filters.marketType}
|
||||
value={value.market_type}
|
||||
onChange={(e) =>
|
||||
updateFilter(
|
||||
"marketType",
|
||||
e.target.value as FilterState["marketType"],
|
||||
"market_type",
|
||||
e.target.value as FilterState["market_type"],
|
||||
)
|
||||
}
|
||||
>
|
||||
@@ -217,11 +217,11 @@ export function ScanFilterPanel({
|
||||
</span>
|
||||
<select
|
||||
className="scan-select"
|
||||
value={filters.timeRange}
|
||||
value={value.time_range}
|
||||
onChange={(e) =>
|
||||
updateFilter(
|
||||
"timeRange",
|
||||
e.target.value as FilterState["timeRange"],
|
||||
"time_range",
|
||||
e.target.value as FilterState["time_range"],
|
||||
)
|
||||
}
|
||||
>
|
||||
@@ -235,7 +235,7 @@ export function ScanFilterPanel({
|
||||
{/* === CTA === */}
|
||||
<button
|
||||
className="scan-cta-button"
|
||||
onClick={() => onScan?.(filters)}
|
||||
onClick={() => onScan?.(value)}
|
||||
disabled={isScanning}
|
||||
>
|
||||
<Search size={16} />
|
||||
|
||||
@@ -3,17 +3,9 @@
|
||||
import React from "react";
|
||||
import { TrendingUp, BarChart3, Radio, DollarSign, Target } from "lucide-react";
|
||||
import { useI18n } from "@/hooks/useI18n";
|
||||
import type { ScanTerminalResponse } from "@/lib/dashboard-types";
|
||||
|
||||
interface KPIData {
|
||||
recommendedCount: number;
|
||||
recommendedDelta?: number;
|
||||
avgEdge: number | null;
|
||||
avgEdgeDelta?: number | null;
|
||||
totalWinRate: number | null;
|
||||
tradableMarkets: number;
|
||||
filteredTotal: number;
|
||||
totalVolume: number;
|
||||
}
|
||||
type KPIData = ScanTerminalResponse["summary"];
|
||||
|
||||
function formatVolume(value: number): string {
|
||||
if (value >= 1_000_000) return `$${(value / 1_000_000).toFixed(1)}M`;
|
||||
@@ -29,42 +21,41 @@ export function ScanKPIBar({ data }: { data: KPIData }) {
|
||||
{
|
||||
icon: Target,
|
||||
label: isEn ? "Recommended" : "推荐机会",
|
||||
value: String(data.recommendedCount),
|
||||
delta:
|
||||
data.recommendedDelta != null
|
||||
? `${isEn ? "vs last" : "较上次"} ${data.recommendedDelta > 0 ? "+" : ""}${data.recommendedDelta}`
|
||||
: null,
|
||||
value: String(data.recommended_count),
|
||||
delta: `${isEn ? "Showing" : "当前展示"} ${data.visible_count}`,
|
||||
accent: "green" as const,
|
||||
},
|
||||
{
|
||||
icon: TrendingUp,
|
||||
label: isEn ? "Avg Edge" : "平均边际优势",
|
||||
value: data.avgEdge != null ? `+${data.avgEdge.toFixed(1)}%` : "--",
|
||||
delta:
|
||||
data.avgEdgeDelta != null
|
||||
? `${isEn ? "vs last" : "较上次"} ${data.avgEdgeDelta > 0 ? "+" : ""}${data.avgEdgeDelta.toFixed(1)}%`
|
||||
: null,
|
||||
value:
|
||||
data.avg_edge_percent != null
|
||||
? `+${data.avg_edge_percent.toFixed(1)}%`
|
||||
: "--",
|
||||
delta: `${isEn ? "Candidates" : "候选总数"} ${data.candidate_total}`,
|
||||
accent: "green" as const,
|
||||
},
|
||||
{
|
||||
icon: BarChart3,
|
||||
label: isEn ? "Total Win Rate" : "总胜率预测",
|
||||
label: isEn ? "Avg Confidence" : "平均主信号置信度",
|
||||
value:
|
||||
data.totalWinRate != null ? `+${data.totalWinRate.toFixed(1)}%` : "--",
|
||||
delta: isEn ? "Model confidence" : "模型全分配",
|
||||
data.avg_primary_confidence != null
|
||||
? `${data.avg_primary_confidence.toFixed(0)}`
|
||||
: "--",
|
||||
delta: isEn ? "Main signal score" : "主信号评分",
|
||||
accent: "green" as const,
|
||||
},
|
||||
{
|
||||
icon: Radio,
|
||||
label: isEn ? "Tradable Markets" : "可交易市场",
|
||||
value: String(data.tradableMarkets),
|
||||
delta: `${isEn ? "Filtered" : "过滤后"} / ${data.filteredTotal}`,
|
||||
value: String(data.tradable_market_count),
|
||||
delta: data.resolved_market_type || "maxtemp",
|
||||
accent: "purple" as const,
|
||||
},
|
||||
{
|
||||
icon: DollarSign,
|
||||
label: isEn ? "Total Volume" : "总成交量",
|
||||
value: formatVolume(data.totalVolume),
|
||||
value: formatVolume(data.total_volume),
|
||||
delta: isEn ? "Past 24h" : "过去 24 小时",
|
||||
accent: "purple" as const,
|
||||
},
|
||||
|
||||
@@ -0,0 +1,515 @@
|
||||
"use client";
|
||||
|
||||
import clsx from "clsx";
|
||||
import {
|
||||
startTransition,
|
||||
useDeferredValue,
|
||||
useEffect,
|
||||
useEffectEvent,
|
||||
useMemo,
|
||||
useState,
|
||||
} from "react";
|
||||
import styles from "./Dashboard.module.css";
|
||||
import detailChromeStyles from "./DetailPanelChrome.module.css";
|
||||
import modalChromeStyles from "./ModalChrome.module.css";
|
||||
import { HeaderBar } from "@/components/dashboard/HeaderBar";
|
||||
import {
|
||||
FilterState,
|
||||
ScanFilterPanel,
|
||||
} from "@/components/dashboard/ScanFilterPanel";
|
||||
import { ScanKPIBar } from "@/components/dashboard/ScanKPIBar";
|
||||
import { OpportunityTable } from "@/components/dashboard/OpportunityTable";
|
||||
import { DashboardStoreProvider, useDashboardStore } from "@/hooks/useDashboardStore";
|
||||
import { I18nProvider, useI18n } from "@/hooks/useI18n";
|
||||
import { dashboardClient } from "@/lib/dashboard-client";
|
||||
import type {
|
||||
MarketScan,
|
||||
PrimarySignal,
|
||||
ScanOpportunityRow,
|
||||
ScanTerminalResponse,
|
||||
} from "@/lib/dashboard-types";
|
||||
import {
|
||||
getLocalizedAirportName,
|
||||
getLocalizedCityName,
|
||||
} from "@/lib/dashboard-home-copy";
|
||||
|
||||
const DEFAULT_FILTERS: FilterState = {
|
||||
scan_mode: "tradable",
|
||||
min_price: 0.05,
|
||||
max_price: 0.95,
|
||||
min_edge_pct: 2,
|
||||
min_liquidity: 500,
|
||||
high_liquidity_only: false,
|
||||
market_type: "maxtemp",
|
||||
time_range: "today",
|
||||
limit: 25,
|
||||
};
|
||||
|
||||
function formatPercent(value?: number | null, signed = false) {
|
||||
if (value == null || Number.isNaN(Number(value))) return "--";
|
||||
const numeric = Number(value);
|
||||
if (!signed) return `${numeric.toFixed(1)}%`;
|
||||
return `${numeric >= 0 ? "+" : ""}${numeric.toFixed(1)}%`;
|
||||
}
|
||||
|
||||
function formatProbability(value?: number | null) {
|
||||
if (value == null || Number.isNaN(Number(value))) return "--";
|
||||
return `${(Number(value) * 100).toFixed(0)}%`;
|
||||
}
|
||||
|
||||
function formatPrice(value?: number | null) {
|
||||
if (value == null || Number.isNaN(Number(value))) return "--";
|
||||
return `${Math.round(Number(value) * 100)}¢`;
|
||||
}
|
||||
|
||||
function formatVolume(value?: number | null) {
|
||||
const numeric = Number(value || 0);
|
||||
if (!Number.isFinite(numeric) || numeric <= 0) return "--";
|
||||
if (numeric >= 1_000_000) return `$${(numeric / 1_000_000).toFixed(1)}M`;
|
||||
if (numeric >= 1_000) return `$${(numeric / 1_000).toFixed(0)}K`;
|
||||
return `$${numeric.toFixed(0)}`;
|
||||
}
|
||||
|
||||
function formatMinutes(value?: number | null, locale = "zh-CN") {
|
||||
if (value == null || !Number.isFinite(Number(value))) return "--";
|
||||
const numeric = Math.max(0, Math.round(Number(value)));
|
||||
if (locale === "en-US") return `${numeric}m`;
|
||||
return `${numeric} 分钟`;
|
||||
}
|
||||
|
||||
function scoreTone(score?: number | null) {
|
||||
const numeric = Number(score || 0);
|
||||
if (numeric >= 85) return "green";
|
||||
if (numeric >= 70) return "amber";
|
||||
return "red";
|
||||
}
|
||||
|
||||
function confidenceDotCount(score?: number | null) {
|
||||
const numeric = Number(score || 0);
|
||||
if (numeric >= 90) return 5;
|
||||
if (numeric >= 80) return 4;
|
||||
if (numeric >= 70) return 3;
|
||||
if (numeric >= 60) return 2;
|
||||
if (numeric > 0) return 1;
|
||||
return 0;
|
||||
}
|
||||
|
||||
function getSideRow(
|
||||
marketScan: MarketScan | null | undefined,
|
||||
selectedRow: ScanOpportunityRow,
|
||||
side: "yes" | "no",
|
||||
) {
|
||||
const rows = Array.isArray(marketScan?.scan_rows) ? marketScan?.scan_rows : [];
|
||||
const matched = rows.find(
|
||||
(row) =>
|
||||
row.market_slug === selectedRow.market_slug &&
|
||||
row.selected_date === selectedRow.selected_date &&
|
||||
row.side === side,
|
||||
);
|
||||
if (matched) return matched;
|
||||
if (selectedRow.side === side) return selectedRow;
|
||||
return null;
|
||||
}
|
||||
|
||||
function DetailPanel({
|
||||
row,
|
||||
marketScan,
|
||||
loading,
|
||||
}: {
|
||||
row: ScanOpportunityRow | null;
|
||||
marketScan?: MarketScan | null;
|
||||
loading?: boolean;
|
||||
}) {
|
||||
const { locale } = useI18n();
|
||||
const isEn = locale === "en-US";
|
||||
|
||||
if (!row) {
|
||||
return (
|
||||
<aside className="scan-detail-panel">
|
||||
<div className="scan-detail-empty">
|
||||
{isEn ? "Select a row to inspect the main signal." : "选择一条机会，查看主信号详情。"}
|
||||
</div>
|
||||
</aside>
|
||||
);
|
||||
}
|
||||
|
||||
const localizedCityName = getLocalizedCityName(
|
||||
row.city,
|
||||
row.city_display_name || row.display_name || row.city,
|
||||
locale,
|
||||
);
|
||||
const localizedAirport = getLocalizedAirportName(
|
||||
row.city,
|
||||
row.airport || "",
|
||||
locale,
|
||||
);
|
||||
const detailSignal = marketScan?.primary_signal as PrimarySignal | null | undefined;
|
||||
const displayRow = detailSignal && detailSignal.market_slug === row.market_slug ? detailSignal : row;
|
||||
const distributionBias = marketScan?.distribution_bias || row.distribution_bias || null;
|
||||
const yesRow = getSideRow(marketScan, row, "yes");
|
||||
const noRow = getSideRow(marketScan, row, "no");
|
||||
const tone = scoreTone(displayRow.final_score);
|
||||
const filledDots = confidenceDotCount(displayRow.final_score);
|
||||
|
||||
return (
|
||||
<aside className="scan-detail-panel">
|
||||
<div className="scan-detail-header">
|
||||
<div className="scan-detail-hero-placeholder" />
|
||||
<div className="scan-detail-city-info">
|
||||
<div className="scan-detail-city-name">{localizedCityName}</div>
|
||||
<div className="scan-detail-city-sub">
|
||||
{displayRow.market_question || displayRow.target_label || "--"}
|
||||
</div>
|
||||
<div className="scan-detail-volume">
|
||||
{formatVolume(displayRow.volume)}{" "}
|
||||
{isEn ? "24h volume" : "24h 成交量"}
|
||||
{loading ? ` · ${isEn ? "loading" : "载入中"}` : ""}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="scan-detail-section">
|
||||
<div className="scan-detail-section-title">
|
||||
{isEn ? "Current Context" : "当前概况"}
|
||||
</div>
|
||||
<div className="scan-conditions-table">
|
||||
<div className="scan-condition-item">
|
||||
<span className="scan-condition-label">
|
||||
{isEn ? "Local Time" : "当地时间"}
|
||||
</span>
|
||||
<span className="scan-condition-value">{row.local_time || "--"}</span>
|
||||
</div>
|
||||
<div className="scan-condition-item">
|
||||
<span className="scan-condition-label">
|
||||
{isEn ? "Current Temp" : "当前温度"}
|
||||
</span>
|
||||
<span className="scan-condition-value">
|
||||
{row.current_temp != null ? `${row.current_temp}${row.temp_symbol || ""}` : "--"}
|
||||
</span>
|
||||
</div>
|
||||
<div className="scan-condition-item">
|
||||
<span className="scan-condition-label">
|
||||
{isEn ? "Day High (So Far)" : "今日高点（至今）"}
|
||||
</span>
|
||||
<span className="scan-condition-value">
|
||||
{row.current_max_so_far != null
|
||||
? `${row.current_max_so_far}${row.temp_symbol || ""}`
|
||||
: "--"}
|
||||
</span>
|
||||
</div>
|
||||
<div className="scan-condition-item">
|
||||
<span className="scan-condition-label">
|
||||
{isEn ? "Target" : "目标温度"}
|
||||
</span>
|
||||
<span className="scan-condition-value">
|
||||
{displayRow.target_label || "--"}
|
||||
</span>
|
||||
</div>
|
||||
<div className="scan-condition-item">
|
||||
<span className="scan-condition-label">
|
||||
{isEn ? "Gap To Target" : "距目标"}
|
||||
</span>
|
||||
<span
|
||||
className={clsx(
|
||||
"scan-condition-value",
|
||||
Number(displayRow.gap_to_target || 0) <= 0
|
||||
? "accent-green"
|
||||
: "accent-red",
|
||||
)}
|
||||
>
|
||||
{displayRow.gap_to_target != null
|
||||
? `${displayRow.gap_to_target >= 0 ? "+" : ""}${displayRow.gap_to_target.toFixed(1)}${row.temp_symbol || ""}`
|
||||
: "--"}
|
||||
</span>
|
||||
</div>
|
||||
<div className="scan-condition-item">
|
||||
<span className="scan-condition-label">
|
||||
{isEn ? "Window Left" : "剩余有效时间"}
|
||||
</span>
|
||||
<span className="scan-condition-value">
|
||||
{formatMinutes(displayRow.remaining_window_minutes, locale)}
|
||||
</span>
|
||||
</div>
|
||||
<div className="scan-condition-item">
|
||||
<span className="scan-condition-label">
|
||||
{isEn ? "Bias" : "分布偏移"}
|
||||
</span>
|
||||
<span className="scan-condition-value">
|
||||
{distributionBias?.direction || "--"} ·{" "}
|
||||
{distributionBias?.score != null
|
||||
? distributionBias.score.toFixed(0)
|
||||
: "--"}
|
||||
</span>
|
||||
</div>
|
||||
<div className="scan-condition-item">
|
||||
<span className="scan-condition-label">
|
||||
{isEn ? "Airport" : "机场锚点"}
|
||||
</span>
|
||||
<span className="scan-condition-value">{localizedAirport || "--"}</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="scan-detail-section">
|
||||
<div className="scan-detail-section-title">
|
||||
{isEn ? "Recommended Trade" : "推荐交易"}
|
||||
</div>
|
||||
<div className="scan-trade-cards">
|
||||
<div className="scan-trade-card yes">
|
||||
<div className="scan-trade-card-title">BUY YES</div>
|
||||
<div className="scan-trade-card-price">
|
||||
{formatPrice(yesRow?.ask ?? marketScan?.yes_buy)} / {formatProbability(yesRow?.model_probability)}
|
||||
</div>
|
||||
<div
|
||||
className={clsx(
|
||||
"scan-trade-card-edge",
|
||||
Number(yesRow?.edge_percent || 0) >= 0 ? "positive" : "negative",
|
||||
)}
|
||||
>
|
||||
{formatPercent(yesRow?.edge_percent, true)}
|
||||
</div>
|
||||
<div className="scan-trade-card-note">
|
||||
{isEn ? "Spread" : "点差"} {formatPrice(yesRow?.spread)} ·{" "}
|
||||
{isEn ? "Liquidity" : "流动性"} {formatVolume(yesRow?.book_liquidity ?? yesRow?.market_liquidity)}
|
||||
</div>
|
||||
</div>
|
||||
<div className="scan-trade-card no">
|
||||
<div className="scan-trade-card-title">BUY NO</div>
|
||||
<div className="scan-trade-card-price">
|
||||
{formatPrice(noRow?.ask ?? marketScan?.no_buy)} / {formatProbability(noRow?.model_probability)}
|
||||
</div>
|
||||
<div
|
||||
className={clsx(
|
||||
"scan-trade-card-edge",
|
||||
Number(noRow?.edge_percent || 0) >= 0 ? "positive" : "negative",
|
||||
)}
|
||||
>
|
||||
{formatPercent(noRow?.edge_percent, true)}
|
||||
</div>
|
||||
<div className="scan-trade-card-note">
|
||||
{isEn ? "Spread" : "点差"} {formatPrice(noRow?.spread)} ·{" "}
|
||||
{isEn ? "Liquidity" : "流动性"} {formatVolume(noRow?.book_liquidity ?? noRow?.market_liquidity)}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="scan-detail-score">
|
||||
<div>
|
||||
<div className="scan-detail-score-big">
|
||||
{Number(displayRow.final_score || 0).toFixed(0)}
|
||||
<span className="scan-detail-score-suffix">/100</span>
|
||||
</div>
|
||||
<div className="scan-detail-score-label">
|
||||
{isEn ? "Composite signal score" : "综合主信号评分"}
|
||||
</div>
|
||||
</div>
|
||||
<div className="scan-confidence-dots">
|
||||
{Array.from({ length: 5 }).map((_, index) => {
|
||||
const filled = index < filledDots;
|
||||
return (
|
||||
<span
|
||||
key={index}
|
||||
className={clsx(
|
||||
"scan-confidence-dot",
|
||||
filled && "filled",
|
||||
filled && tone === "amber" && "amber",
|
||||
filled && tone === "red" && "red",
|
||||
)}
|
||||
/>
|
||||
);
|
||||
})}
|
||||
</div>
|
||||
</div>
|
||||
</aside>
|
||||
);
|
||||
}
|
||||
|
||||
function ScanTerminalScreen() {
|
||||
const store = useDashboardStore();
|
||||
const { locale } = useI18n();
|
||||
const isEn = locale === "en-US";
|
||||
const [draftFilters, setDraftFilters] = useState<FilterState>(DEFAULT_FILTERS);
|
||||
const [activeFilters, setActiveFilters] = useState<FilterState>(DEFAULT_FILTERS);
|
||||
const [terminalData, setTerminalData] = useState<ScanTerminalResponse | null>(null);
|
||||
const [loading, setLoading] = useState(false);
|
||||
const [error, setError] = useState<string | null>(null);
|
||||
const [selectedRowId, setSelectedRowId] = useState<string | null>(null);
|
||||
const [detailByRowId, setDetailByRowId] = useState<Record<string, MarketScan | null>>({});
|
||||
const [detailLoadingId, setDetailLoadingId] = useState<string | null>(null);
|
||||
|
||||
const deferredRows = useDeferredValue(terminalData?.rows || []);
|
||||
const selectedRow = useMemo(() => {
|
||||
if (!deferredRows.length) return null;
|
||||
return (
|
||||
deferredRows.find((row) => row.id === selectedRowId) ||
|
||||
deferredRows[0] ||
|
||||
null
|
||||
);
|
||||
}, [deferredRows, selectedRowId]);
|
||||
|
||||
const fetchTerminal = useEffectEvent(async (filters: FilterState, force = false) => {
|
||||
setLoading(true);
|
||||
setError(null);
|
||||
try {
|
||||
const response = await dashboardClient.getScanTerminal(filters, { force });
|
||||
startTransition(() => {
|
||||
setTerminalData(response);
|
||||
setActiveFilters(filters);
|
||||
setSelectedRowId((current) => {
|
||||
if (current && response.rows.some((row) => row.id === current)) {
|
||||
return current;
|
||||
}
|
||||
return response.top_signal?.id || response.rows[0]?.id || null;
|
||||
});
|
||||
});
|
||||
} catch (fetchError) {
|
||||
setError(String(fetchError));
|
||||
} finally {
|
||||
setLoading(false);
|
||||
}
|
||||
});
|
||||
|
||||
const fetchDetail = useEffectEvent(async (row: ScanOpportunityRow) => {
|
||||
if (!row.market_slug || !row.selected_date) return;
|
||||
if (detailByRowId[row.id] !== undefined) return;
|
||||
setDetailLoadingId(row.id);
|
||||
try {
|
||||
const response = await dashboardClient.getCityMarketScan(row.city, {
|
||||
force: false,
|
||||
marketSlug: row.market_slug,
|
||||
targetDate: row.selected_date,
|
||||
});
|
||||
setDetailByRowId((current) => ({
|
||||
...current,
|
||||
[row.id]: response.market_scan || null,
|
||||
}));
|
||||
} catch {
|
||||
setDetailByRowId((current) => ({
|
||||
...current,
|
||||
[row.id]: null,
|
||||
}));
|
||||
} finally {
|
||||
setDetailLoadingId((current) => (current === row.id ? null : current));
|
||||
}
|
||||
});
|
||||
|
||||
useEffect(() => {
|
||||
void fetchTerminal(DEFAULT_FILTERS, false);
|
||||
}, [fetchTerminal]);
|
||||
|
||||
useEffect(() => {
|
||||
const intervalId = window.setInterval(() => {
|
||||
void fetchTerminal(activeFilters, false);
|
||||
}, 30_000);
|
||||
return () => {
|
||||
window.clearInterval(intervalId);
|
||||
};
|
||||
}, [activeFilters, fetchTerminal]);
|
||||
|
||||
useEffect(() => {
|
||||
if (!selectedRow) return;
|
||||
void fetchDetail(selectedRow);
|
||||
}, [selectedRow, fetchDetail]);
|
||||
|
||||
const selectedDetail = selectedRow ? detailByRowId[selectedRow.id] : null;
|
||||
|
||||
return (
|
||||
<div
|
||||
className={clsx(
|
||||
styles.root,
|
||||
detailChromeStyles.root,
|
||||
modalChromeStyles.root,
|
||||
)}
|
||||
>
|
||||
<HeaderBar
|
||||
refreshAction={() => fetchTerminal(activeFilters, true)}
|
||||
refreshSpinning={loading || store.loadingState.refresh}
|
||||
/>
|
||||
|
||||
<div className="scan-terminal">
|
||||
<ScanFilterPanel
|
||||
value={draftFilters}
|
||||
onChange={setDraftFilters}
|
||||
onScan={(filters) => {
|
||||
setDraftFilters(filters);
|
||||
void fetchTerminal(filters, true);
|
||||
}}
|
||||
isScanning={loading}
|
||||
/>
|
||||
|
||||
<main className="scan-data-grid">
|
||||
<div className="scan-data-grid-header">
|
||||
<div>
|
||||
<div className="scan-data-grid-title">
|
||||
{isEn ? "Tradable Opportunities" : "可交易机会"}
|
||||
</div>
|
||||
<div className="scan-data-grid-subtitle">
|
||||
{isEn
|
||||
? "REST-only EMOS scan with one main signal per city/date."
|
||||
: "基于 EMOS 分布与 CLOB REST 盘口，只输出每个城市/日期的单一主信号。"}
|
||||
</div>
|
||||
</div>
|
||||
<div className="scan-data-grid-controls">
|
||||
<span className="scan-status-badge tone-neutral">
|
||||
{isEn ? "Updated" : "数据时间"} ·{" "}
|
||||
{terminalData?.generated_at
|
||||
? terminalData.generated_at.replace("T", " ").slice(0, 19)
|
||||
: "--"}
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<ScanKPIBar
|
||||
data={
|
||||
terminalData?.summary || {
|
||||
recommended_count: 0,
|
||||
visible_count: 0,
|
||||
candidate_total: 0,
|
||||
avg_edge_percent: null,
|
||||
avg_primary_confidence: null,
|
||||
tradable_market_count: 0,
|
||||
total_volume: 0,
|
||||
resolved_market_type: "maxtemp",
|
||||
}
|
||||
}
|
||||
/>
|
||||
|
||||
<div className="scan-view-tabs">
|
||||
<button type="button" className="scan-view-tab active">
|
||||
{isEn ? "Opportunity List" : "机会列表"}
|
||||
</button>
|
||||
</div>
|
||||
|
||||
{error ? (
|
||||
<div className="scan-detail-empty">
|
||||
{isEn ? "Scan failed." : "扫描失败。"} {error}
|
||||
</div>
|
||||
) : (
|
||||
<OpportunityTable
|
||||
rows={deferredRows}
|
||||
selectedRowId={selectedRowId}
|
||||
onSelectRow={(row) => setSelectedRowId(row.id)}
|
||||
/>
|
||||
)}
|
||||
</main>
|
||||
|
||||
<DetailPanel
|
||||
row={selectedRow}
|
||||
marketScan={selectedDetail}
|
||||
loading={detailLoadingId === selectedRow?.id}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
export function ScanTerminalDashboard() {
|
||||
return (
|
||||
<I18nProvider>
|
||||
<DashboardStoreProvider>
|
||||
<ScanTerminalScreen />
|
||||
</DashboardStoreProvider>
|
||||
</I18nProvider>
|
||||
);
|
||||
}
|
||||
@@ -6,6 +6,8 @@ import {
|
||||
CitySummary,
|
||||
HistoryPayload,
|
||||
MarketScan,
|
||||
ScanTerminalFilters,
|
||||
ScanTerminalResponse,
|
||||
} from "@/lib/dashboard-types";
|
||||
|
||||
export type AssistantOpportunityContext = {
|
||||
@@ -73,6 +75,7 @@ const pendingCityMarketScanRequests = new Map<
|
||||
}>
|
||||
>();
|
||||
const pendingAssistantRequests = new Map<string, Promise<AssistantChatResponse>>();
|
||||
const pendingScanTerminalRequests = new Map<string, Promise<ScanTerminalResponse>>();
|
||||
const PRIORITY_WARM_SESSION_KEY = "polyWeather_priority_warm_v1";
|
||||
|
||||
type CityCacheMeta = {
|
||||
@@ -350,6 +353,41 @@ export const dashboardClient = {
|
||||
return request;
|
||||
},
|
||||
|
||||
async getScanTerminal(
|
||||
filters: ScanTerminalFilters,
|
||||
options?: { force?: boolean },
|
||||
) {
|
||||
const force = options?.force ?? false;
|
||||
const params = new URLSearchParams({
|
||||
scan_mode: String(filters.scan_mode),
|
||||
min_price: String(filters.min_price),
|
||||
max_price: String(filters.max_price),
|
||||
min_edge_pct: String(filters.min_edge_pct),
|
||||
min_liquidity: String(filters.min_liquidity),
|
||||
high_liquidity_only: String(filters.high_liquidity_only),
|
||||
market_type: String(filters.market_type),
|
||||
time_range: String(filters.time_range),
|
||||
limit: String(filters.limit),
|
||||
force_refresh: String(force),
|
||||
});
|
||||
const requestKey = `${params.toString()}::${force ? "force" : "cached"}`;
|
||||
if (!force) {
|
||||
const existing = pendingScanTerminalRequests.get(requestKey);
|
||||
if (existing) {
|
||||
return existing;
|
||||
}
|
||||
}
|
||||
const request = fetchJson<ScanTerminalResponse>(
|
||||
`/api/scan/terminal?${params.toString()}`,
|
||||
).finally(() => {
|
||||
pendingScanTerminalRequests.delete(requestKey);
|
||||
});
|
||||
if (!force) {
|
||||
pendingScanTerminalRequests.set(requestKey, request);
|
||||
}
|
||||
return request;
|
||||
},
|
||||
|
||||
async getHistory(cityName: string, options?: { includeRecords?: boolean }) {
|
||||
const includeRecords = options?.includeRecords === true;
|
||||
const requestKey = `${normalizeCityName(cityName)}::${
|
||||
|
||||
@@ -399,6 +399,134 @@ export interface MarketScan {
|
||||
recent_trades?: unknown[];
|
||||
scan_scope?: "lite" | "full" | string | null;
|
||||
websocket?: Record<string, unknown>;
|
||||
distribution_bias?: DistributionBias | null;
|
||||
window_phase?: string | null;
|
||||
window_score?: number | null;
|
||||
primary_signal?: PrimarySignal | null;
|
||||
signal_status?: string | null;
|
||||
candidate_count?: number | null;
|
||||
scan_rows?: ScanOpportunityRow[] | null;
|
||||
resolved_market_type?: string | null;
|
||||
}
|
||||
|
||||
export interface DistributionBias {
|
||||
available?: boolean;
|
||||
value?: number | null;
|
||||
score?: number | null;
|
||||
direction?: "hotter" | "colder" | "balanced" | string | null;
|
||||
}
|
||||
|
||||
export interface ScanTerminalFilters {
|
||||
scan_mode: "tradable" | "early" | "touch" | "trend";
|
||||
min_price: number;
|
||||
max_price: number;
|
||||
min_edge_pct: number;
|
||||
min_liquidity: number;
|
||||
high_liquidity_only: boolean;
|
||||
market_type: "maxtemp" | "all" | string;
|
||||
time_range: "today" | "tomorrow" | "week" | string;
|
||||
limit: number;
|
||||
}
|
||||
|
||||
export interface ScanOpportunityRow {
|
||||
id: string;
|
||||
rank?: number | null;
|
||||
city: string;
|
||||
city_display_name?: string | null;
|
||||
display_name?: string | null;
|
||||
selected_date?: string | null;
|
||||
local_date?: string | null;
|
||||
local_time?: string | null;
|
||||
temp_symbol?: string | null;
|
||||
current_temp?: number | null;
|
||||
current_max_so_far?: number | null;
|
||||
deb_prediction?: number | null;
|
||||
airport?: string | null;
|
||||
risk_level?: RiskLevel | null;
|
||||
market_slug?: string | null;
|
||||
market_question?: string | null;
|
||||
market_url?: string | null;
|
||||
market_key?: string | null;
|
||||
side?: "yes" | "no" | string | null;
|
||||
action?: string | null;
|
||||
market_direction?: string | null;
|
||||
temperature_direction?: string | null;
|
||||
target_label?: string | null;
|
||||
target_value?: number | null;
|
||||
target_threshold?: number | null;
|
||||
target_lower?: number | null;
|
||||
target_upper?: number | null;
|
||||
target_unit?: string | null;
|
||||
model_probability?: number | null;
|
||||
market_probability?: number | null;
|
||||
model_event_probability?: number | null;
|
||||
market_event_probability?: number | null;
|
||||
gap?: number | null;
|
||||
signed_gap?: number | null;
|
||||
yes_token_id?: string | null;
|
||||
no_token_id?: string | null;
|
||||
yes_ask?: number | null;
|
||||
yes_bid?: number | null;
|
||||
no_ask?: number | null;
|
||||
no_bid?: number | null;
|
||||
ask?: number | null;
|
||||
bid?: number | null;
|
||||
midpoint?: number | null;
|
||||
spread?: number | null;
|
||||
book_liquidity?: number | null;
|
||||
market_liquidity?: number | null;
|
||||
volume?: number | null;
|
||||
quote_source?: string | null;
|
||||
quote_age_ms?: number | null;
|
||||
edge?: number | null;
|
||||
edge_percent?: number | null;
|
||||
edge_score?: number | null;
|
||||
bias_score?: number | null;
|
||||
distribution_bias?: DistributionBias | null;
|
||||
distribution_bias_direction?: string | null;
|
||||
distribution_bias_score?: number | null;
|
||||
distribution_bias_available?: boolean;
|
||||
window_phase?: string | null;
|
||||
window_score?: number | null;
|
||||
remaining_window_minutes?: number | null;
|
||||
liquidity_score?: number | null;
|
||||
price_usefulness_score?: number | null;
|
||||
spread_penalty?: number | null;
|
||||
final_score?: number | null;
|
||||
current_reference?: number | null;
|
||||
gap_to_target?: number | null;
|
||||
touch_distance?: number | null;
|
||||
trend_alignment?: boolean;
|
||||
tradable?: boolean;
|
||||
active?: boolean;
|
||||
closed?: boolean;
|
||||
accepting_orders?: boolean;
|
||||
enable_order_book?: boolean;
|
||||
is_primary_market?: boolean;
|
||||
is_primary_signal?: boolean;
|
||||
signal_confidence?: number | null;
|
||||
signal_status?: string | null;
|
||||
candidate_count?: number | null;
|
||||
resolved_market_type?: string | null;
|
||||
}
|
||||
|
||||
export interface PrimarySignal extends ScanOpportunityRow {}
|
||||
|
||||
export interface ScanTerminalResponse {
|
||||
generated_at: string;
|
||||
filters: ScanTerminalFilters;
|
||||
summary: {
|
||||
recommended_count: number;
|
||||
visible_count: number;
|
||||
candidate_total: number;
|
||||
avg_edge_percent?: number | null;
|
||||
avg_primary_confidence?: number | null;
|
||||
tradable_market_count: number;
|
||||
total_volume: number;
|
||||
resolved_market_type?: string | null;
|
||||
};
|
||||
top_signal?: PrimarySignal | null;
|
||||
rows: ScanOpportunityRow[];
|
||||
}
|
||||
|
||||
export interface IntradayMeteorologySignal {
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -484,3 +484,193 @@ def test_find_primary_market_prefers_preferred_temperature_and_cache_key():
|
||||
assert reason_22 is None
|
||||
assert selected_27["slug"] == "highest-temperature-in-madrid-on-april-23-2026-27c"
|
||||
assert selected_22["slug"] == "highest-temperature-in-madrid-on-april-23-2026-22corbelow"
|
||||
|
||||
|
||||
def _build_scan_test_layer():
|
||||
layer = PolymarketReadOnlyLayer()
|
||||
markets = [
|
||||
{
|
||||
"id": "m-above-14",
|
||||
"slug": "highest-temperature-in-wellington-on-april-24-2026-14c-or-higher",
|
||||
"question": "Will the highest temperature in Wellington be 14C or higher on April 24?",
|
||||
"active": True,
|
||||
"closed": False,
|
||||
"acceptingOrders": True,
|
||||
"enableOrderBook": True,
|
||||
"liquidityNum": 12000,
|
||||
"volumeNum": 4000,
|
||||
"_model_prob": 0.60,
|
||||
},
|
||||
{
|
||||
"id": "m-below-16",
|
||||
"slug": "highest-temperature-in-wellington-on-april-24-2026-16c-or-lower",
|
||||
"question": "Will the highest temperature in Wellington be 16C or lower on April 24?",
|
||||
"active": True,
|
||||
"closed": False,
|
||||
"acceptingOrders": True,
|
||||
"enableOrderBook": True,
|
||||
"liquidityNum": 9000,
|
||||
"volumeNum": 3500,
|
||||
"_model_prob": 0.30,
|
||||
},
|
||||
{
|
||||
"id": "m-above-17",
|
||||
"slug": "highest-temperature-in-wellington-on-april-24-2026-17c-or-higher",
|
||||
"question": "Will the highest temperature in Wellington be 17C or higher on April 24?",
|
||||
"active": True,
|
||||
"closed": False,
|
||||
"acceptingOrders": True,
|
||||
"enableOrderBook": True,
|
||||
"liquidityNum": 7000,
|
||||
"volumeNum": 2800,
|
||||
"_model_prob": 0.20,
|
||||
},
|
||||
]
|
||||
token_map = {
|
||||
"m-above-14": {"yes": "yes-14", "no": "no-14"},
|
||||
"m-below-16": {"yes": "yes-16", "no": "no-16"},
|
||||
"m-above-17": {"yes": "yes-17", "no": "no-17"},
|
||||
}
|
||||
quote_map = {
|
||||
"yes-14": {"buy": 0.48, "sell": 0.46, "midpoint": 0.40, "spread": 0.02, "book_liquidity": 14000},
|
||||
"no-14": {"buy": 0.54, "sell": 0.52, "midpoint": 0.60, "spread": 0.02, "book_liquidity": 14000},
|
||||
"yes-16": {"buy": 0.42, "sell": 0.40, "midpoint": 0.50, "spread": 0.02, "book_liquidity": 9000},
|
||||
"no-16": {"buy": 0.56, "sell": 0.54, "midpoint": 0.50, "spread": 0.02, "book_liquidity": 9000},
|
||||
"yes-17": {"buy": 0.08, "sell": 0.07, "midpoint": 0.10, "spread": 0.01, "book_liquidity": 7500},
|
||||
"no-17": {"buy": 0.92, "sell": 0.91, "midpoint": 0.90, "spread": 0.01, "book_liquidity": 7500},
|
||||
}
|
||||
|
||||
layer._collect_related_temperature_markets = lambda **_kwargs: markets
|
||||
layer._aggregate_distribution_probability_for_market = (
|
||||
lambda market, **_kwargs: market.get("_model_prob")
|
||||
)
|
||||
layer._extract_market_tokens = lambda market: [
|
||||
{"outcome": "Yes", "token_id": token_map[market["id"]]["yes"]},
|
||||
{"outcome": "No", "token_id": token_map[market["id"]]["no"]},
|
||||
]
|
||||
layer._batch_get_token_market_data = (
|
||||
lambda token_ids, include_books=False: {
|
||||
token_id: dict(quote_map[token_id])
|
||||
for token_id in token_ids
|
||||
if token_id in quote_map
|
||||
}
|
||||
)
|
||||
return layer, markets
|
||||
|
||||
|
||||
def test_distribution_scan_bias_flips_below_markets_into_hotter_signal():
|
||||
layer, markets = _build_scan_test_layer()
|
||||
|
||||
scan = layer._build_distribution_scan_pack(
|
||||
city_key="wellington",
|
||||
target_date="2026-04-24",
|
||||
primary_market=markets[0],
|
||||
probability_distribution=[],
|
||||
temp_symbol="°C",
|
||||
scan_context={
|
||||
"local_date": "2026-04-24",
|
||||
"local_time": "13:10",
|
||||
"peak": {"first_h": 14, "last_h": 16},
|
||||
"current_max_so_far": 13.4,
|
||||
"current_temp": 13.0,
|
||||
"trend": {"recent": []},
|
||||
"network_lead_signal": {},
|
||||
},
|
||||
scan_filters={"limit": 10},
|
||||
)
|
||||
|
||||
bias = scan["distribution_bias"]
|
||||
assert bias["available"] is True
|
||||
assert bias["direction"] == "hotter"
|
||||
assert bias["score"] > 0
|
||||
|
||||
|
||||
def test_distribution_scan_returns_single_primary_signal_from_yes_no_mix():
|
||||
layer, markets = _build_scan_test_layer()
|
||||
|
||||
scan = layer._build_distribution_scan_pack(
|
||||
city_key="wellington",
|
||||
target_date="2026-04-24",
|
||||
primary_market=markets[0],
|
||||
probability_distribution=[],
|
||||
temp_symbol="°C",
|
||||
scan_context={
|
||||
"local_date": "2026-04-24",
|
||||
"local_time": "13:10",
|
||||
"peak": {"first_h": 14, "last_h": 16},
|
||||
"current_max_so_far": 13.6,
|
||||
"current_temp": 13.2,
|
||||
"trend": {"recent": []},
|
||||
"network_lead_signal": {},
|
||||
},
|
||||
scan_filters={"limit": 10, "min_edge_pct": 2},
|
||||
)
|
||||
|
||||
assert scan["candidate_count"] >= 2
|
||||
assert isinstance(scan["primary_signal"], dict)
|
||||
assert scan["primary_signal"]["side"] == "yes"
|
||||
assert scan["primary_signal"]["id"] == scan["rows"][0]["id"]
|
||||
assert scan["signal_status"] == "ready"
|
||||
|
||||
|
||||
def test_distribution_scan_hard_filters_block_unusable_extreme_quotes():
|
||||
layer, markets = _build_scan_test_layer()
|
||||
layer._batch_get_token_market_data = (
|
||||
lambda token_ids, include_books=False: {
|
||||
token_id: {
|
||||
"buy": 0.99 if token_id.startswith("yes") else 0.01,
|
||||
"sell": 0.95 if token_id.startswith("yes") else 0.0,
|
||||
"midpoint": 0.97 if token_id.startswith("yes") else 0.03,
|
||||
"spread": 0.04,
|
||||
"book_liquidity": 100,
|
||||
}
|
||||
for token_id in token_ids
|
||||
}
|
||||
)
|
||||
|
||||
scan = layer._build_distribution_scan_pack(
|
||||
city_key="wellington",
|
||||
target_date="2026-04-24",
|
||||
primary_market=markets[0],
|
||||
probability_distribution=[],
|
||||
temp_symbol="°C",
|
||||
scan_context={
|
||||
"local_date": "2026-04-24",
|
||||
"local_time": "13:10",
|
||||
"peak": {"first_h": 14, "last_h": 16},
|
||||
"current_max_so_far": 13.6,
|
||||
"current_temp": 13.2,
|
||||
"trend": {"recent": []},
|
||||
"network_lead_signal": {},
|
||||
},
|
||||
scan_filters={"limit": 10},
|
||||
)
|
||||
|
||||
assert scan["signal_status"] == "no_signal"
|
||||
assert scan["candidate_count"] == 0
|
||||
assert scan["rows"] == []
|
||||
|
||||
|
||||
def test_batch_token_market_data_falls_back_to_single_fetch_when_batch_fails():
|
||||
layer = PolymarketReadOnlyLayer()
|
||||
layer._clob_post = lambda *_args, **_kwargs: None
|
||||
layer._fetch_token_market_data = lambda token_id: {
|
||||
"buy": 0.33,
|
||||
"sell": 0.31,
|
||||
"midpoint": 0.32,
|
||||
"quote_source": f"fallback:{token_id}",
|
||||
}
|
||||
|
||||
result = layer._batch_get_token_market_data(["token-a", "token-b"])
|
||||
|
||||
assert result["token-a"]["midpoint"] == 0.32
|
||||
assert result["token-b"]["quote_source"] == "fallback:token-b"
|
||||
|
||||
|
||||
def test_normalize_scan_filters_raises_liquidity_floor_when_high_liquidity_only():
|
||||
layer = PolymarketReadOnlyLayer()
|
||||
|
||||
filters = layer._normalize_scan_filters({"high_liquidity_only": True})
|
||||
|
||||
assert filters["high_liquidity_only"] is True
|
||||
assert filters["min_liquidity"] >= 5000
|
||||
|
||||
@@ -3,6 +3,7 @@ from fastapi.testclient import TestClient
|
||||
|
||||
from web.app import app
|
||||
import web.routes as routes
|
||||
from web.scan_terminal_service import _scan_terminal_cache_key
|
||||
from src.database.db_manager import DBManager
|
||||
from src.database.runtime_state import TruthRecordRepository, TrainingFeatureRecordRepository
|
||||
|
||||
@@ -260,6 +261,69 @@ def test_ops_truth_history_returns_filtered_rows(monkeypatch):
|
||||
assert payload["items"][0]["settlement_station_code"] == "RCSS"
|
||||
|
||||
|
||||
def test_scan_terminal_endpoint_forwards_filters(monkeypatch):
|
||||
monkeypatch.setattr(routes, "_assert_entitlement", lambda request: None)
|
||||
|
||||
captured = {}
|
||||
|
||||
def _fake_build_scan_terminal_payload(filters, *, force_refresh=False):
|
||||
captured["filters"] = dict(filters)
|
||||
captured["force_refresh"] = force_refresh
|
||||
return {
|
||||
"generated_at": "2026-04-23T00:00:00Z",
|
||||
"filters": filters,
|
||||
"summary": {
|
||||
"recommended_count": 1,
|
||||
"visible_count": 1,
|
||||
"candidate_total": 3,
|
||||
"avg_edge_percent": 4.2,
|
||||
"avg_primary_confidence": 88.0,
|
||||
"tradable_market_count": 1,
|
||||
"total_volume": 1500,
|
||||
"resolved_market_type": "maxtemp",
|
||||
},
|
||||
"top_signal": None,
|
||||
"rows": [],
|
||||
}
|
||||
|
||||
monkeypatch.setattr(routes, "build_scan_terminal_payload", _fake_build_scan_terminal_payload)
|
||||
|
||||
response = client.get(
|
||||
"/api/scan/terminal?scan_mode=trend&min_price=0.1&max_price=0.8&min_edge_pct=3"
|
||||
"&min_liquidity=700&high_liquidity_only=true&market_type=all&time_range=week&limit=12&force_refresh=true"
|
||||
)
|
||||
|
||||
assert response.status_code == 200
|
||||
payload = response.json()
|
||||
assert payload["summary"]["recommended_count"] == 1
|
||||
assert captured["force_refresh"] is True
|
||||
assert captured["filters"]["scan_mode"] == "trend"
|
||||
assert captured["filters"]["market_type"] == "all"
|
||||
assert captured["filters"]["time_range"] == "week"
|
||||
assert captured["filters"]["limit"] == 12
|
||||
|
||||
|
||||
def test_scan_terminal_cache_key_includes_filter_dimensions():
|
||||
first = _scan_terminal_cache_key(
|
||||
{
|
||||
"scan_mode": "tradable",
|
||||
"time_range": "today",
|
||||
"limit": 25,
|
||||
}
|
||||
)
|
||||
second = _scan_terminal_cache_key(
|
||||
{
|
||||
"scan_mode": "trend",
|
||||
"time_range": "week",
|
||||
"limit": 10,
|
||||
}
|
||||
)
|
||||
|
||||
assert first != second
|
||||
assert "trend" in second
|
||||
assert "week" in second
|
||||
|
||||
|
||||
def test_city_history_is_read_only_and_uses_sqlite_truth_and_features(monkeypatch):
|
||||
monkeypatch.setattr(routes, "_assert_entitlement", lambda request: None)
|
||||
|
||||
|
||||
@@ -2805,6 +2805,7 @@ def _build_city_market_scan_payload(
|
||||
market_slug: Optional[str] = None,
|
||||
target_date: Optional[str] = None,
|
||||
lite: bool = False,
|
||||
scan_filters: Optional[Dict[str, Any]] = None,
|
||||
) -> Dict[str, Any]:
|
||||
city = str(data.get("name") or "").strip().lower()
|
||||
local_date = str(data.get("local_date") or "").strip()
|
||||
@@ -2878,6 +2879,16 @@ def _build_city_market_scan_payload(
|
||||
for p in distribution_all[:8]
|
||||
if isinstance(p, dict)
|
||||
]
|
||||
current = data.get("current") or {}
|
||||
scan_context = {
|
||||
"local_date": data.get("local_date"),
|
||||
"local_time": data.get("local_time"),
|
||||
"peak": data.get("peak") or {},
|
||||
"current_max_so_far": current.get("max_so_far"),
|
||||
"current_temp": current.get("temp"),
|
||||
"trend": data.get("trend") or {},
|
||||
"network_lead_signal": data.get("network_lead_signal") or {},
|
||||
}
|
||||
market_scan = _market_layer.build_market_scan(
|
||||
city=data.get("name"),
|
||||
target_date=selected_date or data.get("local_date"),
|
||||
@@ -2888,6 +2899,8 @@ def _build_city_market_scan_payload(
|
||||
fallback_sparkline=fallback_sparkline,
|
||||
forced_market_slug=market_slug,
|
||||
include_related_buckets=not lite,
|
||||
scan_filters=scan_filters,
|
||||
scan_context=scan_context,
|
||||
)
|
||||
if isinstance(market_scan, dict):
|
||||
market_scan["anchor_model"] = anchor_model
|
||||
|
||||
@@ -32,6 +32,7 @@ from web.analysis_service import (
|
||||
_build_city_market_scan_payload,
|
||||
_build_city_summary_payload,
|
||||
)
|
||||
from web.scan_terminal_service import build_scan_terminal_payload
|
||||
from web.core import (
|
||||
AnalyticsEventRequest,
|
||||
CITIES,
|
||||
@@ -1694,3 +1695,36 @@ async def city_market_scan(
|
||||
lite,
|
||||
)
|
||||
|
||||
|
||||
@router.get("/api/scan/terminal")
|
||||
async def scan_terminal(
|
||||
request: Request,
|
||||
scan_mode: str = "tradable",
|
||||
min_price: float = 0.05,
|
||||
max_price: float = 0.95,
|
||||
min_edge_pct: float = 2.0,
|
||||
min_liquidity: float = 500.0,
|
||||
high_liquidity_only: bool = False,
|
||||
market_type: str = "maxtemp",
|
||||
time_range: str = "today",
|
||||
limit: int = 25,
|
||||
force_refresh: bool = False,
|
||||
):
|
||||
_assert_entitlement(request)
|
||||
filters = {
|
||||
"scan_mode": scan_mode,
|
||||
"min_price": min_price,
|
||||
"max_price": max_price,
|
||||
"min_edge_pct": min_edge_pct,
|
||||
"min_liquidity": min_liquidity,
|
||||
"high_liquidity_only": high_liquidity_only,
|
||||
"market_type": market_type,
|
||||
"time_range": time_range,
|
||||
"limit": limit,
|
||||
}
|
||||
return await run_in_threadpool(
|
||||
build_scan_terminal_payload,
|
||||
filters,
|
||||
force_refresh=force_refresh,
|
||||
)
|
||||
|
||||
|
||||
@@ -0,0 +1,358 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import os
|
||||
import threading
|
||||
import time
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from loguru import logger
|
||||
|
||||
from web.analysis_service import _analyze, _build_city_market_scan_payload
|
||||
from web.core import CITIES
|
||||
|
||||
_SCAN_TERMINAL_CACHE_LOCK = threading.Lock()
|
||||
_SCAN_TERMINAL_CACHE: Dict[str, Dict[str, Any]] = {}
|
||||
SCAN_TERMINAL_PAYLOAD_TTL_SEC = max(
|
||||
5,
|
||||
int(os.getenv("POLYWEATHER_SCAN_TERMINAL_PAYLOAD_TTL_SEC", "30")),
|
||||
)
|
||||
|
||||
|
||||
def _safe_float(value: Any) -> Optional[float]:
|
||||
try:
|
||||
if value is None or value == "":
|
||||
return None
|
||||
return float(value)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
def _safe_int(value: Any, default: int) -> int:
|
||||
try:
|
||||
return int(value)
|
||||
except Exception:
|
||||
return int(default)
|
||||
|
||||
|
||||
def _normalize_scan_terminal_filters(
|
||||
raw_filters: Optional[Dict[str, Any]] = None,
|
||||
) -> Dict[str, Any]:
|
||||
raw = raw_filters if isinstance(raw_filters, dict) else {}
|
||||
min_price = _safe_float(raw.get("min_price"))
|
||||
max_price = _safe_float(raw.get("max_price"))
|
||||
if min_price is None:
|
||||
min_price = 0.05
|
||||
if max_price is None:
|
||||
max_price = 0.95
|
||||
min_price = max(0.0, min(1.0, min_price))
|
||||
max_price = max(0.0, min(1.0, max_price))
|
||||
if min_price > max_price:
|
||||
min_price, max_price = max_price, min_price
|
||||
|
||||
high_liquidity_only = bool(raw.get("high_liquidity_only"))
|
||||
min_liquidity = _safe_float(raw.get("min_liquidity"))
|
||||
if min_liquidity is None:
|
||||
min_liquidity = 5000.0 if high_liquidity_only else 500.0
|
||||
if high_liquidity_only:
|
||||
min_liquidity = max(min_liquidity, 5000.0)
|
||||
|
||||
return {
|
||||
"scan_mode": str(raw.get("scan_mode") or "tradable").strip().lower()
|
||||
or "tradable",
|
||||
"min_price": float(min_price),
|
||||
"max_price": float(max_price),
|
||||
"min_edge_pct": max(0.0, _safe_float(raw.get("min_edge_pct")) or 2.0),
|
||||
"min_liquidity": max(0.0, float(min_liquidity)),
|
||||
"high_liquidity_only": high_liquidity_only,
|
||||
"market_type": str(raw.get("market_type") or "maxtemp").strip().lower()
|
||||
or "maxtemp",
|
||||
"time_range": str(raw.get("time_range") or "today").strip().lower()
|
||||
or "today",
|
||||
"limit": max(1, min(_safe_int(raw.get("limit"), 25), 100)),
|
||||
"max_spread": max(0.0, _safe_float(raw.get("max_spread")) or 0.03),
|
||||
}
|
||||
|
||||
|
||||
def _scan_terminal_cache_key(filters: Dict[str, Any]) -> str:
|
||||
normalized = _normalize_scan_terminal_filters(filters)
|
||||
return json.dumps(normalized, ensure_ascii=True, sort_keys=True)
|
||||
|
||||
|
||||
def _get_cached_scan_terminal_payload(
|
||||
filters: Dict[str, Any],
|
||||
) -> Optional[Dict[str, Any]]:
|
||||
cache_key = _scan_terminal_cache_key(filters)
|
||||
now = time.time()
|
||||
with _SCAN_TERMINAL_CACHE_LOCK:
|
||||
cached = _SCAN_TERMINAL_CACHE.get(cache_key)
|
||||
if not cached:
|
||||
return None
|
||||
cached_at = float(cached.get("t") or 0.0)
|
||||
if now - cached_at >= float(SCAN_TERMINAL_PAYLOAD_TTL_SEC):
|
||||
return None
|
||||
payload = cached.get("payload")
|
||||
if not isinstance(payload, dict):
|
||||
return None
|
||||
return dict(payload)
|
||||
|
||||
|
||||
def _set_cached_scan_terminal_payload(
|
||||
filters: Dict[str, Any],
|
||||
payload: Dict[str, Any],
|
||||
) -> None:
|
||||
cache_key = _scan_terminal_cache_key(filters)
|
||||
with _SCAN_TERMINAL_CACHE_LOCK:
|
||||
_SCAN_TERMINAL_CACHE[cache_key] = {
|
||||
"t": time.time(),
|
||||
"payload": dict(payload),
|
||||
}
|
||||
|
||||
|
||||
def _resolve_time_range_dates(data: Dict[str, Any], time_range: str) -> List[str]:
|
||||
local_date = str(data.get("local_date") or "").strip()
|
||||
multi_model_daily = data.get("multi_model_daily") or {}
|
||||
available_dates = sorted(
|
||||
str(date_key).strip()
|
||||
for date_key in (multi_model_daily.keys() if isinstance(multi_model_daily, dict) else [])
|
||||
if str(date_key).strip()
|
||||
)
|
||||
|
||||
if not local_date:
|
||||
return available_dates[:1]
|
||||
if time_range == "today":
|
||||
return [local_date]
|
||||
|
||||
try:
|
||||
local_dt = datetime.fromisoformat(local_date)
|
||||
except Exception:
|
||||
return available_dates[:7] if time_range == "week" else available_dates[:1]
|
||||
|
||||
if time_range == "tomorrow":
|
||||
target = (local_dt + timedelta(days=1)).strftime("%Y-%m-%d")
|
||||
if target in available_dates:
|
||||
return [target]
|
||||
future_dates = [date_key for date_key in available_dates if date_key > local_date]
|
||||
return future_dates[:1]
|
||||
|
||||
if time_range == "week":
|
||||
target_dates = [date_key for date_key in available_dates if date_key >= local_date]
|
||||
if local_date not in target_dates:
|
||||
target_dates.insert(0, local_date)
|
||||
deduped: List[str] = []
|
||||
for date_key in target_dates:
|
||||
if date_key not in deduped:
|
||||
deduped.append(date_key)
|
||||
if len(deduped) >= 7:
|
||||
break
|
||||
return deduped
|
||||
|
||||
return [local_date]
|
||||
|
||||
|
||||
def _build_terminal_row(
|
||||
*,
|
||||
city: str,
|
||||
data: Dict[str, Any],
|
||||
scan: Dict[str, Any],
|
||||
row: Dict[str, Any],
|
||||
) -> Dict[str, Any]:
|
||||
current = data.get("current") or {}
|
||||
multi_model_daily = data.get("multi_model_daily") or {}
|
||||
selected_date = str(row.get("selected_date") or scan.get("selected_date") or data.get("local_date") or "").strip()
|
||||
daily_entry = multi_model_daily.get(selected_date) if isinstance(multi_model_daily, dict) else {}
|
||||
if not isinstance(daily_entry, dict):
|
||||
daily_entry = {}
|
||||
|
||||
display_name = str(data.get("display_name") or city).strip() or city
|
||||
market_slug = str(row.get("market_slug") or "").strip()
|
||||
side = str(row.get("side") or "").strip().lower()
|
||||
edge_percent = _safe_float(row.get("edge_percent"))
|
||||
final_score = _safe_float(row.get("final_score"))
|
||||
volume = _safe_float(row.get("volume")) or 0.0
|
||||
primary_signal = scan.get("primary_signal") or {}
|
||||
|
||||
return {
|
||||
**row,
|
||||
"id": str(row.get("id") or f"{city}|{selected_date}|{market_slug}|{side}"),
|
||||
"city": city,
|
||||
"city_display_name": display_name,
|
||||
"selected_date": selected_date or None,
|
||||
"local_date": data.get("local_date"),
|
||||
"local_time": data.get("local_time"),
|
||||
"temp_symbol": data.get("temp_symbol"),
|
||||
"current_temp": current.get("temp"),
|
||||
"current_max_so_far": current.get("max_so_far"),
|
||||
"deb_prediction": ((daily_entry.get("deb") or {}).get("prediction") if isinstance(daily_entry.get("deb"), dict) else None)
|
||||
or ((data.get("deb") or {}).get("prediction") if isinstance(data.get("deb"), dict) else None),
|
||||
"display_name": display_name,
|
||||
"airport": ((data.get("risk") or {}).get("airport") if isinstance(data.get("risk"), dict) else None),
|
||||
"risk_level": ((data.get("risk") or {}).get("level") if isinstance(data.get("risk"), dict) else None),
|
||||
"distribution_bias": scan.get("distribution_bias"),
|
||||
"window_phase": row.get("window_phase") or scan.get("window_phase"),
|
||||
"window_score": row.get("window_score") if row.get("window_score") is not None else scan.get("window_score"),
|
||||
"signal_status": scan.get("signal_status"),
|
||||
"candidate_count": scan.get("candidate_count"),
|
||||
"resolved_market_type": scan.get("resolved_market_type") or "maxtemp",
|
||||
"market_key": f"{city}|{selected_date}|{market_slug}",
|
||||
"is_primary_signal": bool(primary_signal and primary_signal.get("id") == row.get("id")),
|
||||
"signal_confidence": final_score,
|
||||
"edge_percent": edge_percent,
|
||||
"final_score": final_score,
|
||||
"volume": volume,
|
||||
}
|
||||
|
||||
|
||||
def _scan_city_terminal_rows(
|
||||
city: str,
|
||||
filters: Dict[str, Any],
|
||||
*,
|
||||
force_refresh: bool = False,
|
||||
) -> Dict[str, Any]:
|
||||
data = _analyze(
|
||||
city,
|
||||
force_refresh=force_refresh,
|
||||
include_llm_commentary=False,
|
||||
detail_mode="market",
|
||||
)
|
||||
target_dates = _resolve_time_range_dates(data, filters["time_range"])
|
||||
rows: List[Dict[str, Any]] = []
|
||||
primary_scores: List[float] = []
|
||||
candidate_total = 0
|
||||
|
||||
for target_date in target_dates:
|
||||
payload = _build_city_market_scan_payload(
|
||||
data,
|
||||
market_slug=None,
|
||||
target_date=target_date,
|
||||
lite=True,
|
||||
scan_filters=filters,
|
||||
)
|
||||
scan = payload.get("market_scan") or {}
|
||||
candidate_total += int(scan.get("candidate_count") or 0)
|
||||
primary_signal = scan.get("primary_signal")
|
||||
if not isinstance(primary_signal, dict) or not primary_signal:
|
||||
continue
|
||||
row = _build_terminal_row(
|
||||
city=city,
|
||||
data=data,
|
||||
scan=scan,
|
||||
row=primary_signal,
|
||||
)
|
||||
rows.append(row)
|
||||
score = _safe_float(row.get("final_score"))
|
||||
if score is not None:
|
||||
primary_scores.append(score)
|
||||
|
||||
return {
|
||||
"city": city,
|
||||
"rows": rows,
|
||||
"candidate_total": candidate_total,
|
||||
"primary_scores": primary_scores,
|
||||
}
|
||||
|
||||
|
||||
def build_scan_terminal_payload(
|
||||
raw_filters: Optional[Dict[str, Any]] = None,
|
||||
*,
|
||||
force_refresh: bool = False,
|
||||
) -> Dict[str, Any]:
|
||||
filters = _normalize_scan_terminal_filters(raw_filters)
|
||||
if not force_refresh:
|
||||
cached = _get_cached_scan_terminal_payload(filters)
|
||||
if cached is not None:
|
||||
return cached
|
||||
|
||||
city_names = list(CITIES.keys())
|
||||
max_workers = max(1, min(6, len(city_names)))
|
||||
city_results: List[Dict[str, Any]] = []
|
||||
|
||||
with ThreadPoolExecutor(max_workers=max_workers) as executor:
|
||||
future_map = {
|
||||
executor.submit(
|
||||
_scan_city_terminal_rows,
|
||||
city_name,
|
||||
filters,
|
||||
force_refresh=force_refresh,
|
||||
): city_name
|
||||
for city_name in city_names
|
||||
}
|
||||
for future in as_completed(future_map):
|
||||
city_name = future_map[future]
|
||||
try:
|
||||
city_results.append(future.result())
|
||||
except Exception as exc:
|
||||
logger.warning("scan terminal city failed city={}: {}", city_name, exc)
|
||||
|
||||
primary_rows: List[Dict[str, Any]] = []
|
||||
primary_scores: List[float] = []
|
||||
candidate_total = 0
|
||||
|
||||
for result in city_results:
|
||||
candidate_total += int(result.get("candidate_total") or 0)
|
||||
primary_rows.extend(result.get("rows") or [])
|
||||
primary_scores.extend(result.get("primary_scores") or [])
|
||||
|
||||
primary_rows.sort(
|
||||
key=lambda row: (
|
||||
float(row.get("final_score") or 0.0),
|
||||
float(row.get("edge_percent") or 0.0),
|
||||
),
|
||||
reverse=True,
|
||||
)
|
||||
|
||||
ranked_rows: List[Dict[str, Any]] = []
|
||||
for index, row in enumerate(primary_rows[: filters["limit"]], start=1):
|
||||
ranked_rows.append(
|
||||
{
|
||||
**row,
|
||||
"rank": index,
|
||||
}
|
||||
)
|
||||
|
||||
unique_market_volume: Dict[str, float] = {}
|
||||
for row in primary_rows:
|
||||
market_key = str(row.get("market_key") or row.get("id") or "").strip()
|
||||
if not market_key:
|
||||
continue
|
||||
unique_market_volume[market_key] = max(
|
||||
unique_market_volume.get(market_key, 0.0),
|
||||
float(row.get("volume") or 0.0),
|
||||
)
|
||||
|
||||
avg_edge = None
|
||||
if primary_rows:
|
||||
edge_values = [
|
||||
float(row.get("edge_percent") or 0.0)
|
||||
for row in primary_rows
|
||||
if _safe_float(row.get("edge_percent")) is not None
|
||||
]
|
||||
if edge_values:
|
||||
avg_edge = sum(edge_values) / len(edge_values)
|
||||
|
||||
avg_confidence = None
|
||||
if primary_scores:
|
||||
avg_confidence = sum(primary_scores) / len(primary_scores)
|
||||
|
||||
top_signal = ranked_rows[0] if ranked_rows else None
|
||||
payload = {
|
||||
"generated_at": datetime.utcnow().isoformat() + "Z",
|
||||
"filters": filters,
|
||||
"summary": {
|
||||
"recommended_count": len(primary_rows),
|
||||
"visible_count": len(ranked_rows),
|
||||
"candidate_total": candidate_total,
|
||||
"avg_edge_percent": avg_edge,
|
||||
"avg_primary_confidence": avg_confidence,
|
||||
"tradable_market_count": len(unique_market_volume),
|
||||
"total_volume": sum(unique_market_volume.values()),
|
||||
"resolved_market_type": "maxtemp",
|
||||
},
|
||||
"top_signal": top_signal,
|
||||
"rows": ranked_rows,
|
||||
}
|
||||
|
||||
_set_cached_scan_terminal_payload(filters, payload)
|
||||
return payload
|
||||
Reference in New Issue
Block a user