584 lines
No EOL
20 KiB
Markdown
584 lines
No EOL
20 KiB
Markdown
Here is a complete, self-contained `REFERENCE.md` file that captures the entire system architecture. It bridges your plaintext TanStack Start frontend with a high-performance **Go-based data loader** streaming raw **YAML telemetry** into a **Valkey** cache (the open-source, high-performance successor to Redis).
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You can save this file locally to use as your architectural blueprint.
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### `REFERENCE.md`
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Markdown
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````
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# Architectural Reference: Quark's Holo-Grid Ledger
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This document serves as the absolute technical reference for the intergalactic terminal sportsbook. The architecture uses a three-tier design to handle high-frequency data streams safely and efficiently:
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1. **Data Layer**: Flat YAML configuration files mimicking raw sub-atomic telemetry.
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2. **Ingestion Layer (Go)**: A high-performance Go routine that parses YAML files and pushes records continuously into a Valkey cache instance.
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3. **Application Layer (TanStack Start)**: A type-safe, server-rendered frontend pulling directly from Valkey via server functions and background synchronization hooks.
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---
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## 1. System Topology
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```mermaid
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graph LR
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subgraph Data Layer
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A[(telemetry.yaml)]
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</nav>
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subgraph Ingestion Layer
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B[Go Ingestion Worker]
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end
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subgraph Cache Layer
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C[(Valkey Cache Instance)]
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end
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subgraph Presentation Layer
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D[TanStack Start SSR Server] --> E[Browser Terminal UI]
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end
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A --> B
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B -- HSET / LPUSH --> C
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C -- READ --> D
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````
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## 2. Data Layer: Telemetry YAML Layout
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Place these static definition assets under the `./data-source/` directory root.
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### File: `./data-source/sectors.yaml`
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YAML
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```
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sectors:
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- id: "sector-001"
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name: "Sector 001 (Earth Array)"
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quadrant: "Alpha"
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initial_shield_capacity: 100
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kinetic_yield: 4.2
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status: "LIVE"
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markets:
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- id: "s1-over"
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name: "Kinetic Yield Over 8.5 TW"
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initial_price: 1.85
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- id: "s1-under"
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name: "Kinetic Yield Under 8.5 TW"
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initial_price: 1.95
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- id: "wolf-359"
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name: "Wolf 359 Outpost"
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quadrant: "Alpha"
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initial_shield_capacity: 34
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kinetic_yield: 12.8
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status: "LIVE"
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markets:
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- id: "w359-fail"
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name: "Total Shield Failure"
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initial_price: 1.40
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- id: "w359-hold"
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name: "Defenses Hold Perimeter"
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initial_price: 2.80
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```
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## 3. Ingestion Layer: Go Worker Utility
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This Go engine handles parsing the local YAML file, calculating artificial odds fluctuations, and writing live JSON payloads directly into **Valkey** using standard Redis protocol definitions via the `go-redis/v9` driver.
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### File: `./ingestion-engine/main.go`
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Go
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```
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package main
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import (
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"context"
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"encoding/json"
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"fmt"
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"log"
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"math/rand"
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"os"
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"time"
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"[github.com/redis/go-redis/v9](https://github.com/redis/go-redis/v9)"
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"gopkg.in/yaml.v3"
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)
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type Market struct {
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ID string `yaml:"id" json:"id"`
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Name string `yaml:"name" json:"name"`
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Price float64 `yaml:"initial_price" json:"price"`
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Trend string `json:"trend"`
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}
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type Sector struct {
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ID string `yaml:"id" json:"id"`
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Name string `yaml:"name" json:"name"`
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Quadrant string `yaml:"quadrant" json:"quadrant"`
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ShieldCapacity int `yaml:"initial_shield_capacity" json:"shieldCapacity"`
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KineticYield float64 `yaml:"kinetic_yield" json:"kinetic_yield"`
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Status string `yaml:"status" json:"status"`
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Markets []Market `yaml:"markets" json:"markets"`
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}
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type Config struct {
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Sectors []Sector `yaml:"sectors"`
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}
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var ctx = context.Background()
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func main() {
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// 1. Connect to Valkey (Using standard Redis protocol port 6379)
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valkeyClient := redis.NewClient(&redis.Options{
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Addr: "localhost:6379",
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Password: "", // Default empty
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DB: 0, // Default DB
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})
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// Test connection
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if err := valkeyClient.Ping(ctx).Err(); err != nil {
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log.Fatalf("Critical: Could not resolve Valkey connection: %v", err)
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}
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fmt.Println("> Subspace Comms Link to Valkey established.")
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// 2. Read and parse the YAML file
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yamlFile, err := os.ReadFile("../data-source/sectors.yaml")
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if err != nil {
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log.Fatalf("Error reading target file definitions: %v", err)
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}
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var config Config
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if err := yaml.Unmarshal(yamlFile, &config); err != nil {
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log.Fatalf("Error parsing configuration details: %v", err)
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}
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// 3. Fire the Ingestion Loop Simulation
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ticker := time.NewTicker(2000 * time.Millisecond)
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defer ticker.Stop()
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fmt.Println("> Telemetry engine loop initiated. Streaming telemetry logs...")
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for range ticker.C {
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for i := range config.Sectors {
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sector := &config.Sectors[i]
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// Fluctuate shield capacities artificially
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shieldChange := rand.Intn(5) - 2 // -2 to +2
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sector.ShieldCapacity += shieldChange
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if sector.ShieldCapacity > 100 {
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sector.ShieldCapacity = 100
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} else if sector.ShieldCapacity < 0 {
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sector.ShieldCapacity = 0
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}
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// Modulate marker coefficients
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for j := range sector.Markets {
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market := §or.Markets[j]
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priceShift := (rand.Float64() * 0.2) - 0.1 // -0.10 to +0.10
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oldPrice := market.Price
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market.Price = oldPrice + priceShift
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if market.Price < 1.05 {
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market.Price = 1.05
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}
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if market.Price > oldPrice {
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market.Trend = "up"
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} else if market.Price < oldPrice {
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market.Trend = "down"
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} else {
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market.Trend = "stable"
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}
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}
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// Marshall state to absolute JSON for application caching efficiency
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jsonPayload, err := json.Marshal(sector)
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if err != nil {
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log.Printf("Error marshalling schema object: %v", err)
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continue
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}
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// Push atomic updates directly into Valkey hash set
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key := fmt.Sprintf("sector:telemetry:%s", sector.ID)
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err = valkeyClient.Set(ctx, key, jsonPayload, 0).Err()
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if err != nil {
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log.Printf("Valkey stream pipeline transmission error: %v", err)
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}
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}
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log.Println(">> Atomic telemetry matrix block sent to Valkey pipeline cluster.")
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}
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}
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```
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## 4. Application Layer: Reading from Valkey in TanStack Start
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Your frontend reads directly from the cache pool inside server execution limits. This guarantees sub-millisecond data delivery times to the rendering layer.
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First, make sure you have standard client integration parameters ready (`npm i ioredis`).
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### File: `./src/server/db/valkeyConnector.ts`
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TypeScript
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```
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import Redis from 'ioredis'
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// Connect to local Valkey instance over port 6379
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export const valkey = new Redis({
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host: '127.0.0.1',
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port: 6379,
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})
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export async function fetchLiveSectorFromCache(sectorId: string) {
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const data = await valkey.get(`sector:telemetry:${sectorId}`)
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if (!data) return null
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return JSON.parse(data)
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}
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```
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### File: `./src/routes/quadrants/$sectorId.tsx`
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TypeScript
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```
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import { createFileRoute } from '@tanstack/react-router'
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import { useQuery } from '@tanstack/react-query'
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import { fetchLiveSectorFromCache } from '../../server/db/valkeyConnector'
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import { createServerFn } from '@tanstack/react-start'
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// Server RPC definition boundary
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const getCachedSectorData = createServerFn({ method: 'GET' })
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.input(String)
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.handler(async ({ input: sectorId }) => {
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return await fetchLiveSectorFromCache(sectorId)
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})
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export const Route = createFileRoute('/quadrants/$sectorId')({
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loader: async ({ params }) => {
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const initialSnapshot = await getCachedSectorData({ input: params.sectorId })
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return { initialSnapshot }
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},
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component: TerminalGridPane,
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})
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function TerminalGridPane() {
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const { initialSnapshot } = Route.useLoaderData()
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const { sectorId } = Route.useParams()
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// High-frequency client polling strategy linking right back to Valkey keyspaces
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const { data: sector } = useQuery({
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queryKey: ['live-telemetry', sectorId],
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queryFn: () => getCachedSectorData({ input: sectorId }),
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initialData: initialSnapshot,
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refetchInterval: 2000,
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})
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if (!sector) return <pre className="text-rose-500">> ERROR: MATRIX SYNC COMPROMISED</pre>
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return (
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<div className="border border-emerald-500/30 p-4 bg-black font-mono">
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<h3 className="text-emerald-400 font-bold mb-2">> COGNITIVE STREAM: {sector.name}</h3>
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<div className="space-y-1 text-xs">
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<p>QUADRANT TELEMETRY: {sector.quadrant}</p>
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<p>SHIELD EFFICIENCY: <span className="text-amber-500">{sector.shieldCapacity}%</span></p>
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</div>
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{/* Dynamic Betting Selections Render */}
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<div className="mt-4 grid grid-cols-1 gap-2">
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{sector.markets.map((m: any) => (
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<button key={m.id} className="p-2 border border-slate-800 text-left hover:border-emerald-500 flex justify-between">
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<span>{m.name}</span>
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<span className={m.trend === 'up' ? 'text-emerald-400' : m.trend === 'down' ? 'text-rose-400' : 'text-slate-400'}>
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[{m.price.toFixed(2)}]
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</span>
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</button>
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))}
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</div>
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</div>
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)
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}
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```
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To bring factions into your data, you just need to explicitly name the two competing forces inside your **Sectors**. In a sportsbook, these are your **Home Team** and **Away Team** (or Competitor A and Competitor B).
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By adding a `factions` object to your data model, your Go engine can calculate specific tactical advantages, and your TanStack Start frontend can render a true _"Versus"_ scoreboard layout.
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Here is how you inject factions across your entire architecture.
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## 1. Update the Data Layer (`data-source/sectors.yaml`)
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Add a `factions` object to each sector inside your YAML file. Give each faction a clear name and track their individual shield capacities separately instead of having just one global shield score.
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YAML
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```
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sectors:
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- id: "sector-001"
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name: "Sector 001 (Earth Array)"
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quadrant: "Alpha"
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kinetic_yield: 4.2
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status: "LIVE"
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factions:
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home:
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name: "Starfleet Command"
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shield_capacity: 100
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away:
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name: "The Borg Collective"
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shield_capacity: 100
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markets:
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- id: "s1-home-win"
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name: "Starfleet Victory"
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initial_price: 2.10
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- id: "s1-away-win"
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name: "Borg Assimilation"
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initial_price: 1.65
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```
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## 2. Update the Go Ingestion Engine (`main.go`)
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Now, update your Go structs to parse these factions, and modify the engine loop so the factions actively damage each other's shields.
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Go
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|
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```
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type Faction struct {
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Name string `yaml:"name" json:"name"`
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ShieldCapacity int `yaml:"shield_capacity" json:"shieldCapacity"`
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}
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|
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type FactionsSchema struct {
|
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Home Faction `yaml:"home" json:"home"`
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Away Faction `yaml:"away" json:"away"`
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}
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|
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type Sector struct {
|
||
ID string `yaml:"id" json:"id"`
|
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Name string `yaml:"name" json:"name"`
|
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Quadrant string `yaml:"quadrant" json:"quadrant"`
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||
KineticYield float64 `yaml:"kinetic_yield" json:"kinetic_yield"`
|
||
Status string `yaml:"status" json:"status"`
|
||
Factions FactionsSchema `yaml:"factions" json:"factions"`
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Markets []Market `yaml:"markets" json:"markets"`
|
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}
|
||
|
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// Inside the background loop, simulate combat between the factions:
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for i := range config.Sectors {
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sector := &config.Sectors[i]
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||
|
||
// Randomly choose which faction takes a hit this tick
|
||
if rand.Float64() > 0.5 {
|
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sector.Factions.Home.ShieldCapacity -= rand.Intn(8) // Borg fires on Starfleet
|
||
} else {
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sector.Factions.Away.ShieldCapacity -= rand.Intn(5) // Starfleet fires on Borg
|
||
}
|
||
|
||
// Clamp values between 0 and 100
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if sector.Factions.Home.ShieldCapacity < 0 { sector.Factions.Home.ShieldCapacity = 0 }
|
||
if sector.Factions.Away.ShieldCapacity < 0 { sector.Factions.Away.ShieldCapacity = 0 }
|
||
|
||
// Dynamically shift odds based on who has higher shields
|
||
for j := range sector.Markets {
|
||
market := §or.Markets[j]
|
||
// If Home shields drop below Away shields, increase Home odds multiplier (underdog status)
|
||
if sector.Factions.Home.ShieldCapacity < sector.Factions.Away.ShieldCapacity {
|
||
if market.ID == "s1-home-win" { market.Price += 0.05 }
|
||
}
|
||
}
|
||
}
|
||
```
|
||
|
||
## 3. Render the Confrontation in the UI (`$sectorId.tsx`)
|
||
|
||
Now that Valkey is serving the detailed faction data, you can build a plaintext split-panel scoreboard at the top of your main grid layout.
|
||
|
||
TypeScript
|
||
|
||
```
|
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function SectorConsoleView() {
|
||
const { sectorId } = Route.useParams()
|
||
const { data: sector } = useQuery({
|
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queryKey: ['live-telemetry', sectorId],
|
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queryFn: () => getCachedSectorData({ input: sectorId }),
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||
refetchInterval: 2000,
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||
})
|
||
|
||
if (!sector) return <pre>> ACCESSING SYSTEM NET...</pre>
|
||
|
||
return (
|
||
<div className="space-y-4">
|
||
{/* Dynamic Versus Arena Header */}
|
||
<div className="border border-slate-800 bg-slate-950 p-4 font-mono text-center">
|
||
<div className="text-[10px] text-slate-500 mb-2">ENGAGEMENT MATRIX // {sector.name}</div>
|
||
|
||
<div className="grid grid-cols-7 items-center text-sm font-bold">
|
||
{/* Home Faction */}
|
||
<div className="col-span-3 text-right">
|
||
<span className="text-amber-400 block">{sector.factions.home.name}</span>
|
||
<span className="text-xs text-slate-400">SHD: {sector.factions.home.shieldCapacity}%</span>
|
||
</div>
|
||
|
||
{/* VS Divider */}
|
||
<div className="col-span-1 text-slate-600 tracking-widest animate-pulse text-xs">VS</div>
|
||
|
||
{/* Away Faction */}
|
||
<div className="col-span-3 text-left">
|
||
<span className="text-rose-400 block">{sector.factions.away.name}</span>
|
||
<span className="text-xs text-slate-400">SHD: {sector.factions.away.shieldCapacity}%</span>
|
||
</div>
|
||
</div>
|
||
|
||
{/* Global Match Clock / Energy Tracker */}
|
||
<div className="mt-3 border-t border-slate-900 pt-2 text-xs text-slate-500 flex justify-between px-4">
|
||
<span>TOTAL ENERGY RELEASED:</span>
|
||
<span className="text-emerald-400 font-bold">{sector.kinetic_yield.toFixed(1)} TW</span>
|
||
</div>
|
||
</div>
|
||
|
||
{/* Markets Selection Grid sits underneath this header view */}
|
||
</div>
|
||
)
|
||
}
|
||
```
|
||
|
||
Here is your consolidated, terminal-ready command dictionary mapping our **Star Trek Telemetry** to standard **Sportsbook Architecture**:
|
||
|
||
| **Space-Grid Term** | **Real-World Sportsbook Translation** | **Practical Example** |
|
||
| ------------------------- | ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------- |
|
||
| **Quadrant** | **Sport Category / League** | _Alpha Quadrant_ (Matches your top-level category menu, like filtering for Premier League or NBA). |
|
||
| **Sector** | **Individual Match / Fixture** | _Sector 001_ or _Wolf 359_ (The active "game page" where an event is currently happening). |
|
||
| **Factions** | **Teams / Contenders** | _Starfleet Command (Home)_ vs. _The Borg Collective (Away)_ (The competitors squaring off inside the arena). |
|
||
| **Shield Capacity** | **Live Defensive Metric / Health** | _Starfleet Shields: 74%_ (Acts like an active match timeline indicator, showing which side is taking damage or losing control). |
|
||
| **Kinetic Yield** | **Live In-Play Match Score** | _14.2 Terawatts released_ (The cumulative scoreboard counter that grows as action happens, like total points scored). |
|
||
| **Market** | **Betting Line / Wager Type** | _Faction Victory_ or _Total Kinetic Yield Over/Under_ (The explicit proposal lines a user risks their Latinum on). |
|
||
| **Latinum** | **Account Balance Base Currency** | _Gold-Pressed Latinum_ (The account balance backing your betslip tokens). |
|
||
| **Subspace Interruption** | **Odds Change Suspended Lockout** | _Market Suspended_ (The background Valkey cache triggers an automatic input lockout when a sudden scoring event happens). |
|
||
|
||
To generate a continuous, realistic stream of sports matches (Sectors) and betting options (Markets), you shouldn't hardcode individual events. Instead, you need a stable, static **Taxonomy Base Data** file.
|
||
|
||
Think of this base taxonomy file as a template library. Your Go ingestion engine will read this library, pick two opposing factions, assign them a sector arena, and generate the live match data dynamically.
|
||
|
||
Here is the structured YAML blueprint for your taxonomy database and how the generation engine maps it out.
|
||
|
||
## 1. The Core Taxonomy Configuration (`data-source/taxonomy.yaml`)
|
||
|
||
This file holds your static master rules. It defines what factions exist, which quadrants they belong to, their natural rivalries, and standard betting templates.
|
||
|
||
YAML
|
||
|
||
```
|
||
# data-source/taxonomy.yaml
|
||
|
||
quadrants:
|
||
- id: "alpha"
|
||
name: "Alpha Quadrant"
|
||
factions:
|
||
- id: "starfleet"
|
||
name: "Starfleet Command"
|
||
type: "defensive"
|
||
- id: "borg"
|
||
name: "The Borg Collective"
|
||
type: "aggressive"
|
||
- id: "cardassians"
|
||
name: "Cardassian Union"
|
||
type: "tactical"
|
||
|
||
- id: "beta"
|
||
name: "Beta Quadrant"
|
||
factions:
|
||
- id: "klingons"
|
||
name: "Klingon Empire"
|
||
type: "aggressive"
|
||
- id: "romulans"
|
||
name: "Romulan Star Empire"
|
||
type: "tactical"
|
||
|
||
# Standard templates used by the generator to instantiate structural betting lines
|
||
market_templates:
|
||
- id: "match-outcomes"
|
||
name: "Combat Resolution"
|
||
selections:
|
||
- type: "home-win"
|
||
suffix: "Decisive Victory"
|
||
- type: "away-win"
|
||
suffix: "Decisive Victory"
|
||
|
||
- id: "kinetic-over-under"
|
||
name: "Total Kinetic Yield"
|
||
selections:
|
||
- type: "over"
|
||
name: "Over 8.5 Terawatts"
|
||
base_price: 1.85
|
||
- type: "under"
|
||
name: "Under 8.5 Terawatts"
|
||
base_price: 1.85
|
||
|
||
# A list of location names the engine can pick to host matches
|
||
sector_pool:
|
||
- id: "wolf-359"
|
||
name: "Wolf 359 Outpost"
|
||
quadrant: "alpha"
|
||
- id: "sector-001"
|
||
name: "Sector 001 (Earth Core)"
|
||
quadrant: "alpha"
|
||
- id: "narendra-3"
|
||
name: "Narendra III Outpost"
|
||
quadrant: "beta"
|
||
```
|
||
|
||
## 2. The Generation Blueprint: From Rules to Live Data
|
||
|
||
When your Go ingestion engine spins up, it loops through this taxonomy file to generate actual live events using an instantiation pattern:
|
||
|
||
Plaintext
|
||
|
||
```
|
||
[ TAXONOMY DEFINITION ]
|
||
│
|
||
▼ (Go Generator Engine)
|
||
1. Pick Location: "Wolf 359" (Alpha)
|
||
2. Pick Home Faction: "Starfleet Command"
|
||
3. Pick Away Faction: "The Borg Collective"
|
||
4. Hydrate Market Templates: Substitute Faction names into strings
|
||
│
|
||
▼
|
||
[ LIVE VALKEY DATA STRUCTURE ]
|
||
```
|
||
|
||
## 3. The Go Generation Logic (Conceptual Snippet)
|
||
|
||
This is how your Go ingestion worker takes the abstract templates from `taxonomy.yaml` and handles generating real live event instances:
|
||
|
||
Go
|
||
|
||
```
|
||
// Psuedocode for the Go template instantiation loop
|
||
func GenerateLiveEvent(location SectorPoolItem, home Faction, away Faction, templates []MarketTemplate) Sector {
|
||
var generatedMarkets []Market
|
||
|
||
for _, tmpl := range templates {
|
||
if tmpl.ID == "match-outcomes" {
|
||
generatedMarkets = append(generatedMarkets, Market{
|
||
ID: location.ID + "-moneyline-home",
|
||
Name: home.Name + " " + tmpl.Selections[0].Suffix, // "Starfleet Command Decisive Victory"
|
||
Price: 2.00, // Balanced starting base odds
|
||
})
|
||
generatedMarkets = append(generatedMarkets, Market{
|
||
ID: location.ID + "-moneyline-away",
|
||
Name: away.Name + " " + tmpl.Selections[1].Suffix, // "The Borg Collective Decisive Victory"
|
||
Price: 1.80, // Borg slight favorite based on aggressive profile
|
||
})
|
||
}
|
||
}
|
||
|
||
return Sector{
|
||
ID: location.ID,
|
||
Name: location.Name,
|
||
Quadrant: location.Quadrant,
|
||
Status: "LIVE",
|
||
Factions: FactionsSchema{
|
||
Home: FactionState{Name: home.Name, ShieldCapacity: 100},
|
||
Away: FactionState{Name: away.Name, ShieldCapacity: 100},
|
||
},
|
||
Markets: generatedMarkets,
|
||
}
|
||
}
|
||
```
|
||
|
||
By organizing your database logic this way, you can add new teams (factions) or new stadiums (sectors) directly to your `taxonomy.yaml` file without altering a single line of your Go simulation logic or your TanStack Start React components. Everything down the wire becomes purely data-driven! |