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"proc-macro2", + "quote", + "syn", +] + +[[package]] +name = "zmij" +version = "1.0.21" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "b8848ee67ecc8aedbaf3e4122217aff892639231befc6a1b58d29fff4c2cabaa" diff --git a/Cargo.toml b/Cargo.toml new file mode 100644 index 0000000..a8c2f3d --- /dev/null +++ b/Cargo.toml @@ -0,0 +1,3 @@ +[workspace] +members = ["apps/api"] +resolver = "2" diff --git a/README.md b/README.md new file mode 100644 index 0000000..2f503d2 --- /dev/null +++ b/README.md @@ -0,0 +1,109 @@ +# 上海房市投资研究系统 + +这是一个面向长期研究和投资判断的上海房市分析底座。第一版重点不是预测神谕式的涨跌，而是建立可复用的数据结构、指标口径和报告流程。 + +当前版本包含： + +- SQLite 数据库 schema +- 官方/市场数据源目录 +- 样例数据加载器 +- 板块投资评分模型 +- 月度 Markdown 报告生成 +- Rust Axum API 骨架 +- PostgreSQL migrations +- 单元测试 + +> `data/sample` 中的数据是演示样例，不代表真实行情。正式研究时应替换为可审计的数据源。 + +## 快速开始 + +```bash +PYTHONPATH=src python3 -m shanghai_housing init-db +PYTHONPATH=src python3 -m shanghai_housing load-sample +PYTHONPATH=src python3 -m shanghai_housing report --month 2026-05 +``` + +生成报告位置： + +```bash +reports/monthly-2026-05.md +``` + +运行测试： + +```bash +PYTHONPATH=src python3 -m unittest discover -s tests +cargo test +``` + +## API 开发 + +API 服务位于 `apps/api`，采用 Rust Axum + SQLx。环境变量配置见： + +- [环境配置](docs/config/environment.md) + +本地启动示例： + +```bash +export DATABASE_URL="postgres://:@:/shanghai_housing_research_dev" +export RUN_MIGRATIONS=true +cargo run -p shanghai-housing-api +``` + +当前接口： + +```text +GET /health +GET /ready +GET /api/v1/areas/scores?month=2026-05 +GET /api/v1/market/overview?month=2026-05 +``` + +## 研究目标 + +系统要长期回答的不是单一“涨/跌”，而是： + +- 当前上海市场处于什么周期位置？ +- 哪些板块流动性更强？ +- 哪些资产有租金和产业支撑？ +- 哪些区域存在供应压力或价格压力？ +- 哪些小区进入观察池？ +- 触发买入、卖出、观望的条件是什么？ + +## MVP 架构 + +```text +config/source_catalog.json 数据源目录 +data/sample/ 演示样例数据 +src/shanghai_housing/schema.sql 数据库结构 +src/shanghai_housing/db.py 初始化和加载数据 +src/shanghai_housing/indicators.py 指标和评分模型 +src/shanghai_housing/reporting.py 报告生成 +src/shanghai_housing/cli.py 命令行入口 +docs/ 研究框架和数据字典 +``` + +## 第一版指标 + +- 成交活跃度 +- 成交价格动量 +- 挂牌库存压力 +- 成交/挂牌折价 +- 租金收益率 +- 新增供应压力 +- 信贷和政策环境 +- 综合投资观察评分 + +## 下一步 + +1. 接入真实月度数据源。 +2. 增加小区级评分和观察池。 +3. 建立板块历史分位和异常检测。 +4. 加入政策事件表和利率情景推演。 +5. 增加可视化看板。 + +## 长期架构 + +当前项目是研究内核 MVP，不是最终产品形态。完整方案见： + +- [系统架构与开发计划](docs/system_architecture_and_development_plan.md) diff --git a/apps/api/Cargo.toml b/apps/api/Cargo.toml new file mode 100644 index 0000000..aadeac3 --- /dev/null +++ b/apps/api/Cargo.toml @@ -0,0 +1,16 @@ +[package] +name = "shanghai-housing-api" +version = "0.1.0" +edition = "2021" + +[dependencies] +anyhow = "1" +axum = { version = "0.8", features = ["macros"] } +serde = { version = "1", features = ["derive"] } +serde_json = "1" +sqlx = { version = "0.8", features = ["runtime-tokio-rustls", "postgres", "migrate"] } +thiserror = "2" +tokio = { version = "1", features = ["macros", "rt-multi-thread", "signal"] } +tower-http = { version = "0.6", features = ["cors", "trace"] } +tracing = "0.1" +tracing-subscriber = { version = "0.3", features = ["env-filter", "fmt"] } diff --git a/apps/api/migrations/202606180001_init.sql b/apps/api/migrations/202606180001_init.sql new file mode 100644 index 0000000..f2ea81e --- /dev/null +++ b/apps/api/migrations/202606180001_init.sql @@ -0,0 +1,134 @@ +CREATE SCHEMA IF NOT EXISTS raw; +CREATE SCHEMA IF NOT EXISTS bronze; +CREATE SCHEMA IF NOT EXISTS silver; +CREATE SCHEMA IF NOT EXISTS gold; +CREATE SCHEMA IF NOT EXISTS app; +CREATE SCHEMA IF NOT EXISTS audit; + +CREATE TABLE IF NOT EXISTS audit.data_sources ( + source_id BIGSERIAL PRIMARY KEY, + name TEXT NOT NULL UNIQUE, + source_type TEXT NOT NULL, + url TEXT, + cadence TEXT, + reliability TEXT NOT NULL DEFAULT 'unknown', + notes TEXT NOT NULL DEFAULT '', + created_at TIMESTAMPTZ NOT NULL DEFAULT now() +); + +CREATE TABLE IF NOT EXISTS audit.ingestion_runs ( + run_id BIGSERIAL PRIMARY KEY, + source_id BIGINT REFERENCES audit.data_sources(source_id), + status TEXT NOT NULL, + started_at TIMESTAMPTZ NOT NULL DEFAULT now(), + finished_at TIMESTAMPTZ, + raw_uri TEXT, + row_count INTEGER, + error_message TEXT +); + +CREATE TABLE IF NOT EXISTS silver.areas ( + area_id TEXT PRIMARY KEY, + name TEXT NOT NULL, + district TEXT NOT NULL, + segment TEXT NOT NULL, + notes TEXT NOT NULL DEFAULT '', + created_at TIMESTAMPTZ NOT NULL DEFAULT now(), + updated_at TIMESTAMPTZ NOT NULL DEFAULT now() +); + +CREATE TABLE IF NOT EXISTS silver.area_monthly_metrics ( + area_id TEXT NOT NULL REFERENCES silver.areas(area_id), + month TEXT NOT NULL CHECK (month ~ '^[0-9]{4}-[0-9]{2}$'), + transaction_count INTEGER NOT NULL CHECK (transaction_count >= 0), + transaction_price_psm DOUBLE PRECISION NOT NULL CHECK (transaction_price_psm >= 0), + listing_count INTEGER NOT NULL CHECK (listing_count >= 0), + listing_price_psm DOUBLE PRECISION NOT NULL CHECK (listing_price_psm >= 0), + median_days_on_market DOUBLE PRECISION NOT NULL CHECK (median_days_on_market >= 0), + rent_price_psm DOUBLE PRECISION NOT NULL CHECK (rent_price_psm >= 0), + new_supply_units INTEGER NOT NULL CHECK (new_supply_units >= 0), + land_residential_gfa_sqm DOUBLE PRECISION NOT NULL CHECK (land_residential_gfa_sqm >= 0), + mortgage_rate_pct DOUBLE PRECISION NOT NULL CHECK (mortgage_rate_pct >= 0), + policy_signal INTEGER NOT NULL DEFAULT 0 CHECK (policy_signal BETWEEN -2 AND 2), + source TEXT NOT NULL DEFAULT 'manual', + created_at TIMESTAMPTZ NOT NULL DEFAULT now(), + updated_at TIMESTAMPTZ NOT NULL DEFAULT now(), + PRIMARY KEY (area_id, month) +); + +CREATE TABLE IF NOT EXISTS silver.neighborhoods ( + neighborhood_id TEXT PRIMARY KEY, + area_id TEXT NOT NULL REFERENCES silver.areas(area_id), + name TEXT NOT NULL, + built_year INTEGER, + property_type TEXT NOT NULL, + metro_distance_m INTEGER, + school_quality TEXT, + notes TEXT NOT NULL DEFAULT '', + created_at TIMESTAMPTZ NOT NULL DEFAULT now(), + updated_at TIMESTAMPTZ NOT NULL DEFAULT now() +); + +CREATE TABLE IF NOT EXISTS silver.neighborhood_monthly_metrics ( + neighborhood_id TEXT NOT NULL REFERENCES silver.neighborhoods(neighborhood_id), + month TEXT NOT NULL CHECK (month ~ '^[0-9]{4}-[0-9]{2}$'), + transaction_count INTEGER NOT NULL CHECK (transaction_count >= 0), + transaction_price_psm DOUBLE PRECISION NOT NULL CHECK (transaction_price_psm >= 0), + listing_count INTEGER NOT NULL CHECK (listing_count >= 0), + listing_price_psm DOUBLE PRECISION NOT NULL CHECK (listing_price_psm >= 0), + rent_price_psm DOUBLE PRECISION NOT NULL CHECK (rent_price_psm >= 0), + median_days_on_market DOUBLE PRECISION NOT NULL CHECK (median_days_on_market >= 0), + available_units INTEGER NOT NULL CHECK (available_units >= 0), + source TEXT NOT NULL DEFAULT 'manual', + created_at TIMESTAMPTZ NOT NULL DEFAULT now(), + updated_at TIMESTAMPTZ NOT NULL DEFAULT now(), + PRIMARY KEY (neighborhood_id, month) +); + +CREATE TABLE IF NOT EXISTS gold.area_scores ( + area_id TEXT NOT NULL REFERENCES silver.areas(area_id), + name TEXT NOT NULL, + district TEXT NOT NULL, + segment TEXT NOT NULL, + month TEXT NOT NULL CHECK (month ~ '^[0-9]{4}-[0-9]{2}$'), + investment_score DOUBLE PRECISION NOT NULL CHECK (investment_score BETWEEN 0 AND 100), + recommendation TEXT NOT NULL, + liquidity_score DOUBLE PRECISION NOT NULL CHECK (liquidity_score BETWEEN 0 AND 100), + momentum_score DOUBLE PRECISION NOT NULL CHECK (momentum_score BETWEEN 0 AND 100), + rent_support_score DOUBLE PRECISION NOT NULL CHECK (rent_support_score BETWEEN 0 AND 100), + safety_margin_score DOUBLE PRECISION NOT NULL CHECK (safety_margin_score BETWEEN 0 AND 100), + credit_support_score DOUBLE PRECISION NOT NULL CHECK (credit_support_score BETWEEN 0 AND 100), + supply_risk_score DOUBLE PRECISION NOT NULL CHECK (supply_risk_score BETWEEN 0 AND 100), + valuation_pressure_score DOUBLE PRECISION NOT NULL CHECK (valuation_pressure_score BETWEEN 0 AND 100), + transaction_count INTEGER NOT NULL CHECK (transaction_count >= 0), + transaction_price_psm DOUBLE PRECISION NOT NULL CHECK (transaction_price_psm >= 0), + listing_count INTEGER NOT NULL CHECK (listing_count >= 0), + listing_price_psm DOUBLE PRECISION NOT NULL CHECK (listing_price_psm >= 0), + rent_price_psm DOUBLE PRECISION NOT NULL CHECK (rent_price_psm >= 0), + median_days_on_market DOUBLE PRECISION NOT NULL CHECK (median_days_on_market >= 0), + annual_rent_yield_pct DOUBLE PRECISION NOT NULL CHECK (annual_rent_yield_pct >= 0), + listing_pressure_ratio DOUBLE PRECISION NOT NULL CHECK (listing_pressure_ratio >= 0), + discount_pct DOUBLE PRECISION NOT NULL, + price_momentum_pct DOUBLE PRECISION NOT NULL, + volume_momentum_pct DOUBLE PRECISION NOT NULL, + model_version TEXT NOT NULL DEFAULT 'sample-v1', + computed_at TIMESTAMPTZ NOT NULL DEFAULT now(), + PRIMARY KEY (area_id, month) +); + +CREATE TABLE IF NOT EXISTS app.watchlist_items ( + watchlist_item_id BIGSERIAL PRIMARY KEY, + neighborhood_id TEXT REFERENCES silver.neighborhoods(neighborhood_id), + area_id TEXT REFERENCES silver.areas(area_id), + target_price_psm DOUBLE PRECISION CHECK (target_price_psm >= 0), + status TEXT NOT NULL DEFAULT 'active', + notes TEXT NOT NULL DEFAULT '', + created_at TIMESTAMPTZ NOT NULL DEFAULT now(), + updated_at TIMESTAMPTZ NOT NULL DEFAULT now(), + CHECK (neighborhood_id IS NOT NULL OR area_id IS NOT NULL) +); + +CREATE INDEX IF NOT EXISTS idx_area_monthly_metrics_month ON silver.area_monthly_metrics(month); +CREATE INDEX IF NOT EXISTS idx_neighborhoods_area ON silver.neighborhoods(area_id); +CREATE INDEX IF NOT EXISTS idx_gold_area_scores_month ON gold.area_scores(month, investment_score DESC); +CREATE INDEX IF NOT EXISTS idx_watchlist_status ON app.watchlist_items(status); diff --git a/apps/api/migrations/202606180002_seed_sample.sql b/apps/api/migrations/202606180002_seed_sample.sql new file mode 100644 index 0000000..c5a7d5a --- /dev/null +++ b/apps/api/migrations/202606180002_seed_sample.sql @@ -0,0 +1,115 @@ +INSERT INTO audit.data_sources (name, source_type, url, cadence, reliability, notes) +VALUES + ('sample', 'manual', NULL, 'ad hoc', 'demo', 'Demonstration data. Not real market data.') +ON CONFLICT (name) DO UPDATE +SET source_type = EXCLUDED.source_type, + cadence = EXCLUDED.cadence, + reliability = EXCLUDED.reliability, + notes = EXCLUDED.notes; + +INSERT INTO silver.areas (area_id, name, district, segment, notes) +VALUES + ('qiantan', '前滩', '浦东新区', '核心改善', '产业和公共配套强，供应节奏需要持续跟踪'), + ('xujiahui', '徐家汇', '徐汇区', '核心成熟', '成熟商圈和教育医疗资源强，价格弹性通常较小'), + ('danning', '大宁', '静安区', '内中环改善', '居住氛围和商业配套较均衡'), + ('zhangjiang', '张江', '浦东新区', '产业成长', '产业人口支撑强，产品分化明显'), + ('hongqiao', '大虹桥', '闵行区', '规划成长', '受商务区兑现和供应影响较大') +ON CONFLICT (area_id) DO UPDATE +SET name = EXCLUDED.name, + district = EXCLUDED.district, + segment = EXCLUDED.segment, + notes = EXCLUDED.notes, + updated_at = now(); + +INSERT INTO silver.area_monthly_metrics ( + area_id, + month, + transaction_count, + transaction_price_psm, + listing_count, + listing_price_psm, + median_days_on_market, + rent_price_psm, + new_supply_units, + land_residential_gfa_sqm, + mortgage_rate_pct, + policy_signal, + source +) +VALUES + ('qiantan', '2026-05', 93, 122500, 380, 126500, 53, 192, 60, 0, 3.35, 1, 'sample'), + ('xujiahui', '2026-05', 67, 127500, 348, 133000, 66, 207, 20, 0, 3.35, 1, 'sample'), + ('danning', '2026-05', 103, 98200, 488, 102200, 69, 158, 110, 0, 3.35, 1, 'sample'), + ('zhangjiang', '2026-05', 126, 84600, 570, 89000, 60, 139, 180, 0, 3.35, 1, 'sample'), + ('hongqiao', '2026-05', 82, 75800, 735, 82000, 96, 119, 360, 0, 3.35, 0, 'sample') +ON CONFLICT (area_id, month) DO UPDATE +SET transaction_count = EXCLUDED.transaction_count, + transaction_price_psm = EXCLUDED.transaction_price_psm, + listing_count = EXCLUDED.listing_count, + listing_price_psm = EXCLUDED.listing_price_psm, + median_days_on_market = EXCLUDED.median_days_on_market, + rent_price_psm = EXCLUDED.rent_price_psm, + new_supply_units = EXCLUDED.new_supply_units, + land_residential_gfa_sqm = EXCLUDED.land_residential_gfa_sqm, + mortgage_rate_pct = EXCLUDED.mortgage_rate_pct, + policy_signal = EXCLUDED.policy_signal, + source = EXCLUDED.source, + updated_at = now(); + +INSERT INTO gold.area_scores ( + area_id, + name, + district, + segment, + month, + investment_score, + recommendation, + liquidity_score, + momentum_score, + rent_support_score, + safety_margin_score, + credit_support_score, + supply_risk_score, + valuation_pressure_score, + transaction_count, + transaction_price_psm, + listing_count, + listing_price_psm, + rent_price_psm, + median_days_on_market, + annual_rent_yield_pct, + listing_pressure_ratio, + discount_pct, + price_momentum_pct, + volume_momentum_pct, + model_version +) +VALUES + ('zhangjiang', '张江', '浦东新区', '产业成长', '2026-05', 72.0, '观察池', 93.0, 60.5, 69.4, 41.6, 66.7, 21.8, 62.9, 126, 84600, 570, 89000, 139, 60, 1.97, 4.52, 4.94, 1.32, 6.78, 'sample-v1'), + ('qiantan', '前滩', '浦东新区', '核心改善', '2026-05', 66.4, '观察池', 79.2, 61.6, 62.9, 25.4, 66.7, 8.1, 78.6, 93, 122500, 380, 126500, 192, 53, 1.88, 4.09, 3.16, 1.24, 8.14, 'sample-v1'), + ('danning', '大宁', '静安区', '内中环改善', '2026-05', 65.6, '观察池', 76.9, 57.9, 66.5, 32.4, 66.7, 20.0, 72.8, 103, 98200, 488, 102200, 158, 69, 1.93, 4.74, 3.91, 1.24, 5.10, 'sample-v1'), + ('xujiahui', '徐家汇', '徐汇区', '核心成熟', '2026-05', 61.9, '中性观望', 59.8, 57.1, 67.7, 34.5, 66.7, 18.5, 70.2, 67, 127500, 348, 133000, 207, 66, 1.95, 5.19, 4.14, 0.55, 8.06, 'sample-v1'), + ('hongqiao', '大虹桥', '闵行区', '规划成长', '2026-05', 55.2, '中性观望', 53.5, 50.9, 63.1, 73.4, 59.2, 99.6, 24.6, 82, 75800, 735, 82000, 119, 96, 1.88, 8.96, 7.56, 0.40, 3.80, 'sample-v1') +ON CONFLICT (area_id, month) DO UPDATE +SET investment_score = EXCLUDED.investment_score, + recommendation = EXCLUDED.recommendation, + liquidity_score = EXCLUDED.liquidity_score, + momentum_score = EXCLUDED.momentum_score, + rent_support_score = EXCLUDED.rent_support_score, + safety_margin_score = EXCLUDED.safety_margin_score, + credit_support_score = EXCLUDED.credit_support_score, + supply_risk_score = EXCLUDED.supply_risk_score, + valuation_pressure_score = EXCLUDED.valuation_pressure_score, + transaction_count = EXCLUDED.transaction_count, + transaction_price_psm = EXCLUDED.transaction_price_psm, + listing_count = EXCLUDED.listing_count, + listing_price_psm = EXCLUDED.listing_price_psm, + rent_price_psm = EXCLUDED.rent_price_psm, + median_days_on_market = EXCLUDED.median_days_on_market, + annual_rent_yield_pct = EXCLUDED.annual_rent_yield_pct, + listing_pressure_ratio = EXCLUDED.listing_pressure_ratio, + discount_pct = EXCLUDED.discount_pct, + price_momentum_pct = EXCLUDED.price_momentum_pct, + volume_momentum_pct = EXCLUDED.volume_momentum_pct, + model_version = EXCLUDED.model_version, + computed_at = now(); diff --git a/apps/api/src/config.rs b/apps/api/src/config.rs new file mode 100644 index 0000000..4774230 --- /dev/null +++ b/apps/api/src/config.rs @@ -0,0 +1,39 @@ +use std::env; +use std::net::SocketAddr; + +use anyhow::{Context, Result}; + +#[derive(Debug, Clone)] +pub struct AppConfig { + pub database_url: String, + pub host: String, + pub port: u16, + pub run_migrations: bool, +} + +impl AppConfig { + pub fn from_env() -> Result { + let database_url = env::var("DATABASE_URL").context("DATABASE_URL is required")?; + let host = env::var("API_HOST").unwrap_or_else(|_| "127.0.0.1".to_string()); + let port = env::var("API_PORT") + .unwrap_or_else(|_| "8080".to_string()) + .parse::() + .context("API_PORT must be a valid port")?; + let run_migrations = env::var("RUN_MIGRATIONS") + .map(|value| matches!(value.as_str(), "1" | "true" | "TRUE" | "yes" | "YES")) + .unwrap_or(false); + + Ok(Self { + database_url, + host, + port, + run_migrations, + }) + } + + pub fn bind_addr(&self) -> SocketAddr { + format!("{}:{}", self.host, self.port) + .parse() + .expect("validated API host and port") + } +} diff --git a/apps/api/src/db.rs b/apps/api/src/db.rs new file mode 100644 index 0000000..447a216 --- /dev/null +++ b/apps/api/src/db.rs @@ -0,0 +1,13 @@ +use anyhow::{Context, Result}; +use sqlx::postgres::PgPoolOptions; +use sqlx::PgPool; +use std::time::Duration; + +pub async fn create_pool(database_url: &str) -> Result { + PgPoolOptions::new() + .max_connections(8) + .acquire_timeout(Duration::from_secs(5)) + .connect(database_url) + .await + .context("failed to connect to PostgreSQL") +} diff --git a/apps/api/src/error.rs b/apps/api/src/error.rs new file mode 100644 index 0000000..091ac2d --- /dev/null +++ b/apps/api/src/error.rs @@ -0,0 +1,33 @@ +use axum::http::StatusCode; +use axum::response::{IntoResponse, Response}; +use axum::Json; +use serde::Serialize; +use thiserror::Error; + +#[derive(Debug, Error)] +pub enum ApiError { + #[error("bad request: {0}")] + BadRequest(String), + #[error("database error")] + Database(#[from] sqlx::Error), +} + +#[derive(Debug, Serialize)] +struct ErrorBody { + error: String, +} + +impl IntoResponse for ApiError { + fn into_response(self) -> Response { + let status = match self { + ApiError::BadRequest(_) => StatusCode::BAD_REQUEST, + ApiError::Database(_) => StatusCode::INTERNAL_SERVER_ERROR, + }; + let body = Json(ErrorBody { + error: self.to_string(), + }); + (status, body).into_response() + } +} + +pub type ApiResult = Result; diff --git a/apps/api/src/handlers.rs b/apps/api/src/handlers.rs new file mode 100644 index 0000000..ffb4ae5 --- /dev/null +++ b/apps/api/src/handlers.rs @@ -0,0 +1,151 @@ +use axum::extract::{Query, State}; +use axum::Json; +use serde::Serialize; + +use crate::error::{ApiError, ApiResult}; +use crate::models::{AreaScore, AreaScoreSummary, MarketOverview, MonthQuery}; +use crate::state::AppState; + +#[derive(Debug, Serialize)] +pub struct HealthResponse { + pub status: &'static str, + pub service: &'static str, +} + +pub async fn health() -> Json { + Json(HealthResponse { + status: "ok", + service: "shanghai-housing-api", + }) +} + +pub async fn ready(State(state): State) -> ApiResult> { + sqlx::query("select 1").execute(&state.pool).await?; + Ok(Json(HealthResponse { + status: "ok", + service: "shanghai-housing-api", + })) +} + +pub async fn list_area_scores( + State(state): State, + Query(query): Query, +) -> ApiResult>> { + validate_month(&query.month)?; + let scores = fetch_area_scores(&state, &query.month).await?; + Ok(Json(scores)) +} + +pub async fn market_overview( + State(state): State, + Query(query): Query, +) -> ApiResult> { + validate_month(&query.month)?; + let scores = fetch_area_scores(&state, &query.month).await?; + let area_count = scores.len(); + let average_investment_score = average(scores.iter().map(|score| score.investment_score)); + let average_rent_yield_pct = average(scores.iter().map(|score| score.annual_rent_yield_pct)); + let top_area = scores.first().map(AreaScoreSummary::from); + let weakest_area = scores.last().map(AreaScoreSummary::from); + let highest_supply_risk_area = scores + .iter() + .max_by(|left, right| { + left.supply_risk_score + .partial_cmp(&right.supply_risk_score) + .unwrap_or(std::cmp::Ordering::Equal) + }) + .map(AreaScoreSummary::from); + + Ok(Json(MarketOverview { + month: query.month, + area_count, + average_investment_score, + average_rent_yield_pct, + top_area, + weakest_area, + highest_supply_risk_area, + })) +} + +async fn fetch_area_scores(state: &AppState, month: &str) -> Result, sqlx::Error> { + sqlx::query_as::<_, AreaScore>( + r#" + SELECT + area_id, + name, + district, + segment, + month, + investment_score, + recommendation, + liquidity_score, + momentum_score, + rent_support_score, + safety_margin_score, + credit_support_score, + supply_risk_score, + valuation_pressure_score, + transaction_count, + transaction_price_psm, + listing_count, + listing_price_psm, + rent_price_psm, + median_days_on_market, + annual_rent_yield_pct, + listing_pressure_ratio, + discount_pct, + price_momentum_pct, + volume_momentum_pct + FROM gold.area_scores + WHERE month = $1 + ORDER BY investment_score DESC, area_id ASC + "#, + ) + .bind(month) + .fetch_all(&state.pool) + .await +} + +fn average(values: impl Iterator) -> f64 { + let (sum, count) = values.fold((0.0, 0usize), |(sum, count), value| { + (sum + value, count + 1) + }); + if count == 0 { + 0.0 + } else { + (sum / count as f64 * 10.0).round() / 10.0 + } +} + +fn validate_month(month: &str) -> ApiResult<()> { + let valid = month.len() == 7 + && month.as_bytes()[4] == b'-' + && month[..4].chars().all(|item| item.is_ascii_digit()) + && month[5..].chars().all(|item| item.is_ascii_digit()) + && matches!(month[5..].parse::(), Ok(value) if (1..=12).contains(&value)); + + if valid { + Ok(()) + } else { + Err(ApiError::BadRequest( + "month must use YYYY-MM format".to_string(), + )) + } +} + +#[cfg(test)] +mod tests { + use super::validate_month; + + #[test] + fn accepts_valid_month() { + assert!(validate_month("2026-05").is_ok()); + } + + #[test] + fn rejects_invalid_month() { + assert!(validate_month("2026-5").is_err()); + assert!(validate_month("2026-13").is_err()); + assert!(validate_month("abcd-05").is_err()); + } +} diff --git a/apps/api/src/main.rs b/apps/api/src/main.rs new file mode 100644 index 0000000..597bf0a --- /dev/null +++ b/apps/api/src/main.rs @@ -0,0 +1,75 @@ +mod config; +mod db; +mod error; +mod handlers; +mod models; +mod routes; +mod state; + +use anyhow::Context; +use tokio::net::TcpListener; +use tracing::info; + +use crate::config::AppConfig; +use crate::db::create_pool; +use crate::routes::build_router; +use crate::state::AppState; + +#[tokio::main] +async fn main() -> anyhow::Result<()> { + tracing_subscriber::fmt() + .with_env_filter( + tracing_subscriber::EnvFilter::try_from_default_env() + .unwrap_or_else(|_| "shanghai_housing_api=info,tower_http=info".into()), + ) + .init(); + + let config = AppConfig::from_env()?; + let pool = create_pool(&config.database_url).await?; + + if config.run_migrations { + info!("running database migrations"); + sqlx::migrate!("./migrations") + .run(&pool) + .await + .context("failed to run database migrations")?; + } + + let state = AppState { pool }; + let app = build_router(state); + let listener = TcpListener::bind(config.bind_addr()) + .await + .context("failed to bind API listener")?; + + info!("listening on http://{}", listener.local_addr()?); + axum::serve(listener, app) + .with_graceful_shutdown(shutdown_signal()) + .await + .context("server failed")?; + + Ok(()) +} + +async fn shutdown_signal() { + let ctrl_c = async { + tokio::signal::ctrl_c() + .await + .expect("failed to install Ctrl+C handler"); + }; + + #[cfg(unix)] + let terminate = async { + tokio::signal::unix::signal(tokio::signal::unix::SignalKind::terminate()) + .expect("failed to install signal handler") + .recv() + .await; + }; + + #[cfg(not(unix))] + let terminate = std::future::pending::<()>(); + + tokio::select! { + _ = ctrl_c => {}, + _ = terminate => {}, + } +} diff --git a/apps/api/src/models.rs b/apps/api/src/models.rs new file mode 100644 index 0000000..ca849d0 --- /dev/null +++ b/apps/api/src/models.rs @@ -0,0 +1,68 @@ +use serde::{Deserialize, Serialize}; +use sqlx::FromRow; + +#[derive(Debug, Deserialize)] +pub struct MonthQuery { + pub month: String, +} + +#[derive(Debug, Clone, Serialize, FromRow)] +pub struct AreaScore { + pub area_id: String, + pub name: String, + pub district: String, + pub segment: String, + pub month: String, + pub investment_score: f64, + pub recommendation: String, + pub liquidity_score: f64, + pub momentum_score: f64, + pub rent_support_score: f64, + pub safety_margin_score: f64, + pub credit_support_score: f64, + pub supply_risk_score: f64, + pub valuation_pressure_score: f64, + pub transaction_count: i32, + pub transaction_price_psm: f64, + pub listing_count: i32, + pub listing_price_psm: f64, + pub rent_price_psm: f64, + pub median_days_on_market: f64, + pub annual_rent_yield_pct: f64, + pub listing_pressure_ratio: f64, + pub discount_pct: f64, + pub price_momentum_pct: f64, + pub volume_momentum_pct: f64, +} + +#[derive(Debug, Serialize)] +pub struct AreaScoreSummary { + pub area_id: String, + pub name: String, + pub district: String, + pub investment_score: f64, + pub recommendation: String, +} + +impl From<&AreaScore> for AreaScoreSummary { + fn from(score: &AreaScore) -> Self { + Self { + area_id: score.area_id.clone(), + name: score.name.clone(), + district: score.district.clone(), + investment_score: score.investment_score, + recommendation: score.recommendation.clone(), + } + } +} + +#[derive(Debug, Serialize)] +pub struct MarketOverview { + pub month: String, + pub area_count: usize, + pub average_investment_score: f64, + pub average_rent_yield_pct: f64, + pub top_area: Option, + pub weakest_area: Option, + pub highest_supply_risk_area: Option, +} diff --git a/apps/api/src/routes.rs b/apps/api/src/routes.rs new file mode 100644 index 0000000..3791ce9 --- /dev/null +++ b/apps/api/src/routes.rs @@ -0,0 +1,22 @@ +use axum::routing::get; +use axum::Router; +use tower_http::cors::CorsLayer; +use tower_http::trace::TraceLayer; + +use crate::handlers::{health, list_area_scores, market_overview, ready}; +use crate::state::AppState; + +pub fn build_router(state: AppState) -> Router { + Router::new() + .route("/health", get(health)) + .route("/ready", get(ready)) + .nest( + "/api/v1", + Router::new() + .route("/areas/scores", get(list_area_scores)) + .route("/market/overview", get(market_overview)), + ) + .layer(TraceLayer::new_for_http()) + .layer(CorsLayer::permissive()) + .with_state(state) +} diff --git a/apps/api/src/state.rs b/apps/api/src/state.rs new file mode 100644 index 0000000..330747b --- /dev/null +++ b/apps/api/src/state.rs @@ -0,0 +1,6 @@ +use sqlx::PgPool; + +#[derive(Clone)] +pub struct AppState { + pub pool: PgPool, +} diff --git a/config/source_catalog.json b/config/source_catalog.json new file mode 100644 index 0000000..ba624ec --- /dev/null +++ b/config/source_catalog.json @@ -0,0 +1,50 @@ +{ + "official_sources": [ + { + "name": "上海市统计局", + "url": "https://tjj.sh.gov.cn/", + "cadence": "monthly", + "use": "房地产开发投资、商品房销售、宏观人口与收入数据", + "reliability": "high" + }, + { + "name": "上海市房屋管理局", + "url": "https://fgj.sh.gov.cn/", + "cadence": "event/monthly", + "use": "房产交易、政策通知、市场监管信息", + "reliability": "high" + }, + { + "name": "上海土地市场", + "url": "https://biz.ghzyj.sh.gov.cn/shtdsc/wz/tdjy/index.jhtml", + "cadence": "event", + "use": "住宅用地公告、成交、楼面价、溢价率", + "reliability": "high" + }, + { + "name": "中国货币网 LPR", + "url": "https://www.chinamoney.com.cn/chinese/bklpr/", + "cadence": "monthly", + "use": "贷款市场报价利率，尤其是五年期以上 LPR", + "reliability": "high" + } + ], + "market_sources": [ + { + "name": "中介挂牌与成交数据", + "url": null, + "cadence": "daily/weekly", + "use": "挂牌量、挂牌价、成交周期、议价空间、小区微观样本", + "reliability": "medium", + "note": "需要记录采集时间、口径、去重规则和合规边界。" + }, + { + "name": "租赁平台样本", + "url": null, + "cadence": "weekly/monthly", + "use": "租金、空置压力、租售比、板块居住需求", + "reliability": "medium", + "note": "挂牌租金不等于成交租金，应单独标注。" + } + ] +} diff --git a/data/sample/area_monthly_metrics.csv b/data/sample/area_monthly_metrics.csv new file mode 100644 index 0000000..4b60ef2 --- /dev/null +++ b/data/sample/area_monthly_metrics.csv @@ -0,0 +1,16 @@ +area_id,month,transaction_count,transaction_price_psm,listing_count,listing_price_psm,median_days_on_market,rent_price_psm,new_supply_units,land_residential_gfa_sqm,mortgage_rate_pct,policy_signal,source +qiantan,2026-03,78,119000,410,125000,62,188,120,0,3.45,1,sample +qiantan,2026-04,86,121000,398,126000,58,190,80,0,3.40,1,sample +qiantan,2026-05,93,122500,380,126500,53,192,60,0,3.35,1,sample +xujiahui,2026-03,64,126000,355,132000,68,205,30,0,3.45,1,sample +xujiahui,2026-04,62,126800,360,132500,70,206,20,0,3.40,1,sample +xujiahui,2026-05,67,127500,348,133000,66,207,20,0,3.35,1,sample +danning,2026-03,91,96000,520,101000,76,155,180,12000,3.45,1,sample +danning,2026-04,98,97000,505,101500,72,156,130,0,3.40,1,sample +danning,2026-05,103,98200,488,102200,69,158,110,0,3.35,1,sample +zhangjiang,2026-03,105,82000,610,87500,70,135,260,36000,3.45,1,sample +zhangjiang,2026-04,118,83500,590,88200,64,137,220,0,3.40,1,sample +zhangjiang,2026-05,126,84600,570,89000,60,139,180,0,3.35,1,sample +hongqiao,2026-03,83,76000,690,82000,88,118,420,68000,3.45,0,sample +hongqiao,2026-04,79,75500,720,81800,94,118,390,0,3.40,0,sample +hongqiao,2026-05,82,75800,735,82000,96,119,360,0,3.35,0,sample diff --git a/data/sample/areas.csv b/data/sample/areas.csv new file mode 100644 index 0000000..9e306f2 --- /dev/null +++ b/data/sample/areas.csv @@ -0,0 +1,6 @@ +area_id,name,district,segment,notes +qiantan,前滩,浦东新区,核心改善,产业和公共配套强，供应节奏需要持续跟踪 +xujiahui,徐家汇,徐汇区,核心成熟,成熟商圈和教育医疗资源强，价格弹性通常较小 +danning,大宁,静安区,内中环改善,居住氛围和商业配套较均衡 +zhangjiang,张江,浦东新区,产业成长,产业人口支撑强，产品分化明显 +hongqiao,大虹桥,闵行区,规划成长,受商务区兑现和供应影响较大 diff --git a/data/sample/neighborhood_monthly_metrics.csv b/data/sample/neighborhood_monthly_metrics.csv new file mode 100644 index 0000000..5ad0b59 --- /dev/null +++ b/data/sample/neighborhood_monthly_metrics.csv @@ -0,0 +1,6 @@ +neighborhood_id,month,transaction_count,transaction_price_psm,listing_count,listing_price_psm,rent_price_psm,median_days_on_market,available_units,source +qiantan_a,2026-05,12,124000,38,128000,195,50,38,sample +xujiahui_a,2026-05,8,131000,30,137000,215,62,30,sample +danning_a,2026-05,14,100000,45,104000,162,65,45,sample +zhangjiang_a,2026-05,18,86000,58,90500,143,58,58,sample +hongqiao_a,2026-05,9,77000,76,83500,121,94,76,sample diff --git a/data/sample/neighborhoods.csv b/data/sample/neighborhoods.csv new file mode 100644 index 0000000..197e46e --- /dev/null +++ b/data/sample/neighborhoods.csv @@ -0,0 +1,6 @@ +neighborhood_id,area_id,name,built_year,property_type,metro_distance_m,school_quality,notes +qiantan_a,qiantan,前滩样例小区A,2018,商品住宅,450,good,演示样例 +xujiahui_a,xujiahui,徐家汇样例小区A,2008,商品住宅,300,excellent,演示样例 +danning_a,danning,大宁样例小区A,2015,商品住宅,600,good,演示样例 +zhangjiang_a,zhangjiang,张江样例小区A,2016,商品住宅,700,normal,演示样例 +hongqiao_a,hongqiao,大虹桥样例小区A,2020,商品住宅,900,normal,演示样例 diff --git a/docs/config/environment.md b/docs/config/environment.md new file mode 100644 index 0000000..60b48a1 --- /dev/null +++ b/docs/config/environment.md @@ -0,0 +1,26 @@ +# 环境配置 + +不要把真实数据库密码写入仓库。开发时在当前 shell 中设置环境变量。 + +## API + +```bash +export DATABASE_URL="postgres://:@:/shanghai_housing_research_dev" +export API_HOST="127.0.0.1" +export API_PORT="8080" +export RUN_MIGRATIONS="true" +``` + +`RUN_MIGRATIONS=true` 会在 API 启动时运行 `apps/api/migrations` 中的 SQLx migrations。生产环境建议由单独的 migration job 执行。 + +## 数据库命名 + +建议使用： + +```text +shanghai_housing_research_dev +shanghai_housing_research_test +shanghai_housing_research +``` + +管理员账户只用于 bootstrap。应用运行建议使用专用低权限账户。 diff --git a/docs/data_dictionary.md b/docs/data_dictionary.md new file mode 100644 index 0000000..0132b62 --- /dev/null +++ b/docs/data_dictionary.md @@ -0,0 +1,40 @@ +# 数据字典 + +## areas + +板块基础信息。 + +| 字段 | 含义 | +| --- | --- | +| area_id | 板块唯一 ID | +| name | 板块名称 | +| district | 行政区 | +| segment | 板块类型 | +| notes | 备注 | + +## area_monthly_metrics + +板块月度指标。 + +| 字段 | 含义 | +| --- | --- | +| transaction_count | 二手或样本成交套数 | +| transaction_price_psm | 成交均价，元/平方米 | +| listing_count | 挂牌套数 | +| listing_price_psm | 挂牌均价，元/平方米 | +| median_days_on_market | 中位成交周期，天 | +| rent_price_psm | 月租金，元/平方米/月 | +| new_supply_units | 新增供应套数 | +| land_residential_gfa_sqm | 住宅土地建筑面积，平方米 | +| mortgage_rate_pct | 主流按揭利率 | +| policy_signal | 政策方向，-2 到 2 | + +## 派生指标 + +| 指标 | 公式 | +| --- | --- | +| 年化租金收益率 | 月租金 / 成交价 * 12 | +| 去化压力 | 挂牌套数 / 成交套数 | +| 成交折价 | (挂牌均价 - 成交均价) / 挂牌均价 | +| 价格动量 | 当月成交均价相对上月变化 | +| 成交动量 | 当月成交套数相对上月变化 | diff --git a/docs/research_framework.md b/docs/research_framework.md new file mode 100644 index 0000000..00bd3da --- /dev/null +++ b/docs/research_framework.md @@ -0,0 +1,56 @@ +# 上海房市投资研究框架 + +## 基本原则 + +长期研究的核心不是寻找一个万能预测因子，而是建立稳定、可审计、可复盘的判断系统。 + +第一版框架采用四个判断层次： + +1. 市场周期：成交量、成交周期、挂牌压力、信贷环境。 +2. 板块质量：产业、交通、公共资源、供给约束、租赁需求。 +3. 资产质量：小区楼龄、物业、产品稀缺性、流动性、硬伤。 +4. 交易安全边际：成交/挂牌折价、历史价格分位、租金收益率、替代品价格。 + +## 投资判断问题 + +系统应持续回答以下问题： + +- 上海整体处于放量、缩量、企稳、补跌还是结构性修复？ +- 哪些板块成交量改善领先于价格？ +- 哪些板块挂牌库存持续堆积？ +- 租金收益率是否能支撑当前价格？ +- 新房供应是否压制二手房议价能力？ +- 政策和利率变化对改善、刚需、豪宅的影响是否不同？ +- 哪些小区流动性足够好，能承担投资退出需求？ +- 当前合理买入价相对挂牌价需要多少折价？ + +## 模型路线 + +第一阶段使用可解释评分： + +- 流动性评分 +- 动量评分 +- 租金支撑评分 +- 安全边际评分 +- 信贷政策评分 +- 供应风险评分 + +第二阶段再加入： + +- 历史分位模型 +- 异常成交识别 +- 小区相似资产比较 +- 板块领先/滞后关系 +- 利率和政策情景推演 + +## 决策输出 + +最终输出应是具体动作，而不是抽象观点： + +- 重点研究 +- 观察池 +- 中性观望 +- 谨慎等待 +- 合理买入价区间 +- 触发买入条件 +- 触发退出条件 diff --git a/docs/system_architecture_and_development_plan.md b/docs/system_architecture_and_development_plan.md new file mode 100644 index 0000000..05dec77 --- /dev/null +++ b/docs/system_architecture_and_development_plan.md @@ -0,0 +1,543 @@ +# 上海房市投资研究系统架构与开发计划 + +## 0. 当前状态 + +当前项目是一个纯 Python 的研究内核 MVP： + +- SQLite 存储 +- 样例数据 +- 板块指标计算 +- Markdown 月报生成 +- 命令行入口 + +它的价值是验证研究口径和指标框架。它不是最终系统形态。后续应演进为一个具备数据采集、资产库、模型、API、前端看板、报告和预警能力的长期研究平台。 + +## 1. 系统定位 + +目标是搭建一个面向上海房市长期投资判断的研究操作系统。系统最终要回答： + +- 市场处在什么周期位置？ +- 哪些板块值得重点跟踪？ +- 哪些小区具有流动性、租金和稀缺性支撑？ +- 合理买入价区间是多少？ +- 什么信号触发买入、观望、卖出或换仓？ +- 判断依据是否可复盘、可解释、可更新？ + +系统不追求单点预测“下月涨跌”，而追求持续形成稳定的投资判断流程。 + +## 2. 总体架构 + +```mermaid +flowchart LR + A["数据源
官方/市场/手工/文档"] --> B["采集层
Ingestion"] + B --> C["原始数据层
Raw Lake"] + C --> D["标准化层
Normalized Store"] + D --> E["特征与指标层
Feature Mart"] + E --> F["模型层
Scoring/Forecast/Scenario"] + F --> G["API 层
Rust Axum"] + G --> H["前端
React + TypeScript + shadcn/ui"] + F --> I["报告与预警
周报/月报/观察池"] + D --> J["研究工作台
Notebook/CLI"] +``` + +## 3. 推荐技术路线 + +### 3.1 推荐结论 + +长期看，推荐采用混合架构： + +- 前端：TypeScript + React + shadcn/ui +- API 后端：Rust Axum + SQLx +- 分析与模型服务：Python，必要时引入 Rust 数据处理组件 +- 主数据库：PostgreSQL + PostGIS +- 本地分析引擎：DuckDB + Parquet +- 数据处理：Python Polars 起步，Rust Polars/DataFusion 用于稳定高性能任务 +- 后台任务：先用轻量 cron/脚本，后续升级 Prefect 或 Airflow + +本项目按长期平台建设，因此不采用 FastAPI 过渡。API 层从第一版就采用 Rust Axum，Python 保留为分析、建模、数据实验和离线任务服务。 + +### 3.2 为什么不是纯 Python + +纯 Python 很适合研究、建模和快速迭代，但当系统开始承载更多前端交互、权限、并发查询、任务调度和长期服务稳定性时，单体 Python 项目会逐渐变重。 + +更成熟的做法是： + +- Python 专注数据、模型、研究逻辑。 +- Rust API 专注服务边界、权限、查询、任务编排。 +- 前端专注研究工作台和投资决策界面。 + +### 3.3 为什么不是纯 Rust + +Rust 很适合高可靠服务和性能敏感 API，但房市研究最重的工作通常不是 API 性能，而是数据清洗、特征工程、统计建模、回测、报告解释和探索式分析。这些领域 Python 生态明显更成熟。 + +因此不建议把模型和研究逻辑全部用 Rust 重写。那会降低迭代速度。 + +## 4. Rust 后端 vs Python 后端 + +| 维度 | Rust 后端 | Python 后端 | +| --- | --- | --- | +| 性能 | 很强，适合高并发、低延迟、重查询网关 | 足够支撑早期和中等规模内部系统 | +| 稳定性 | 编译期约束强，运行期类型错误少 | 依赖测试和类型检查，长期维护需更强纪律 | +| 开发速度 | 初期慢，类型和生命周期成本更高 | 很快，尤其适合快速验证业务逻辑 | +| 数据科学生态 | 可用但不如 Python 丰富 | 极强，Polars、pandas、statsmodels、sklearn 等成熟 | +| API 工程 | Axum + SQLx 很稳，适合长期服务 | FastAPI 开发快，OpenAPI 友好 | +| 团队门槛 | 较高 | 较低 | +| 部署 | 单二进制优势明显 | 依赖环境和包管理，容器化后可控 | +| 与模型集成 | 通常需要调用 Python 服务或共享数据库 | 原生集成最顺 | +| 推荐阶段 | 从第一版开始承担主 API、权限、查询网关 | 只保留为模型服务、数据任务、研究实验 | + +推荐策略： + +1. 第一阶段：Rust Axum 建立主 API 和服务边界。 +2. Python 保持研究内核、数据处理、模型和报告生成。 +3. 前后端通过 OpenAPI 或共享 schema 对齐契约。 +4. 稳定、重复、高性能的数据处理任务可逐步迁入 Rust Polars/DataFusion。 + +本项目采用 Rust API + Python analytics 双服务作为默认路线。 + +## 4.1 Rust 数据处理 vs Python 数据处理 + +Rust 的数据处理能力并不弱。Polars 有 Rust 版本，DataFusion 本身就是基于 Rust 和 Apache Arrow 的高性能查询引擎，适合构建稳定、高性能、可嵌入的数据系统。 + +但对本系统来说，Python 仍然在以下方面更强： + +- 探索式分析更快，交互式 Notebook、临时统计、画图、验证假设都更顺手。 +- 统计建模和机器学习生态更完整，尤其是回归、时间序列、聚类、解释性分析、模型评估。 +- 采集、清洗、Excel/CSV、报告、可视化周边库更丰富。 +- 研究逻辑经常变化，Python 的迭代成本低。 + +Rust 更适合： + +- API 查询层和权限层。 +- 稳定的数据校验、标准化和导入管线。 +- 大批量 CSV/Parquet 扫描、聚合、特征预计算。 +- 需要长期运行、低内存占用、高并发的服务。 +- 已经沉淀稳定的数据处理逻辑。 + +因此判断不是“Rust 弱”，而是“Rust 更工程化，Python 更研究化”。本系统的最佳分工是： + +```text +Rust：API、数据库访问、权限、稳定导入、稳定特征计算、高性能查询 +Python：探索分析、模型实验、统计预测、报告解释、策略研究 +``` + +## 5. 前端方案 + +你的偏好是 TypeScript + React + shadcn/ui，这个方向适合本系统。 + +推荐栈： + +- Next.js App Router 或 Vite + React +- TypeScript +- shadcn/ui +- Tailwind CSS +- TanStack Query +- Recharts 或 ECharts +- MapLibre GL，用于地图和板块空间分析 +- Zod，用于前端数据校验 + +选择建议： + +- 如果要做完整产品、登录、路由、服务端渲染、报告页分享，选 Next.js。 +- 如果只是本地研究工作台和内部 SPA，选 Vite + React 更轻。 + +我建议采用 Next.js，因为后续报告页、板块详情页、观察池和权限体系会自然变复杂。 + +## 6. 前端信息架构 + +第一版前端应直接进入研究工作台，不做营销式首页。 + +核心页面： + +1. 市场总览 + - 上海整体成交、挂牌、库存、租金、利率、政策事件 + - 周期状态判断 + - 关键风险提示 + +2. 板块地图 + - 按综合评分、供应压力、租售比、价格动量着色 + - 支持行政区、环线、地铁、产业标签筛选 + +3. 板块详情 + - 成交/挂牌/租金走势 + - 新房供应与土地成交 + - 小区排行 + - 相似板块对比 + +4. 小区观察池 + - 自定义关注小区 + - 合理买入价 + - 挂牌变化 + - 成交样本 + - 风险标签 + +5. 策略与情景 + - 利率变化情景 + - 政策松紧情景 + - 收入/租金/供应变化情景 + +6. 报告中心 + - 周报 + - 月报 + - 板块专题 + - 投资备忘录 + +## 7. 后端模块设计 + +### 7.1 API 模块 + +职责： + +- 用户、权限、配置 +- 板块、小区、指标查询 +- 观察池管理 +- 报告查询 +- 模型结果查询 +- 任务触发和状态查询 + +典型接口： + +- `GET /api/market/overview?month=YYYY-MM` +- `GET /api/areas` +- `GET /api/areas/{area_id}/scores` +- `GET /api/areas/{area_id}/metrics` +- `GET /api/neighborhoods/{id}` +- `POST /api/watchlist` +- `GET /api/reports/monthly/{month}` +- `POST /api/jobs/ingest` + +### 7.2 Analytics 模块 + +职责： + +- 数据清洗 +- 特征生成 +- 板块评分 +- 小区评分 +- 异常检测 +- 情景推演 +- 报告生成 + +当前 Python 代码应逐步演进为这个模块。 + +### 7.3 Ingestion 模块 + +职责： + +- 官方数据导入 +- 市场数据导入 +- 手工 CSV/Excel 导入 +- 数据源版本记录 +- 失败重试 +- 采集日志 + +所有采集结果必须保留原始文件或原始响应，不直接覆盖。 + +### 7.4 Report 模块 + +职责： + +- 周报/月报 +- 板块专题 +- 小区备忘录 +- 组合观察报告 + +报告不应只是文本，应保留结构化结论，方便前端二次展示。 + +## 8. 数据架构 + +### 8.1 数据分层 + +```text +raw 原始数据，保留来源、采集时间、文件哈希 +bronze 初步解析，字段类型基本清理 +silver 标准化实体，统一板块、小区、月份、口径 +gold 指标、特征、模型输出、报告结论 +``` + +### 8.2 核心实体 + +- `areas`：板块 +- `districts`：行政区 +- `neighborhoods`：小区 +- `transactions`：成交样本 +- `listings`：挂牌样本 +- `rents`：租赁样本 +- `new_projects`：新房项目 +- `land_sales`：土地成交 +- `policy_events`：政策事件 +- `rates`：利率 +- `area_monthly_features`：板块月度特征 +- `neighborhood_monthly_features`：小区月度特征 +- `model_runs`：模型运行版本 +- `watchlists`：观察池 +- `reports`：报告 + +### 8.3 数据治理要求 + +每条关键数据至少记录： + +- 来源 +- 采集时间 +- 原始字段 +- 清洗规则版本 +- 是否估算 +- 是否人工修正 +- 置信度 + +这是系统长期可信的核心。 + +## 9. 模型体系 + +### 9.1 第一阶段：可解释评分 + +- 流动性评分 +- 价格动量评分 +- 成交动量评分 +- 租金支撑评分 +- 安全边际评分 +- 供应风险评分 +- 政策/信贷环境评分 + +输出： + +- 重点研究 +- 观察池 +- 中性观望 +- 谨慎等待 + +### 9.2 第二阶段：相对估值 + +- 同板块小区比较 +- 相似小区比较 +- 同总价段比较 +- 同楼龄/地铁距离/物业类型比较 +- 历史价格分位 + +输出： + +- 合理买入价区间 +- 溢价/折价解释 +- 替代标的推荐 + +### 9.3 第三阶段：预测与情景推演 + +先做情景推演，再做点预测。 + +情景变量： + +- 利率 +- 首付比例 +- 限购/限贷政策 +- 新房供应 +- 租金变化 +- 板块产业兑现 +- 人口和就业变化 + +输出： + +- 乐观/基准/谨慎情景 +- 价格压力区间 +- 流动性风险区间 +- 买入触发条件 + +## 10. 推荐仓库结构 + +```text +apps/ + web/ React + TypeScript + shadcn/ui + api/ Rust Axum + SQLx +services/ + analytics/ Python 指标、模型、报告 + ingestion/ 数据采集与导入任务 +packages/ + contracts/ OpenAPI schema / shared types + config/ 共享配置 +data/ + raw/ + bronze/ + silver/ + gold/ + sample/ +infra/ + docker-compose.yml + migrations/ +docs/ + system_architecture_and_development_plan.md + research_framework.md + data_dictionary.md +tests/ +``` + +当前 `src/shanghai_housing` 后续可以迁移到 `services/analytics`。 + +## 11. 开发路线 + +### Phase 0：研究内核验证，已开始 + +目标： + +- 建立基本数据表 +- 建立板块评分 +- 生成月报 +- 验证指标口径 + +状态： + +- 已完成 MVP。 + +### Phase 1：数据底座 + +目标： + +- 引入 PostgreSQL/PostGIS +- 建立 raw/bronze/silver/gold 数据分层 +- 建立数据源版本记录 +- 增加 CSV/Excel 导入 +- 增加官方数据手工导入模板 + +交付： + +- 可导入真实数据 +- 可追踪数据来源 +- 可复跑指标 + +### Phase 2：API 与前端骨架 + +目标： + +- 建立 API 服务 +- 建立 React 前端 +- 实现市场总览、板块列表、板块详情 +- 前端接入真实 API + +推荐实现： + +- 前端：Next.js + TypeScript + shadcn/ui +- API：Rust Axum + SQLx +- 数据库：PostgreSQL/PostGIS + +交付： + +- 可浏览市场总览 +- 可查看板块评分 +- 可生成并查看月报 + +### Phase 3：小区观察池 + +目标： + +- 小区资产库 +- 小区评分 +- 合理买入价 +- 关注列表 +- 风险标签 + +交付： + +- 小区筛选 +- 观察池 +- 标的对比 +- 投资备忘录 + +### Phase 4：模型与情景推演 + +目标： + +- 历史分位 +- 相似资产比较 +- 异常成交检测 +- 利率/政策/供应情景推演 + +交付： + +- 板块和小区估值解释 +- 买入价区间 +- 情景报告 + +### Phase 5：自动化与预警 + +目标： + +- 定时数据更新 +- 指标重算 +- 报告自动生成 +- 预警规则 + +交付： + +- 周报自动生成 +- 板块风险预警 +- 观察池价格变动提醒 + +## 12. 工程质量要求 + +### 12.1 数据质量 + +- 所有数据有来源和时间戳 +- 所有清洗规则版本化 +- 样本数据和真实数据严格隔离 +- 模型输出记录运行版本 + +### 12.2 API 质量 + +- OpenAPI 契约 +- 统一错误格式 +- 分页、排序、筛选 +- 请求日志 +- 慢查询追踪 + +### 12.3 前端质量 + +- 页面以研究工作流为中心 +- 表格、图表、地图是核心，不做装饰性首页 +- 所有图表口径可查看 +- 所有评分可解释 +- 重要结论可回溯到数据 + +### 12.4 测试 + +- 指标单元测试 +- 数据导入测试 +- API 契约测试 +- 前端组件测试 +- 端到端测试 + +## 13. 技术决策建议 + +推荐默认路线： + +```text +前端：Next.js + TypeScript + React + shadcn/ui +API：Rust Axum + SQLx +分析：Python + Polars + DuckDB +数据库：PostgreSQL + PostGIS +任务：先脚本/cron，后 Prefect 或 Airflow +图表：Recharts 起步，复杂图表可加入 ECharts +地图：MapLibre GL +``` + +Python 分析服务保留为长期组件： + +```text +API：Rust Axum 负责在线服务 +Analytics：Python 负责研究、建模、报告和实验 +Stable ETL：Rust Polars/DataFusion 可逐步承接稳定高性能任务 +Contract：OpenAPI 或共享 schema +``` + +这条路线从第一天就按强工程化平台建设，同时保留 Python 的研究效率。 + +## 14. 官方资料参考 + +- React TypeScript 文档：https://react.dev/learn/typescript +- shadcn/ui 文档：https://ui.shadcn.com/ +- Next.js 文档：https://nextjs.org/docs +- TanStack Query 文档：https://tanstack.com/query/latest/docs/framework/react/overview +- Recharts 文档：https://recharts.org/en-US/ +- Axum 文档：https://docs.rs/axum/latest/axum/ +- SQLx 文档：https://docs.rs/sqlx/latest/sqlx/ +- Polars 文档：https://docs.pola.rs/ +- DataFusion 文档：https://datafusion.apache.org/user-guide/introduction.html +- DuckDB 文档：https://duckdb.org/docs/ +- PostgreSQL 文档：https://www.postgresql.org/docs/ diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..b46e988 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,9 @@ +[project] +name = "shanghai-housing-research" +version = "0.1.0" +description = "A lightweight Shanghai housing market research and investment analysis system." +requires-python = ">=3.9" +dependencies = [] + +[tool.setuptools.packages.find] +where = ["src"] diff --git a/reports/.gitkeep b/reports/.gitkeep new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/reports/.gitkeep @@ -0,0 +1 @@ + diff --git a/src/shanghai_housing/__init__.py b/src/shanghai_housing/__init__.py new file mode 100644 index 0000000..706fc73 --- /dev/null +++ b/src/shanghai_housing/__init__.py @@ -0,0 +1,3 @@ +"""Shanghai housing market research toolkit.""" + +__version__ = "0.1.0" diff --git a/src/shanghai_housing/__main__.py b/src/shanghai_housing/__main__.py new file mode 100644 index 0000000..a049ad7 --- /dev/null +++ b/src/shanghai_housing/__main__.py @@ -0,0 +1,5 @@ +from .cli import main + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/shanghai_housing/cli.py b/src/shanghai_housing/cli.py new file mode 100644 index 0000000..1523135 --- /dev/null +++ b/src/shanghai_housing/cli.py @@ -0,0 +1,70 @@ +from __future__ import annotations + +import argparse +from pathlib import Path +from typing import Optional + +from .db import DEFAULT_DB_PATH, connect, init_db, load_sample_data +from .indicators import compute_area_scores +from .reporting import format_scores_table, write_monthly_report + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="shanghai_housing", + description="Shanghai housing market research toolkit.", + ) + parser.add_argument( + "--db", + type=Path, + default=DEFAULT_DB_PATH, + help=f"SQLite database path. Default: {DEFAULT_DB_PATH}", + ) + + subparsers = parser.add_subparsers(dest="command", required=True) + subparsers.add_parser("init-db", help="Create or update the SQLite schema.") + subparsers.add_parser("load-sample", help="Load demonstration sample data.") + + score_parser = subparsers.add_parser("score", help="Print area scores for a month.") + score_parser.add_argument("--month", required=True, help="Month in YYYY-MM format.") + + report_parser = subparsers.add_parser("report", help="Generate a monthly Markdown report.") + report_parser.add_argument("--month", required=True, help="Month in YYYY-MM format.") + report_parser.add_argument( + "--output", + type=Path, + help="Output Markdown path. Default: reports/monthly-YYYY-MM.md", + ) + + return parser + + +def main(argv: Optional[list[str]] = None) -> int: + parser = build_parser() + args = parser.parse_args(argv) + + if args.command == "init-db": + path = init_db(args.db) + print(f"Initialized database: {path}") + return 0 + + if args.command == "load-sample": + load_sample_data(args.db) + print(f"Loaded sample data into: {args.db}") + return 0 + + if args.command == "score": + with connect(args.db) as conn: + scores = compute_area_scores(conn, args.month) + print(format_scores_table(scores)) + return 0 + + if args.command == "report": + output = args.output or Path("reports") / f"monthly-{args.month}.md" + with connect(args.db) as conn: + path = write_monthly_report(conn, args.month, output) + print(f"Generated report: {path}") + return 0 + + parser.error(f"Unknown command: {args.command}") + return 2 diff --git a/src/shanghai_housing/db.py b/src/shanghai_housing/db.py new file mode 100644 index 0000000..8e7fa3c --- /dev/null +++ b/src/shanghai_housing/db.py @@ -0,0 +1,109 @@ +from __future__ import annotations + +import csv +import sqlite3 +from pathlib import Path +from typing import Iterable, Mapping, Union + + +PROJECT_ROOT = Path(__file__).resolve().parents[2] +DEFAULT_DB_PATH = PROJECT_ROOT / "data" / "shanghai_housing.sqlite" +SCHEMA_PATH = Path(__file__).with_name("schema.sql") +SAMPLE_DIR = PROJECT_ROOT / "data" / "sample" + + +def connect(db_path: Union[Path, str] = DEFAULT_DB_PATH) -> sqlite3.Connection: + conn = sqlite3.connect(str(db_path)) + conn.row_factory = sqlite3.Row + conn.execute("PRAGMA foreign_keys = ON") + return conn + + +def init_db(db_path: Union[Path, str] = DEFAULT_DB_PATH) -> Path: + path = Path(db_path) + path.parent.mkdir(parents=True, exist_ok=True) + with connect(path) as conn: + conn.executescript(SCHEMA_PATH.read_text(encoding="utf-8")) + return path + + +def load_sample_data(db_path: Union[Path, str] = DEFAULT_DB_PATH) -> None: + init_db(db_path) + with connect(db_path) as conn: + clear_tables( + conn, + [ + "neighborhood_monthly_metrics", + "neighborhoods", + "area_monthly_metrics", + "policy_events", + "areas", + ], + ) + load_csv(conn, "areas", SAMPLE_DIR / "areas.csv") + load_csv(conn, "area_monthly_metrics", SAMPLE_DIR / "area_monthly_metrics.csv") + load_csv(conn, "neighborhoods", SAMPLE_DIR / "neighborhoods.csv") + load_csv( + conn, + "neighborhood_monthly_metrics", + SAMPLE_DIR / "neighborhood_monthly_metrics.csv", + ) + + +def clear_tables(conn: sqlite3.Connection, tables: Iterable[str]) -> None: + for table in tables: + conn.execute(f"DELETE FROM {table}") + + +def load_csv(conn: sqlite3.Connection, table: str, path: Path) -> int: + with path.open("r", encoding="utf-8-sig", newline="") as handle: + rows = list(csv.DictReader(handle)) + if not rows: + return 0 + + columns = list(rows[0].keys()) + placeholders = ", ".join(["?"] * len(columns)) + column_sql = ", ".join(columns) + update_sql = ", ".join([f"{column}=excluded.{column}" for column in columns]) + sql = ( + f"INSERT INTO {table} ({column_sql}) VALUES ({placeholders}) " + f"ON CONFLICT DO UPDATE SET {update_sql}" + ) + conn.executemany(sql, [tuple(row[column] for column in columns) for row in rows]) + return len(rows) + + +def fetch_area_metrics(conn: sqlite3.Connection, month: str) -> list[sqlite3.Row]: + return conn.execute( + """ + SELECT + a.area_id, + a.name, + a.district, + a.segment, + m.* + FROM area_monthly_metrics m + JOIN areas a ON a.area_id = m.area_id + WHERE m.month = ? + ORDER BY a.district, a.name + """, + (month,), + ).fetchall() + + +def fetch_area_history( + conn: sqlite3.Connection, area_id: str, through_month: str +) -> list[sqlite3.Row]: + return conn.execute( + """ + SELECT * + FROM area_monthly_metrics + WHERE area_id = ? AND month <= ? + ORDER BY month + """, + (area_id, through_month), + ).fetchall() + + +def row_to_dict(row: Union[sqlite3.Row, Mapping[str, object]]) -> dict[str, object]: + return {key: row[key] for key in row.keys()} if isinstance(row, sqlite3.Row) else dict(row) diff --git a/src/shanghai_housing/indicators.py b/src/shanghai_housing/indicators.py new file mode 100644 index 0000000..45aea13 --- /dev/null +++ b/src/shanghai_housing/indicators.py @@ -0,0 +1,212 @@ +from __future__ import annotations + +import sqlite3 +from dataclasses import asdict, dataclass +from datetime import date +from typing import Optional + +from .db import fetch_area_history, fetch_area_metrics + + +@dataclass(frozen=True) +class AreaScore: + area_id: str + name: str + district: str + segment: str + month: str + investment_score: float + recommendation: str + liquidity_score: float + momentum_score: float + rent_support_score: float + safety_margin_score: float + credit_support_score: float + supply_risk_score: float + valuation_pressure_score: float + transaction_count: int + transaction_price_psm: float + listing_count: int + listing_price_psm: float + rent_price_psm: float + median_days_on_market: float + annual_rent_yield_pct: float + listing_pressure_ratio: float + discount_pct: float + price_momentum_pct: float + volume_momentum_pct: float + + def to_dict(self) -> dict[str, object]: + return asdict(self) + + +def previous_month(month: str) -> str: + year, month_num = [int(part) for part in month.split("-")] + current = date(year, month_num, 1) + if current.month == 1: + return f"{current.year - 1}-12" + return f"{current.year}-{current.month - 1:02d}" + + +def compute_area_scores(conn: sqlite3.Connection, month: str) -> list[AreaScore]: + current_rows = fetch_area_metrics(conn, month) + if not current_rows: + raise ValueError(f"No area metrics found for month {month}.") + + prior_month = previous_month(month) + prior_rows = {row["area_id"]: row for row in fetch_area_metrics(conn, prior_month)} + max_transactions = max(float(row["transaction_count"]) for row in current_rows) or 1.0 + + scores = [ + score_area(row, prior_rows.get(row["area_id"]), max_transactions, conn) + for row in current_rows + ] + return sorted(scores, key=lambda score: score.investment_score, reverse=True) + + +def score_area( + row: sqlite3.Row, + prior_row: Optional[sqlite3.Row], + max_transactions: float, + conn: sqlite3.Connection, +) -> AreaScore: + transaction_count = int(row["transaction_count"]) + transaction_price = float(row["transaction_price_psm"]) + listing_count = int(row["listing_count"]) + listing_price = float(row["listing_price_psm"]) + rent_price = float(row["rent_price_psm"]) + days_on_market = float(row["median_days_on_market"]) + new_supply_units = int(row["new_supply_units"]) + mortgage_rate = float(row["mortgage_rate_pct"]) + policy_signal = int(row["policy_signal"]) + + annual_rent_yield = safe_div(rent_price * 12, transaction_price) * 100 + listing_pressure = safe_div(listing_count, transaction_count) + supply_ratio = safe_div(new_supply_units, transaction_count) + discount_pct = safe_div(listing_price - transaction_price, listing_price) * 100 + price_momentum = momentum_pct( + transaction_price, + float(prior_row["transaction_price_psm"]) if prior_row else None, + ) + volume_momentum = momentum_pct( + transaction_count, + int(prior_row["transaction_count"]) if prior_row else None, + ) + + history = fetch_area_history(conn, row["area_id"], row["month"]) + price_percentile = historical_percentile( + transaction_price, + [float(item["transaction_price_psm"]) for item in history], + ) + + liquidity_score = clamp( + 0.65 * (transaction_count / max_transactions * 100) + + 0.35 * low_better(days_on_market, good=45, bad=120) + ) + momentum_score = clamp( + 0.55 * high_better(volume_momentum, bad=-20, good=25) + + 0.45 * high_better(price_momentum, bad=-3, good=4) + ) + rent_support_score = high_better(annual_rent_yield, bad=1.0, good=2.4) + supply_risk_score = clamp( + 0.60 * high_better(listing_pressure, bad=3.5, good=9.0) + + 0.40 * high_better(supply_ratio, bad=0.5, good=4.0) + ) + valuation_pressure_score = clamp( + 0.60 * price_percentile + 0.40 * (100 - rent_support_score) + ) + safety_margin_score = clamp( + 0.55 * high_better(discount_pct, bad=1.5, good=8.0) + + 0.45 * (100 - valuation_pressure_score) + ) + credit_support_score = clamp( + 0.70 * low_better(mortgage_rate, good=3.2, bad=5.0) + + 0.30 * ((policy_signal + 2) / 4 * 100) + ) + + investment_score = clamp( + 0.25 * liquidity_score + + 0.20 * momentum_score + + 0.20 * rent_support_score + + 0.15 * safety_margin_score + + 0.10 * credit_support_score + + 0.10 * (100 - supply_risk_score) + ) + + return AreaScore( + area_id=row["area_id"], + name=row["name"], + district=row["district"], + segment=row["segment"], + month=row["month"], + investment_score=round(investment_score, 1), + recommendation=recommendation(investment_score), + liquidity_score=round(liquidity_score, 1), + momentum_score=round(momentum_score, 1), + rent_support_score=round(rent_support_score, 1), + safety_margin_score=round(safety_margin_score, 1), + credit_support_score=round(credit_support_score, 1), + supply_risk_score=round(supply_risk_score, 1), + valuation_pressure_score=round(valuation_pressure_score, 1), + transaction_count=transaction_count, + transaction_price_psm=round(transaction_price, 1), + listing_count=listing_count, + listing_price_psm=round(listing_price, 1), + rent_price_psm=round(rent_price, 1), + median_days_on_market=round(days_on_market, 1), + annual_rent_yield_pct=round(annual_rent_yield, 2), + listing_pressure_ratio=round(listing_pressure, 2), + discount_pct=round(discount_pct, 2), + price_momentum_pct=round(price_momentum, 2), + volume_momentum_pct=round(volume_momentum, 2), + ) + + +def safe_div(numerator: float, denominator: float) -> float: + return numerator / denominator if denominator else 0.0 + + +def momentum_pct(current: float, previous: Optional[float]) -> float: + if previous is None or previous == 0: + return 0.0 + return (current - previous) / previous * 100 + + +def historical_percentile(current: float, values: list[float]) -> float: + if not values: + return 50.0 + low = min(values) + high = max(values) + if high == low: + return 50.0 + return clamp((current - low) / (high - low) * 100) + + +def high_better(value: float, bad: float, good: float) -> float: + if value <= bad: + return 0.0 + if value >= good: + return 100.0 + return clamp((value - bad) / (good - bad) * 100) + + +def low_better(value: float, good: float, bad: float) -> float: + if value <= good: + return 100.0 + if value >= bad: + return 0.0 + return clamp((bad - value) / (bad - good) * 100) + + +def clamp(value: float, lower: float = 0.0, upper: float = 100.0) -> float: + return max(lower, min(upper, value)) + + +def recommendation(score: float) -> str: + if score >= 75: + return "重点研究" + if score >= 65: + return "观察池" + if score >= 50: + return "中性观望" + return "谨慎等待" diff --git a/src/shanghai_housing/reporting.py b/src/shanghai_housing/reporting.py new file mode 100644 index 0000000..9ba53bc --- /dev/null +++ b/src/shanghai_housing/reporting.py @@ -0,0 +1,131 @@ +from __future__ import annotations + +import sqlite3 +from pathlib import Path + +from .indicators import AreaScore, compute_area_scores + + +def build_monthly_report(conn: sqlite3.Connection, month: str) -> str: + scores = compute_area_scores(conn, month) + top = scores[0] + weakest = scores[-1] + high_supply_risk = max(scores, key=lambda item: item.supply_risk_score) + avg_score = sum(score.investment_score for score in scores) / len(scores) + avg_yield = sum(score.annual_rent_yield_pct for score in scores) / len(scores) + + lines = [ + f"# 上海房市投资研究月报 {month}", + "", + "> 本报告由本地研究系统生成。当前数据若来自 `data/sample`，仅用于演示方法，不构成真实投资建议。", + "", + "## 核心结论", + "", + f"- 综合评分最高板块：{top.name}（{top.investment_score}，{top.recommendation}）。", + f"- 综合评分最低板块：{weakest.name}（{weakest.investment_score}，{weakest.recommendation}）。", + f"- 供应压力最高板块：{high_supply_risk.name}（供应风险 {high_supply_risk.supply_risk_score}）。", + f"- 样本平均投资观察评分：{avg_score:.1f}；样本平均年化租金收益率：{avg_yield:.2f}%。", + "", + "## 板块评分", + "", + "| 排名 | 板块 | 行政区 | 类型 | 综合分 | 建议 | 流动性 | 动量 | 租金支撑 | 安全边际 | 供应风险 |", + "| --- | --- | --- | --- | ---: | --- | ---: | ---: | ---: | ---: | ---: |", + ] + + for index, score in enumerate(scores, start=1): + lines.append( + "| {rank} | {name} | {district} | {segment} | {investment_score:.1f} | " + "{recommendation} | {liquidity_score:.1f} | {momentum_score:.1f} | " + "{rent_support_score:.1f} | {safety_margin_score:.1f} | {supply_risk_score:.1f} |".format( + rank=index, + name=score.name, + district=score.district, + segment=score.segment, + investment_score=score.investment_score, + recommendation=score.recommendation, + liquidity_score=score.liquidity_score, + momentum_score=score.momentum_score, + rent_support_score=score.rent_support_score, + safety_margin_score=score.safety_margin_score, + supply_risk_score=score.supply_risk_score, + ) + ) + + lines.extend( + [ + "", + "## 关键指标明细", + "", + "| 板块 | 成交套数 | 成交均价/㎡ | 挂牌套数 | 挂牌均价/㎡ | 去化压力 | 成交周期 | 年化租金收益率 | 成交折价 | 价格动量 | 成交动量 |", + "| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |", + ] + ) + + for score in scores: + lines.append( + "| {name} | {transaction_count} | {transaction_price_psm:,.0f} | {listing_count} | " + "{listing_price_psm:,.0f} | {listing_pressure_ratio:.2f} | " + "{median_days_on_market:.0f}天 | {annual_rent_yield_pct:.2f}% | " + "{discount_pct:.2f}% | {price_momentum_pct:.2f}% | {volume_momentum_pct:.2f}% |".format( + **score.to_dict() + ) + ) + + lines.extend( + [ + "", + "## 模型口径", + "", + "- 综合分由流动性、成交/价格动量、租金支撑、安全边际、信贷政策环境、供应风险共同决定。", + "- 供应风险越高表示挂牌库存、新增供应相对成交越重；综合分会对其反向处理。", + "- 安全边际主要来自成交/挂牌折价和历史价格分位。", + "- 第一版模型适合建立观察池，不适合单独作为买卖决策。", + "", + "## 下一步人工复核", + "", + "- 检查高分板块内的小区分化，剔除楼龄、物业、噪音、硬伤户型等不可量化风险。", + "- 对供应风险高的板块追踪新房开盘节奏、竞品库存和开发商价格策略。", + "- 将政策事件和信贷变化作为情景变量，而不是简单线性外推。", + "", + ] + ) + return "\n".join(lines) + + +def write_monthly_report( + conn: sqlite3.Connection, month: str, output_path: Path +) -> Path: + output_path.parent.mkdir(parents=True, exist_ok=True) + output_path.write_text(build_monthly_report(conn, month), encoding="utf-8") + return output_path + + +def format_scores_table(scores: list[AreaScore]) -> str: + rows = [ + ( + score.name, + f"{score.investment_score:.1f}", + score.recommendation, + f"{score.liquidity_score:.1f}", + f"{score.momentum_score:.1f}", + f"{score.rent_support_score:.1f}", + f"{score.supply_risk_score:.1f}", + ) + for score in scores + ] + header = ("板块", "综合分", "建议", "流动性", "动量", "租金", "供应风险") + return render_plain_table(header, rows) + + +def render_plain_table(header: tuple[str, ...], rows: list[tuple[str, ...]]) -> str: + widths = [ + max(len(str(row[index])) for row in [header, *rows]) + for index in range(len(header)) + ] + lines = [format_table_row(header, widths), format_table_row(tuple("-" * width for width in widths), widths)] + lines.extend(format_table_row(row, widths) for row in rows) + return "\n".join(lines) + + +def format_table_row(row: tuple[str, ...], widths: list[int]) -> str: + return " ".join(str(value).ljust(widths[index]) for index, value in enumerate(row)) diff --git a/src/shanghai_housing/schema.sql b/src/shanghai_housing/schema.sql new file mode 100644 index 0000000..0200411 --- /dev/null +++ b/src/shanghai_housing/schema.sql @@ -0,0 +1,68 @@ +PRAGMA foreign_keys = ON; + +CREATE TABLE IF NOT EXISTS areas ( + area_id TEXT PRIMARY KEY, + name TEXT NOT NULL, + district TEXT NOT NULL, + segment TEXT NOT NULL, + notes TEXT DEFAULT '' +); + +CREATE TABLE IF NOT EXISTS area_monthly_metrics ( + area_id TEXT NOT NULL, + month TEXT NOT NULL, + transaction_count INTEGER NOT NULL CHECK (transaction_count >= 0), + transaction_price_psm REAL NOT NULL CHECK (transaction_price_psm >= 0), + listing_count INTEGER NOT NULL CHECK (listing_count >= 0), + listing_price_psm REAL NOT NULL CHECK (listing_price_psm >= 0), + median_days_on_market REAL NOT NULL CHECK (median_days_on_market >= 0), + rent_price_psm REAL NOT NULL CHECK (rent_price_psm >= 0), + new_supply_units INTEGER NOT NULL CHECK (new_supply_units >= 0), + land_residential_gfa_sqm REAL NOT NULL CHECK (land_residential_gfa_sqm >= 0), + mortgage_rate_pct REAL NOT NULL CHECK (mortgage_rate_pct >= 0), + policy_signal INTEGER NOT NULL DEFAULT 0 CHECK (policy_signal BETWEEN -2 AND 2), + source TEXT NOT NULL DEFAULT 'manual', + PRIMARY KEY (area_id, month), + FOREIGN KEY (area_id) REFERENCES areas(area_id) +); + +CREATE TABLE IF NOT EXISTS neighborhoods ( + neighborhood_id TEXT PRIMARY KEY, + area_id TEXT NOT NULL, + name TEXT NOT NULL, + built_year INTEGER, + property_type TEXT NOT NULL, + metro_distance_m INTEGER, + school_quality TEXT, + notes TEXT DEFAULT '', + FOREIGN KEY (area_id) REFERENCES areas(area_id) +); + +CREATE TABLE IF NOT EXISTS neighborhood_monthly_metrics ( + neighborhood_id TEXT NOT NULL, + month TEXT NOT NULL, + transaction_count INTEGER NOT NULL CHECK (transaction_count >= 0), + transaction_price_psm REAL NOT NULL CHECK (transaction_price_psm >= 0), + listing_count INTEGER NOT NULL CHECK (listing_count >= 0), + listing_price_psm REAL NOT NULL CHECK (listing_price_psm >= 0), + rent_price_psm REAL NOT NULL CHECK (rent_price_psm >= 0), + median_days_on_market REAL NOT NULL CHECK (median_days_on_market >= 0), + available_units INTEGER NOT NULL CHECK (available_units >= 0), + source TEXT NOT NULL DEFAULT 'manual', + PRIMARY KEY (neighborhood_id, month), + FOREIGN KEY (neighborhood_id) REFERENCES neighborhoods(neighborhood_id) +); + +CREATE TABLE IF NOT EXISTS policy_events ( + event_id INTEGER PRIMARY KEY AUTOINCREMENT, + event_date TEXT NOT NULL, + level TEXT NOT NULL, + title TEXT NOT NULL, + impact_direction INTEGER NOT NULL CHECK (impact_direction BETWEEN -2 AND 2), + notes TEXT DEFAULT '', + source_url TEXT +); + +CREATE INDEX IF NOT EXISTS idx_area_metrics_month ON area_monthly_metrics(month); +CREATE INDEX IF NOT EXISTS idx_neighborhood_metrics_month ON neighborhood_monthly_metrics(month); +CREATE INDEX IF NOT EXISTS idx_policy_events_date ON policy_events(event_date); diff --git a/tests/test_indicators.py b/tests/test_indicators.py new file mode 100644 index 0000000..1e58e05 --- /dev/null +++ b/tests/test_indicators.py @@ -0,0 +1,36 @@ +from __future__ import annotations + +import tempfile +import unittest +from pathlib import Path + +from shanghai_housing.db import connect, load_sample_data +from shanghai_housing.indicators import compute_area_scores, previous_month + + +class IndicatorTests(unittest.TestCase): + def test_previous_month_handles_year_boundary(self) -> None: + self.assertEqual(previous_month("2026-01"), "2025-12") + self.assertEqual(previous_month("2026-05"), "2026-04") + + def test_area_scores_are_bounded_and_sorted(self) -> None: + with tempfile.TemporaryDirectory() as tmpdir: + db_path = Path(tmpdir) / "test.sqlite" + load_sample_data(db_path) + with connect(db_path) as conn: + scores = compute_area_scores(conn, "2026-05") + + self.assertEqual(len(scores), 5) + self.assertEqual( + [score.investment_score for score in scores], + sorted([score.investment_score for score in scores], reverse=True), + ) + for score in scores: + self.assertGreaterEqual(score.investment_score, 0) + self.assertLessEqual(score.investment_score, 100) + self.assertGreaterEqual(score.supply_risk_score, 0) + self.assertLessEqual(score.supply_risk_score, 100) + + +if __name__ == "__main__": + unittest.main()