From 30f86eab891a58c0c96fee11eecd2ec6ce624984 Mon Sep 17 00:00:00 2001 From: AmandaloveYang <2569718930@qq.com> Date: Wed, 18 Feb 2026 09:56:50 +0800 Subject: [PATCH] refactor: remove legacy trading engine, streamline project to weather-only bot --- MARKET_DISCOVERY.md | 74 --- MARKET_DISCOVERY_ZH.md | 74 --- PAPER_TRADING_GUIDE.md | 89 --- README.md | 2 - README_ZH.md | 2 - bot_listener.py | 4 - check_freshness.py | 23 - config/config.yaml | 86 +-- dashboard/streamlit_app.py | 200 ------ main.py | 865 ------------------------- run.py | 42 +- src/analysis/__init__.py | 0 src/analysis/orderbook_analyzer.py | 95 --- src/analysis/technical_indicators.py | 146 ----- src/analysis/volume_analyzer.py | 135 ---- src/analysis/whale_tracker.py | 59 -- src/data_collection/onchain_tracker.py | 56 -- src/data_collection/polymarket_api.py | 291 --------- src/models/__init__.py | 0 src/models/statistical_model.py | 285 -------- src/strategy/__init__.py | 0 src/strategy/decision_engine.py | 198 ------ src/strategy/position_manager.py | 153 ----- src/strategy/risk_manager.py | 99 --- src/trading/__init__.py | 0 src/trading/order_executor.py | 219 ------- src/trading/paper_trader.py | 158 ----- src/utils/notifier.py | 285 -------- tests/test_models.py | 29 - 29 files changed, 25 insertions(+), 3644 deletions(-) delete mode 100644 MARKET_DISCOVERY.md delete mode 100644 MARKET_DISCOVERY_ZH.md delete mode 100644 PAPER_TRADING_GUIDE.md delete mode 100644 check_freshness.py delete mode 100644 dashboard/streamlit_app.py delete mode 100644 main.py delete mode 100644 src/analysis/__init__.py delete mode 100644 src/analysis/orderbook_analyzer.py delete mode 100644 src/analysis/technical_indicators.py delete mode 100644 src/analysis/volume_analyzer.py delete mode 100644 src/analysis/whale_tracker.py delete mode 100644 src/data_collection/onchain_tracker.py delete mode 100644 src/data_collection/polymarket_api.py delete mode 100644 src/models/__init__.py delete mode 100644 src/models/statistical_model.py delete mode 100644 src/strategy/__init__.py delete mode 100644 src/strategy/decision_engine.py delete mode 100644 src/strategy/position_manager.py delete mode 100644 src/strategy/risk_manager.py delete mode 100644 src/trading/__init__.py delete mode 100644 src/trading/order_executor.py delete mode 100644 src/trading/paper_trader.py delete mode 100644 src/utils/notifier.py delete mode 100644 tests/test_models.py diff --git a/MARKET_DISCOVERY.md b/MARKET_DISCOVERY.md deleted file mode 100644 index a7003168..00000000 --- a/MARKET_DISCOVERY.md +++ /dev/null @@ -1,74 +0,0 @@ -# Polymarket Weather Market Discovery Technical Documentation - -> ⚠️ **Current Status: Suspended** -> The automated market discovery and monitoring engine described here has been commented out in `run.py`. The system currently operates in "passive query mode" — weather analysis is only triggered by the `/city` command. - -This document explains the technical implementation of how PolyWeather identifies and tracks weather markets on Polymarket. - -## 1. Data Sources - -We bypass high-level SDKs and interact directly with the **Polymarket Gamma API**, which is the primary metadata layer for Discovery. - -- **Base URL:** `https://gamma-api.polymarket.com` -- **Endpoint:** `/markets` - -## 2. Discovery Strategy - -The system uses a multi-layered search approach to ensure no city segments are missed. - -### 2.1 Keyword Triple-Search - -Instead of one query, we execute three concurrent search patterns: - -1. `"highest temperature"`: Targets the primary question text. -2. `"temperature in"`: Broad search for regional markets. -3. `"daily weather"`: Fallback for markets with different naming conventions. - -### 2.2 Prioritization - -We apply specific sorting to find the **latest** available contracts (e.g., February 9th, 2026): - -- `order=id` & `ascending=false`: Scans the newest created markets first. -- `active=true` & `closed=false`: Filters out resolved or expired contracts. - -## 3. Filtering & Parsing Logic - -Since Polymarket hosts thousands of events, we apply a strict "Weather Filter" in the code: - -### 3.1 Text Validation - -We inspect both the `question` and the `slug`: - -- **Pattern Match:** Must contain `"highest temperature in"` or `"highest-temperature-in"`. -- **Exclusion:** (Implicitly handled by keyword search) filtered from sports or politics. - -### 3.2 Negative Risk Market Handling - -Weather markets on Polymarket are often structured as **Negative Risk** groups (where multiple outcomes like "70°F or higher" and "68-69°F" belong to one event). - -**Technical Challenge:** In the API's list view, the `activeTokenId` field is often `null` for these complex markets. -**Our Solution:** - -1. Check `clobTokenIds`. -2. If it's a JSON string (common in Gamma), parse it into a Python list. -3. If `activeTokenId` is missing, we treat the first token ID in the list as the **"YES" Token**. -4. This allows us to fetch the real-time orderbook/price even for markets that haven't fully "activated" in the front-end metadata. - -## 4. Market Data Structure - -Every market found is normalized into this structure for the Decision Engine: - -- `condition_id`: The UMA condition ID for resolution. -- `active_token_id`: The specific ERC1155 token ID we want to buy/monitor. -- `group_id`: The `negRiskMarketID`, which allows the bot to understand that specific temperature ranges (e.g., 70°F vs 72°F) are related to the same city. -- `slug`: Used for generating direct dashboard links. - -## 5. Frequency & Caching - -- **Discovery Frequency:** The system rescans for new cities/dates every **5 minutes**. -- **Caching:** Found markets are stored in an internal memory cache (`_weather_markets_cache`) to reduce API pressure and avoid rate limits. - ---- - -_Created on: 2026-02-07_ -_PolyWeather System Documentation_ diff --git a/MARKET_DISCOVERY_ZH.md b/MARKET_DISCOVERY_ZH.md deleted file mode 100644 index 5011bfdb..00000000 --- a/MARKET_DISCOVERY_ZH.md +++ /dev/null @@ -1,74 +0,0 @@ -# Polymarket 天气市场搜寻技术文档 - -> ⚠️ **当前状态：功能休眠** -> 本中提到的自动搜寻与监控引擎目前已在 `run.py` 中被注释。当前系统工作于“被动查询模式”，仅在用户输入 `/city` 指令时提供天气分析。 - -本文档详细说明了 PolyWeather 如何在 Polymarket 上自动识别、筛选并跟踪天气相关市场的技术实现逻辑。 - -## 1. 数据来源 - -我们跳过了复杂的官方 SDK，直接与 **Polymarket Gamma API** 交互。这是 Polymarket 的官方元数据层，负责所有市场的发现与展示。 - -- **Base URL:** `https://gamma-api.polymarket.com` -- **Endpoint:** `/markets` - -## 2. 搜寻策略 - -由于 Polymarket 同时挂载数千个预测市场，系统采用多层搜索方案以确保不会遗漏任何城市的分段合约。 - -### 2.1 关键词三重搜索 - -程序并非只搜索一个词，而是并发执行三个搜索模式： - -1. `"highest temperature"`: 匹配大多数天气问题的核心描述。 -2. `"temperature in"`: 针对特定地区市场的宽泛搜索。 -3. `"daily weather"`: 针对某些命名不规范市场的兜底搜索。 - -### 2.3 优先级与排序 - -为了确保能搜到**最新**发布的合约（例如 2026年2月9日 的市场），我们应用了特定的 API 排序参数： - -- `order=id` & `ascending=false`: 优先扫描最新创建的市场 ID。 -- `active=true` & `closed=false`: 过滤掉已结算或已关闭的无效合约。 - -## 3. 过滤与解析逻辑 - -系统在获取 API 返回的列表后，会进行二次深度筛选： - -### 3.1 文本校验 - -检查市场的 `question`（问题描述）和 `slug`（URL 路径）： - -- **模式匹配:** 必须包含 `"highest temperature in"` 或 `"highest-temperature-in"`。 -- **城市提取:** 逻辑会自动识别问题中的城市名（如 芝加哥、伦敦 等）。 - -### 3.2 负风险（Negative Risk）市场处理 - -Polymarket 的天气市场通常以 **Negative Risk** 分组形式存在（一个事件下包含多个互斥的区间，如“70°F以上”和“68-69°F”）。 - -**技术挑战:** 在 API 的列表视图中，这类市场的 `activeTokenId` 字段经常返回 `null`。 -**我们的解决方案:** - -1. 检查 `clobTokenIds` 字段。 -2. 如果该字段是 JSON 字符串（Gamma API 的常见返回格式），则将其解析为 Python 列表。 -3. 如果 `activeTokenId` 缺失，我们将列表中的第一个 Token ID 视为 **"YES" Token**。 -4. 这使系统能够绕过元数据同步延迟，直接在 CLOB 层面抓取实时买入/卖出价格。 - -## 4. 市场规范化结构 - -每个搜寻到的分段都会被规范化为以下结构，供决策引擎（Decision Engine）使用： - -- `condition_id`: 用于结果判定的 UMA 条件 ID。 -- `active_token_id`: 我们需要监控并买入的特定 ERC1155 Token ID。 -- `group_id`: 即 `negRiskMarketID`。这让机器人知道哪些不同的温度区间是属于同一个城市的，从而进行跨区间套利或对冲分析。 -- `slug`: 市场的唯一路径名，用于在 Telegram 预警中生成直接跳转链接。 - -## 5. 频率与缓存机制 - -- **搜寻频率:** 系统每 **5 分钟** 重新扫描一次新城市和新日期。 -- **缓存策略:** 搜寻到的市场会存入内存缓存（`_weather_markets_cache`），以减轻 API 压力并避免触发现速限制。 - ---- - -_创建日期: 2026-02-07_ -_PolyWeather 系统技术文档_ diff --git a/PAPER_TRADING_GUIDE.md b/PAPER_TRADING_GUIDE.md deleted file mode 100644 index 93efb9e2..00000000 --- a/PAPER_TRADING_GUIDE.md +++ /dev/null @@ -1,89 +0,0 @@ -# 📈 PolyWeather 模拟仓 (Paper Trading) 使用指南 - -> ⚠️ **当前状态：功能暂停** -> 为了专注于高实时性的天气查询服务，自动模拟交易功能目前已在代码中被禁用。电报指令 `/portfolio` 暂不可用。 - -本系统提供全自动的模拟交易功能，让您在不投入真实资金的情况下，验证天气预测逻辑的盈利能力。 - -## 🛠️ 运行机制 - -1. **自动开仓**: - - 监控引擎在扫描中，一旦发现任何档位的 **Buy Yes** 或 **Buy No** 价格处于 **85¢ - 95¢** 区间（与城市监控报告一致），即触发买入。 - - 初始本金: **$1000.00** - - 单笔投入: **动态 $3-$10**（根据四层风控策略自动调整，见下文） - - 资金检查: 余额不足时将停止开仓。 - - **价格来源**: 使用真实 **Ask 价格**（实际可成交价格），而非中间价 -2. **实时估值**: - - 每轮扫描结束后，系统会根据最新盘口中间价更新持仓价值。 -3. **自动结项**: - - 当市场价格达到 0¢ 或 100¢（Polymarket 已结算），系统自动平仓并计算盈亏，资金回笼。 -4. **数据持久化**: - - 持仓与余额保存在 `data/paper_positions.json`。 - -## 🎯 四层风控仓位策略 - -系统结合 **Open-Meteo 天气预测**、**结算时间** 和 **成交量** 自动决定仓位大小： - -| 条件组合 | 基础仓位 | 标签 | 说明 | -| ------------------------------- | -------- | ---------- | -------------- | -| 价格 ≥90¢ + 天气支持 + 高成交量 | **$10** | 🔥高置信 | 三重确认，重注 | -| 价格 ≥90¢ + 天气支持 | **$7** | ⭐中置信 | 双重确认 | -| 价格 ≥92¢ | **$5** | 📌价格锁定 | 纯价格锁定 | -| 其他 85-91¢ | **$3** | 💡试探 | 最小仓位试探 | - -### 风控过滤规则 - -1. **时间衰减**: - - ≤1小时: 停止建仓 (0%) - - 1-4小时: 缩小至 40% - - 4-12小时: 缩小至 70% - - > 12小时: 100% -2. **预算上限**: 每日最高投入 $50 -3. **成交量加权**: 低活跃市场额外缩减 20% - -### 天气支持判断逻辑 - -- **买 NO**: Open-Meteo 预测温度在选项区间 **之外** (±2° 容差) -- **买 YES**: Open-Meteo 预测温度 **落入** 选项区间 - -### 策略建议显示 - -当推送包含交易信号时，会附带策略建议： - -``` -💡 策略建议: -• 预测温度19.0°C落在21°C区间，市场与模型一致 -``` - -### METAR 实测数据 - -当天结算的市场会额外显示机场实测数据，帮助验证预测准确性： - -``` -✈️ 机场实测 (KORD): - 🌡️ 32.0°F | 风速:12kt - 🕐 观测: 14:00 UTC -``` - -## 📊 盈亏计算公式 - -- **持仓份额** = $5 / (买入价格 / 100) -- **可用余额** = 初始本金 - 累计投入总额 -- **浮动盈亏** = 当前总价值 - 投入本金 ($5) - -## 🤖 电报指令 - -您可以直接在机器人中通过以下指令查看进度： - -- **/portfolio**: 实时返回当前所有“浮动”持仓的盈亏状况、历史胜率以及账户余额。 - -## 📁 存储文件说明 - -如果您需要手动清理或修改仓位，可以编辑 `data/paper_positions.json`。 - -- `status: "OPEN"` 表示正在持仓。 -- `entry_price` 以美分为单位（如 91 表示 0.91$）。 - ---- - -**蚂蚁重力 (Antigravity) 实验室** diff --git a/README.md b/README.md index e050cd48..50d7c98a 100644 --- a/README.md +++ b/README.md @@ -54,8 +54,6 @@ py -3.11 run.py > Local machine is for editing code and Git push only. IDE import errors are expected (dependencies not installed locally) and do not affect VPS operation. -_Note: The system is currently in **Weather Query Mode**. Legacy market monitoring and automated trading modules are suspended._ - --- ## 🤖 Telegram Bot Commands diff --git a/README_ZH.md b/README_ZH.md index 90d1e3af..f24cd2d6 100644 --- a/README_ZH.md +++ b/README_ZH.md @@ -54,8 +54,6 @@ py -3.11 run.py > 本地笔记本**不需要安装依赖**，只用来编辑代码和 Git 推送。IDE 的 import 报错是因为本地没装依赖，不影响 VPS 运行。 -_注意：系统当前处于 **天气查询模式**。主动市场监控和自动交易模块已暂停。_ - --- ## 🤖 Telegram 机器人指令 diff --git a/bot_listener.py b/bot_listener.py index 75c16d13..547a4af9 100644 --- a/bot_listener.py +++ b/bot_listener.py @@ -299,10 +299,6 @@ def start_bot(): parse_mode="HTML", ) - @bot.message_handler(commands=["signal", "portfolio", "status"]) - def disabled_feature(message): - bot.reply_to(message, "ℹ️ 监控引擎与交易模拟功能已暂停，现仅提供天气查询服务。") - @bot.message_handler(commands=["city"]) def get_city_info(message): """查询指定城市的天气详情""" diff --git a/check_freshness.py b/check_freshness.py deleted file mode 100644 index acb9a730..00000000 --- a/check_freshness.py +++ /dev/null @@ -1,23 +0,0 @@ -import requests -import json -import time - -def test_mgm(istno): - # 添加时间戳防止缓存 - url = f"https://servis.mgm.gov.tr/web/sondurumlar?istno={istno}&_={int(time.time()*1000)}" - headers = { - "Origin": "https://www.mgm.gov.tr", - "User-Agent": "Mozilla/5.0" - } - try: - resp = requests.get(url, headers=headers) - if resp.status_code == 200: - data = resp.json() - print(json.dumps(data, indent=2, ensure_ascii=False)) - else: - print(f"Error: {resp.status_code}") - except Exception as e: - print(f"Exception: {e}") - -print("--- Station 17128 (Esenboğa) ---") -test_mgm(17128) diff --git a/config/config.yaml b/config/config.yaml index 2e351929..d8c6744c 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -1,68 +1,25 @@ -# API Configuration -api: - polymarket: - base_url: "https://clob.polymarket.com" - ws_url: "wss://ws-subscriptions-clob.polymarket.com/ws/market" - timeout: 30 - retry_attempts: 3 - api_key: "019c2d40-5d23-75a6-ab33-02ae5d2a033e" +# Weather API Configuration +weather: + meteoblue_api_key: null # Set via METEOBLUE_API_KEY env var + timeout: 30 - weather: - openweather: - base_url: "https://api.openweathermap.org/data/2.5" - timeout: 10 - wunderground: - base_url: "https://api.weather.com/v3" - timeout: 10 - visualcrossing: - base_url: "https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services" - timeout: 10 - -# Trading Parameters -trading: - min_confidence: 0.65 # Minimum model confidence to trade - max_single_trade: 500 # Maximum single trade amount ($) - max_position_ratio: 0.25 # Maximum position as ratio of capital - max_total_exposure: 0.80 # Maximum total exposure - min_trade_size: 10 # Minimum trade size ($) - -# Risk Management -risk: - max_drawdown: 0.10 # Maximum allowed drawdown (10%) - stop_loss: 0.15 # Stop loss threshold (15%) - take_profit: 0.30 # Take profit threshold (30%) - min_liquidity: 1000 # Minimum market liquidity ($) - max_slippage: 0.02 # Maximum acceptable slippage (2%) - -# Analysis Parameters -analysis: - volume_threshold: 2.0 # Volume spike threshold (std dev) - large_order_threshold: 1000 # Large order detection threshold ($) - rsi_period: 14 # RSI calculation period - bollinger_period: 20 # Bollinger Bands period - bollinger_std: 2.0 # Bollinger Bands standard deviation - -# Model Weights (for multi-factor decision) -weights: - statistical_prediction: 0.50 - data_source_consensus: 0.15 - market_volume_signal: 0.15 - orderbook_analysis: 0.10 - technical_indicators: 0.05 - onchain_whale_signal: 0.05 - -# Target Markets -markets: - - id: "ankara" - city: "Ankara" - country: "Turkey" - latitude: 39.9334 - longitude: 32.8597 +# Target Cities +cities: - id: "london" city: "London" country: "UK" latitude: 51.5074 longitude: -0.1278 + - id: "paris" + city: "Paris" + country: "France" + latitude: 48.8566 + longitude: 2.3522 + - id: "ankara" + city: "Ankara" + country: "Turkey" + latitude: 39.9334 + longitude: 32.8597 - id: "new_york" city: "New York" country: "USA" @@ -73,20 +30,9 @@ markets: country: "USA" latitude: 41.8781 longitude: -87.6298 - - id: "paris" - city: "Paris" - country: "France" - latitude: 48.8566 - longitude: 2.3522 # Logging logging: level: "INFO" rotation: "10 MB" retention: "10 days" - -# Scheduler -scheduler: - data_refresh_interval: 60 # seconds - model_update_interval: 300 # seconds - risk_check_interval: 30 # seconds diff --git a/dashboard/streamlit_app.py b/dashboard/streamlit_app.py deleted file mode 100644 index 5156dd94..00000000 --- a/dashboard/streamlit_app.py +++ /dev/null @@ -1,200 +0,0 @@ -import streamlit as st -import pandas as pd -import plotly.express as px -import plotly.graph_objects as go -from datetime import datetime, timedelta -import sys -sys.path.insert(0, '..') - -st.set_page_config( - page_title="Polymarket Trading Dashboard", - page_icon="📊", - layout="wide" -) - -# Custom CSS -st.markdown(""" - -""", unsafe_allow_html=True) - -# Header -st.title("📊 Polymarket Trading Dashboard") -st.markdown("---") - -# Sidebar -with st.sidebar: - st.header("⚙️ Settings") - market_id = st.text_input("Market ID", "weather-ankara-temperature") - refresh_rate = st.slider("Refresh Rate (seconds)", 10, 300, 60) - st.markdown("---") - st.header("📈 Quick Stats") - st.metric("Total PnL", "$0.00", "+0%") - st.metric("Open Positions", "0") - st.metric("Win Rate", "N/A") - -# Main content -col1, col2, col3, col4 = st.columns(4) - -with col1: - st.metric( - label="Current Price", - value="$0.92", - delta="+2.3%" - ) - -with col2: - st.metric( - label="Model Prediction", - value="7.2°C", - delta="+0.5°C" - ) - -with col3: - st.metric( - label="Confidence Score", - value="0.78", - delta="+0.05" - ) - -with col4: - st.metric( - label="Signal", - value="BUY", - delta="Strong" - ) - -st.markdown("---") - -# Charts -col_left, col_right = st.columns(2) - -with col_left: - st.subheader("📉 Price History") - - # Demo price data - dates = pd.date_range(start=datetime.now() - timedelta(days=7), periods=168, freq='H') - prices = [0.85 + i * 0.0005 + (i % 24) * 0.001 for i in range(168)] - - df_prices = pd.DataFrame({ - 'Date': dates, - 'Price': prices - }) - - fig_price = px.line(df_prices, x='Date', y='Price', - template='plotly_dark', - color_discrete_sequence=['#00ff88']) - fig_price.update_layout( - height=300, - margin=dict(l=0, r=0, t=0, b=0) - ) - st.plotly_chart(fig_price, use_container_width=True) - -with col_right: - st.subheader("🌡️ Temperature Forecast") - - # Demo temperature data - forecast_dates = pd.date_range(start=datetime.now(), periods=72, freq='H') - temps = [5 + (i % 24) * 0.3 + (i // 24) * 0.5 for i in range(72)] - - df_temp = pd.DataFrame({ - 'Date': forecast_dates, - 'Temperature': temps - }) - - fig_temp = px.line(df_temp, x='Date', y='Temperature', - template='plotly_dark', - color_discrete_sequence=['#ff6b6b']) - fig_temp.update_layout( - height=300, - margin=dict(l=0, r=0, t=0, b=0) - ) - st.plotly_chart(fig_temp, use_container_width=True) - -st.markdown("---") - -# Decision Factors -st.subheader("🎯 Decision Factors") - -factors_col1, factors_col2 = st.columns(2) - -with factors_col1: - # Factor scores - factors = { - 'Statistical Prediction': 0.85, - 'Data Consensus': 0.90, - 'Volume Signal': 0.65, - 'Orderbook Analysis': 0.72, - 'Technical Indicators': 0.58, - 'Whale Signal': 0.45 - } - - fig_factors = go.Figure(go.Bar( - x=list(factors.values()), - y=list(factors.keys()), - orientation='h', - marker_color=['#00ff88' if v > 0.65 else '#ffaa00' if v > 0.4 else '#ff6b6b' - for v in factors.values()] - )) - fig_factors.update_layout( - template='plotly_dark', - height=250, - margin=dict(l=0, r=0, t=0, b=0), - xaxis_title="Score", - xaxis_range=[0, 1] - ) - st.plotly_chart(fig_factors, use_container_width=True) - -with factors_col2: - # Order book visualization - st.markdown("**📚 Order Book**") - - bids = [ - {"price": 0.91, "size": 500}, - {"price": 0.90, "size": 800}, - {"price": 0.89, "size": 1200}, - ] - asks = [ - {"price": 0.93, "size": 600}, - {"price": 0.94, "size": 400}, - {"price": 0.95, "size": 900}, - ] - - orderbook_df = pd.DataFrame({ - 'Bid Price': [b['price'] for b in bids], - 'Bid Size': [b['size'] for b in bids], - 'Ask Price': [a['price'] for a in asks], - 'Ask Size': [a['size'] for a in asks] - }) - - st.dataframe(orderbook_df, use_container_width=True, hide_index=True) - -st.markdown("---") - -# Recent Trades -st.subheader("📝 Recent Trades") - -trades_df = pd.DataFrame({ - 'Time': ['10:30:15', '10:28:42', '10:25:11'], - 'Side': ['BUY', 'BUY', 'SELL'], - 'Price': ['$0.92', '$0.91', '$0.88'], - 'Amount': ['$100', '$150', '$75'], - 'Status': ['✅ Filled', '✅ Filled', '✅ Filled'] -}) - -st.dataframe(trades_df, use_container_width=True, hide_index=True) - -# Footer -st.markdown("---") -st.markdown("*Last updated: " + datetime.now().strftime("%Y-%m-%d %H:%M:%S") + "*") diff --git a/main.py b/main.py deleted file mode 100644 index 69378c96..00000000 --- a/main.py +++ /dev/null @@ -1,865 +0,0 @@ -import sys -import time -import os -import json -import re -from datetime import datetime, timedelta -from loguru import logger - -from src.utils.config_loader import load_config -from src.utils.logger import setup_logger -from src.data_collection.polymarket_api import PolymarketClient -from src.data_collection.weather_sources import WeatherDataCollector -from src.data_collection.onchain_tracker import OnchainTracker -from src.models.statistical_model import TemperaturePredictor -from src.analysis.whale_tracker import WhaleTracker -from src.strategy.decision_engine import DecisionEngine -from src.strategy.risk_manager import RiskManager -from src.trading.paper_trader import PaperTrader -from src.utils.notifier import TelegramNotifier - - -def main(): - # 1. 初始化配置与日志 - config_data = load_config() - setup_logger(config_data.get("app", {}).get("log_level", "INFO")) - - logger.info("🌟 PolyWeather 监控引擎启动中...") - - # 2. 初始化核心组件 - polymarket = PolymarketClient(config_data["polymarket"]) - weather = WeatherDataCollector(config_data["weather"]) - onchain = OnchainTracker(config_data["polymarket"], polymarket) - notifier = TelegramNotifier(config_data["telegram"]) - - # 3. 初始化分析与交易组件 - predictor = TemperaturePredictor() - risk_manager = RiskManager(config_data.get("config", {})) - decision_engine = DecisionEngine(config_data.get("config", {})) - whale_tracker = WhaleTracker(config_data.get("config", {}), onchain) - paper_trader = PaperTrader() - - # 发送启动通知 - notifier._send_message( - "🚀 Polymarket 天气监控系统启动成功\n正在扫描 12 个核心城市的最高温市场..." - ) - - # 信号记忆（持久化到文件） - pushed_signals = {} - SIGNALS_FILE = "data/pushed_signals.json" - if os.path.exists(SIGNALS_FILE): - try: - with open(SIGNALS_FILE, "r", encoding="utf-8") as f: - pushed_signals = json.load(f) - logger.info(f"已加载历史推送记录，共 {len(pushed_signals)} 条") - except: - pushed_signals = {} - - # 确保data目录存在 - if not os.path.exists("data"): - os.makedirs("data") - - location_cache = {} - - # 价格历史追踪（用于计算趋势） - PRICE_HISTORY_FILE = "data/price_history.json" - price_history = {} - if os.path.exists(PRICE_HISTORY_FILE): - try: - with open(PRICE_HISTORY_FILE, "r", encoding="utf-8") as f: - price_history = json.load(f) - except: - price_history = {} - - try: - while True: - logger.info("--- 开启新一轮全量动态监控 (自动搜寻所有天气市场) ---") - cached_signals = {} - all_markets_cache = {} - - # 1. 直接从 Polymarket 获取所有天气合约 - all_weather_markets = polymarket.get_weather_markets() - - # 1.5 尝试通过slug获取可能遗漏的市场（如部分结算的市场） - special_slugs = [] - - for slug in special_slugs: - event = polymarket.get_event_by_slug(slug) - if event: - title = event.get("title", "") - logger.info(f"通过slug找到特殊事件: {title}") - - # 提取城市名 - city = weather.extract_city_from_question(title) - if not city: - city = "Unknown" - - # 将该事件的所有市场添加到列表 - for m in event.get("markets", []): - # 检查是否已存在 - c_id = m.get("conditionId") - if not any( - existing.get("condition_id") == c_id - for existing in all_weather_markets - ): - all_weather_markets.append( - { - "condition_id": c_id, - "question": m.get("groupItemTitle") - or m.get("question"), - "active_token_id": m.get("activeTokenId"), - "tokens": m.get("clobTokenIds"), - "prices": m.get("outcomePrices"), - "event_title": title, - "slug": slug, - "city": city, # 提前标记城市 - } - ) - logger.debug(f"添加特殊市场: {m.get('groupItemTitle')}") - - if not all_weather_markets: - logger.warning("当前 Polymarket 似乎没有任何活跃的天气市场，等待中...") - time.sleep(300) - continue - - # 2. 批量同步盘口价格 (优化：为每个档位获取其对应的真实 Token 价格) - token_price_map = {} - price_requests = [] - for m in all_weather_markets: - ts = m.get("tokens", []) - if isinstance(ts, str): - try: - ts = json.loads(ts) - except: - ts = [] - - active_tid = m.get("active_token_id") - - # 智能识别买入/买否 Token - if active_tid and isinstance(ts, list): - # 获取该档位的买入价 (Ask) - price_requests.append({"token_id": active_tid, "side": "ask"}) - - if len(ts) == 2: - # 传统的二选一，直接获取 No Token 的 Ask - no_tid = ts[1] if ts[0] == active_tid else ts[0] - price_requests.append({"token_id": no_tid, "side": "ask"}) - else: - # 多选一，需要用 1 - Bid(Yes) 来模拟 Buy No - price_requests.append({"token_id": active_tid, "side": "bid"}) - - if price_requests: - logger.info(f"正在同步 {len(price_requests)} 个档位的真实盘口价格...") - token_price_map = polymarket.get_multiple_prices(price_requests) - logger.info(f"价格同步完成，成功获取 {len(token_price_map)} 个实时报价") - - # 3. 按城市分组（按condition_id去重） - markets_by_city = {} - seen_condition_ids = set() # Initialize seen_condition_ids here - for i, m in enumerate(all_weather_markets): - # Use condition_id + active_token_id as unique key to support multi-bracket markets - unique_market_key = f"{m.get('condition_id')}_{m.get('active_token_id')}" - if unique_market_key in seen_condition_ids: - continue - seen_condition_ids.add(unique_market_key) - - # 注入实时批量价格 - ts = m.get("tokens", []) - if isinstance(ts, str): - try: - ts = json.loads(ts) - except: - ts = [] - - active_tid = m.get("active_token_id") - - if active_tid and isinstance(ts, list): - m["buy_yes_live"] = token_price_map.get(f"{active_tid}:ask") - - if len(ts) == 2: - no_tid = ts[1] if ts[0] == active_tid else ts[0] - m["buy_no_live"] = token_price_map.get(f"{no_tid}:ask") - else: - # 1 - Bid(Yes) = Ask(No) - bid_val = token_price_map.get(f"{active_tid}:bid") - if bid_val: - m["buy_no_live"] = 1.0 - bid_val - - # 优先使用发现阶段已经识别出的城市名 - city = m.get("city") - - # 如果发现阶段没识别出，再尝试从问题文本或 Slug 提取 - if not city or city == "Unknown": - full_context = f"{m.get('event_title', '')} {m.get('question', '')} {m.get('slug', '')}" - city = weather.extract_city_from_question(full_context) - - if i < 5: - logger.debug( - f"分析合约 {i}: City='{city}' | Title='{m.get('event_title')}" - ) - - if not city: - continue - - if city not in markets_by_city: - markets_by_city[city] = [] - markets_by_city[city].append(m) - - logger.info( - f"动态发现 {len(markets_by_city)} 个受监控城市，共 {len(all_weather_markets)} 个合约" - ) - - # 3. 逐个城市分析 - for city, city_markets in markets_by_city.items(): - try: - # 获取/缓存坐标 - if city not in location_cache: - coords = weather.get_coordinates(city) - if not coords: - continue - location_cache[city] = coords - logger.info( - f"📍 城市定位成功: {city} -> ({coords['lat']}, {coords['lon']})" - ) - - loc = location_cache[city] - - # A. 获取实时天气共识 - weather_data = weather.fetch_all_sources( - city, lat=loc["lat"], lon=loc["lon"] - ) - consensus = weather.check_consensus(weather_data) - - if not consensus.get("consensus"): - continue - - temp_unit = weather_data.get("open-meteo", {}).get( - "unit", "celsius" - ) - temp_symbol = "°F" if temp_unit == "fahrenheit" else "°C" - logger.info( - f"☁️ {city} 当前气温: {consensus['average_temp']}{temp_symbol} (unit={temp_unit}) | 监控合约: {len(city_markets)}" - ) - - # --- 本城市汇总预警缓存 --- - city_alerts = [] - city_local_time = None - city_total_vol = 0 - city_pred_high = None - city_target_date = None - city_strategy_tips = [] - - # B. 遍历该城市所有合约 - for market in city_markets: - market_id = market.get("condition_id") - question = market.get("question", "未知市场") - event_title = market.get("event_title", "") - - # 累计城市总成交量 - vol_raw = market.get("volume", 0) - if isinstance(vol_raw, str): - try: - vol_raw = float( - vol_raw.replace("$", "").replace(",", "") - ) - except: - vol_raw = 0 - city_total_vol += vol_raw - - # 识别该合约的目标日期 - target_date = weather.extract_date_from_title( - event_title - ) or weather.extract_date_from_title(question) - ref_temp = consensus["average_temp"] - if target_date: - daily_data = weather_data.get("open-meteo", {}).get( - "daily", {} - ) - if daily_data: - dates = daily_data.get("time", []) - max_temps = daily_data.get("temperature_2m_max", []) - for idx, d_str in enumerate(dates): - if target_date == d_str: - ref_temp = max_temps[idx] - break - - # --- 价格获取逻辑 (增强版) --- - # 使用 token_price_map 获取实时数据 - active_tid = market.get("active_token_id") - ts = market.get("tokens", []) - if isinstance(ts, str): - ts = json.loads(ts) - - buy_yes_price = None - buy_no_price = None - bid_yes_price = None - - if len(ts) == 2: - # 传统二选一市场 (Yes/No Token 独立) - buy_yes_price = token_price_map.get(f"{ts[0]}:ask") - buy_no_price = token_price_map.get(f"{ts[1]}:ask") - bid_yes_price = token_price_map.get(f"{ts[0]}:bid") - elif active_tid: - # 多选一市场 (单 Token 对应一个档位) - buy_yes_price = token_price_map.get(f"{active_tid}:ask") - bid_yes_price = token_price_map.get(f"{active_tid}:bid") - if bid_yes_price is not None: - buy_no_price = 1.0 - bid_yes_price - - # 兜底概率计算 - current_prob = ( - (buy_yes_price + bid_yes_price) / 2 - if (buy_yes_price and bid_yes_price) - else (buy_yes_price or 0.5) - ) - if buy_no_price is None: - buy_no_price = 1.0 - current_prob - - # 计算价格趋势 - prev_data = price_history.get(market_id, {}) - prev_prob = prev_data.get("price", current_prob) - prob_change = (current_prob - prev_prob) * 100 - trend_str = ( - f"▲{abs(prob_change):.0f}%" - if prob_change > 0.5 - else ( - f"▼{abs(prob_change):.0f}%" - if prob_change < -0.5 - else "" - ) - ) - - # 更新历史缓存 - price_history[market_id] = { - "price": current_prob, - "timestamp": datetime.now().isoformat(), - } - - # --- 预警收集 (自动推送逻辑) --- - # 严格触发条件: 价格必须处于 85-95¢ 区间 (真正的高概率信号) - yes_in_range = buy_yes_price and 0.85 <= buy_yes_price <= 0.95 - no_in_range = buy_no_price and 0.85 <= buy_no_price <= 0.95 - - # 50¢ 保护：价格接近 50% 说明市场无明确方向，跳过 - is_undecided = 0.45 <= current_prob <= 0.55 - - if (yes_in_range or no_in_range) and not is_undecided: - alert_key = f"alert_{market_id}_{int(current_prob * 100)}" - if alert_key not in pushed_signals: - # 获取温度符号（在此处定义以便后续使用） - temp_unit = weather_data.get("open-meteo", {}).get( - "unit", "celsius" - ) - temp_symbol = ( - "°F" if temp_unit == "fahrenheit" else "°C" - ) - - # 预测偏差分析 - if ref_temp: - city_pred_high = ref_temp # 记录到城市概览 - temp_match = re.search( - r"(\d+)(?:-(\d+))?°[FC]", question - ) - if temp_match: - low_b = int(temp_match.group(1)) - high_b = ( - int(temp_match.group(2)) - if temp_match.group(2) - else low_b - ) - diff = ref_temp - ((low_b + high_b) / 2) - # 偏差信息将在后面构建 msg 时统一添加 - - # 生成策略建议：仅保留模型一致提示 - if abs(diff) < 2 and current_prob > 0.7: - city_strategy_tips.append( - f"预测温度{ref_temp}{temp_symbol}落在{question}区间，市场与模型一致" - ) - - # 模拟下单 - 使用 Ask 价格（实际可成交价格） - if buy_yes_price and buy_yes_price > 0.5: - trigger_side = "Buy Yes" - trigger_price = int(buy_yes_price * 100) - else: - trigger_side = "Buy No" - trigger_price = ( - int(buy_no_price * 100) - if buy_no_price - else int((1 - current_prob) * 100) - ) - - # 构建预测文本 - forecast_text = ( - f"{ref_temp}{temp_symbol}" if ref_temp else "N/A" - ) - - # 构建简约版消息 - side_display = ( - "Buy No" if trigger_side == "Buy No" else "Buy Yes" - ) - msg = f"⚡ {question} ({target_date}): {side_display} {trigger_price}¢ | 预测:{forecast_text}" - - success = paper_trader.open_position( - market_id=market_id, - city=city, - option=question, - price=trigger_price, - side="YES" if trigger_side == "Buy Yes" else "NO", - amount_usd=5.0, - target_date=target_date, - predicted_temp=ref_temp, - ) - - # 添加模拟交易标签 - if success: - msg += " [🛒 $5.0 💡试探]" - - city_alerts.append( - { - "market": target_date or "今日", - "msg": msg, - "bought": success, - "amount": 5.0, - "confidence": "💡试探", - } - ) - pushed_signals[alert_key] = time.time() - if target_date: - city_target_date = target_date - - # C. 准备缓存数据 - temp_unit = weather_data.get("open-meteo", {}).get( - "unit", "celsius" - ) - temp_symbol = "°F" if temp_unit == "fahrenheit" else "°C" - city_local_time = ( - weather_data.get("open-meteo", {}) - .get("current", {}) - .get("local_time") - ) - - current_price = buy_yes_price if buy_yes_price else 0.5 - - # 计算价格趋势 - prev_data = price_history.get(market_id, {}) - prev_price = prev_data.get("price", current_price) - price_change_pct = ( - ((current_price - prev_price) / prev_price * 100) - if prev_price > 0 - else 0 - ) - - # 更新价格历史缓存 - price_history[market_id] = { - "price": current_price, - "timestamp": datetime.now().isoformat(), - } - - cache_entry = { - "city": city, - "full_title": event_title, - "option": question, - "prediction": f"{ref_temp}{temp_symbol}", - "price": int(current_price * 100), - "buy_yes": int(buy_yes_price * 100) if buy_yes_price else 0, - "buy_no": int(buy_no_price * 100) if buy_no_price else 0, - "url": f"https://polymarket.com/event/{market.get('slug')}", - "local_time": city_local_time, - "target_date": target_date, - "score": 0, - "rationale": "ACTIVE", - "trend": round(price_change_pct, 1), - } - - # --- 最终过滤器 (拦截垃圾信号) --- - - # 1. 过滤已锁定价格 (>= 98.5c) - if (buy_yes_price and buy_yes_price >= 0.985) or ( - buy_no_price and buy_no_price >= 0.985 - ): - cache_entry["rationale"] = "ENDED" - all_markets_cache[market_id] = cache_entry - continue - - # 2. 过滤已过期日期 (动态获取当前日期) - current_today = datetime.now().strftime("%Y-%m-%d") - if target_date and target_date < current_today: - cache_entry["rationale"] = "EXPIRED" - all_markets_cache[market_id] = cache_entry - continue - - # 3. 评分计算 - try: - signal = decision_engine.calculate_signal( - model_prediction=predictor.predict_ensemble([ref_temp]), - market_data={ - "orderbook": {}, - "price_history": [current_price], - "transactions": [], - }, - weather_consensus={"average_temp": ref_temp}, - whale_activity=None, - ) - cache_entry["score"] = signal.get("final_score", 0) - cache_entry["rationale"] = signal.get( - "recommendation", "ACTIVE" - ) - except Exception as e: - logger.error(f"计算信号失败 [{market_id}]: {e}") - cache_entry["score"] = 0 - cache_entry["rationale"] = "ERROR" - - all_markets_cache[market_id] = cache_entry - - # --- 预警收集 (自动推送逻辑) --- - if (buy_yes_price and 0.85 <= buy_yes_price <= 0.95) or ( - buy_no_price and 0.85 <= buy_no_price <= 0.95 - ): - alert_key = f"alert_{market_id}_range_85_95" - if alert_key not in pushed_signals: - # --- 基础参数识别 --- - is_categorical = len(ts) > 2 and active_tid - if is_categorical: - # 语义转换逻辑保持一致 - if buy_no_price and buy_no_price >= 0.85: - trigger_side = "Buy No" # 直接统一为 Buy No - trigger_price = int(buy_no_price * 100) - else: - trigger_side = "Buy Yes" - trigger_price = int(buy_yes_price * 100) - else: - trigger_side = ( - "Buy Yes" if buy_yes_price >= 0.85 else "Buy No" - ) - trigger_price = ( - int(buy_yes_price * 100) - if trigger_side == "Buy Yes" - else int(buy_no_price * 100) - ) - - # --- 智能动态仓位计算 --- - # 1. 获取 Open-Meteo 对目标日期的最高温预测 - predicted_high = None - weather_supports = False - daily_data = weather_data.get("open-meteo", {}).get( - "daily", {} - ) - if daily_data and target_date: - dates = daily_data.get("time", []) - max_temps = daily_data.get("temperature_2m_max", []) - for idx, d_str in enumerate(dates): - if target_date == d_str and idx < len( - max_temps - ): - predicted_high = max_temps[idx] - break - - # 2. 判断天气预测是否支持当前方向 - if predicted_high is not None: - # 解析选项的温度范围 (例如 "40-41°F" 或 "32°F or below") - temp_match = re.search( - r"(\d+)(?:-(\d+))?°[FC]", question - ) - if temp_match: - low_bound = int(temp_match.group(1)) - high_bound = ( - int(temp_match.group(2)) - if temp_match.group(2) - else low_bound - ) - - # 如果买 NO，天气预测应该在这个区间之外 - if trigger_side == "Buy No": - weather_supports = ( - predicted_high < low_bound - 2 - ) or (predicted_high > high_bound + 2) - else: # 买 YES - weather_supports = ( - low_bound - 2 - <= predicted_high - <= high_bound + 2 - ) - - # 3. 获取成交量信息 - market_volume = market.get("volume", 0) - if isinstance(market_volume, str): - try: - market_volume = float( - market_volume.replace("$", "").replace( - ",", "" - ) - ) - except: - market_volume = 0 - high_volume = market_volume >= 5000 # $5000+ 算高成交量 - - # --- Pro 级仓位决策系统 --- - # 1. 计算离结算剩余小时数 (假设气温市场在目标日期晚上 23:59 结算) - hours_to_settle = 24.0 - if target_date: - try: - settle_dt = datetime.strptime( - f"{target_date} 23:59:59", - "%Y-%m-%d %H:%M:%S", - ) - now_utc = datetime.utcnow() - diff = settle_dt - now_utc - hours_to_settle = diff.total_seconds() / 3600.0 - except: - pass - - # 2. 计算相对成交量比例 - total_daily_vol = sum( - [ - float( - str(m.get("volume", 0)) - .replace("$", "") - .replace(",", "") - ) - for m in city_markets - if ( - weather.extract_date_from_title( - m.get("event_title", "") - ) - or weather.extract_date_from_title( - m.get("question", "") - ) - ) - == target_date - ] - ) - market_vol = float( - str(market.get("volume", 0)) - .replace("$", "") - .replace(",", "") - ) - is_rel_high_vol = ( - (market_vol / total_daily_vol > 0.3) - if total_daily_vol > 0 - else False - ) - - # 3. 基础意向仓位 (基于置信度) - base_pos = 3.0 # 默认探路 - confidence_tag = "💡试探" - if ( - trigger_price >= 90 - and weather_supports - and high_volume - ): - base_pos, confidence_tag = 10.0, "🔥高置信" - elif trigger_price >= 90 and weather_supports: - base_pos, confidence_tag = 7.0, "⭐中置信" - elif trigger_price >= 92: - base_pos, confidence_tag = 5.0, "📌价格锁定" - - # 4. 仓位决策 - amount_usd, risk_reason = ( - risk_manager.calculate_position_size( - base_confidence_usd=base_pos, - hours_to_settle=hours_to_settle, - is_high_relative_volume=is_rel_high_vol, - ) - ) - - logger.info( - f"【Pro仓位】{city} {question} | " - f"基础:{base_pos}$ -> 最终:{amount_usd}$ | 原因:{risk_reason} | " - f"剩:{hours_to_settle:.1f}h" - ) - - # --- 模拟交易触发逻辑 --- - if amount_usd > 0: - side = "YES" if trigger_side == "Buy Yes" else "NO" - success = paper_trader.open_position( - market_id=market_id, - city=city, - option=question, - price=trigger_price, - side=side, - amount_usd=amount_usd, - target_date=target_date, - predicted_temp=predicted_high, - ) - if success: - risk_manager.record_trade(amount_usd) - else: - # 如果被风控拦截（金额为0），则不进行任何推送，避免刷屏 - success = False - logger.info( - f"Skipping alert for {question}: {risk_reason}" - ) - continue - - # 构建预测温度显示文本 - temp_unit = weather_data.get("open-meteo", {}).get( - "unit", "celsius" - ) - temp_symbol = ( - "°F" if temp_unit == "fahrenheit" else "°C" - ) - forecast_text = ( - f"{predicted_high}{temp_symbol}" - if predicted_high - else "N/A" - ) - - # 构建简约版消息: ⚡ {question} ({date}): {side} {price}¢ | 预测:{forecast} [🛒 ${amount} {tag}] - side_display = trigger_side - msg = ( - f"⚡ {question} ({target_date}): {side_display} {trigger_price}¢ | " - f"预测:{forecast_text} [🛒 ${amount_usd} {confidence_tag}]" - ) - - city_alerts.append( - { - "type": "price", - "market": f"{target_date or '今日'}", - "msg": msg, - "bought": success, - "amount": amount_usd, - "confidence": confidence_tag, - } - ) - pushed_signals[alert_key] = time.time() - - # 3. 信号暂存 - cached_signals[market_id] = cache_entry - - # E. 统一发送城市汇总通知 (使用新 Pro 模板) - if city_alerts: - # 去重策略建议 - unique_tips = list(dict.fromkeys(city_strategy_tips)) - # 获取 METAR 数据（仅当天结算的市场才显示） - today_str = datetime.now().strftime("%Y-%m-%d") - # 检查是否有当天结算的市场 - has_today_market = any( - a.get("market") == today_str or a.get("market") == "今日" - for a in city_alerts - ) - metar_data = ( - weather_data.get("metar") if has_today_market else None - ) - # notifier.send_combined_alert( - # city=city, - # alerts=city_alerts, - # local_time=city_local_time, - # forecast_temp=f"{city_pred_high}{temp_symbol}" - # if city_pred_high - # else "N/A", - # total_volume=city_total_vol, - # brackets_count=len(city_markets), - # strategy_tips=unique_tips, - # metar_data=metar_data, - # ) - - except Exception as e: - logger.error(f"分析城市 {city} 时出错: {e}") - # --- 每处理完一个城市，立即更新 JSON 文件 --- - try: - # --- 周期性结算：保存高价值信号 --- - active_signals = [] - for mid, entry in all_markets_cache.items(): - # Relaxed filtering: Let the bot decide, but mark ENDED - rationale = entry.get("rationale") - if rationale == "ERROR": - continue - - target_dt = entry.get("target_date") - # Only filter out truly ancient history - if target_dt and target_dt < "2026-02-01": - continue - - active_signals.append(entry) - - # 按分数排序 - active_signals.sort(key=lambda x: x.get("score", 0), reverse=True) - - with open("data/active_signals.json", "w", encoding="utf-8") as f: - json.dump(active_signals, f, ensure_ascii=False, indent=4) - - logger.info( - f"已更新活跃信号库，包含 {len(active_signals)} 个有效信号。" - ) - - # 2. 更新全量市场缓存 - try: - with open("data/all_markets.json", "r", encoding="utf-8") as f: - existing_markets = json.load(f) - except: - existing_markets = {} - - existing_markets.update(all_markets_cache) - - # 清理过期日期 - today_str = datetime.now().strftime("%Y-%m-%d") - cleaned_markets = {} - for k, v in existing_markets.items(): - t_date = v.get("target_date") - if not t_date or t_date >= today_str: - cleaned_markets[k] = v - - with open("data/all_markets.json", "w", encoding="utf-8") as f: - json.dump(cleaned_markets, f, ensure_ascii=False, indent=2) - - # 3. 保存推送记录 - with open("data/pushed_signals.json", "w", encoding="utf-8") as f: - json.dump(pushed_signals, f, ensure_ascii=False) - - # 3.5 保存价格历史（用于趋势计算） - with open(PRICE_HISTORY_FILE, "w", encoding="utf-8") as f: - json.dump(price_history, f, ensure_ascii=False) - - # --- 4. 更新模拟仓位盈亏 --- - price_snapshot = {} - for mid, entry in all_markets_cache.items(): - price_snapshot[mid] = {"price": entry["price"]} - paper_trader.update_pnl(price_snapshot) - - # --- 5. 每日收益总结推送 (北京时间 23:55 - 00:05 之间发送) --- - now_bj = datetime.utcnow() + timedelta(hours=8) - if now_bj.hour == 23 and now_bj.minute >= 50: - summary_key = f"daily_pnl_{now_bj.strftime('%Y%m%d')}" - if summary_key not in pushed_signals: - # 构造总结消息 - total_cost = 0 - total_pnl = 0 - data = paper_trader._load_data() - pos_list = data.get("positions", {}) - - if pos_list: - report = [ - f"📊 每日模拟仓结算总结 ({now_bj.strftime('%Y-%m-%d')})\n" - + "═" * 15 - ] - for p in pos_list.values(): - if p["status"] == "OPEN": - total_cost += p["cost_usd"] - total_pnl += p.get("pnl_usd", 0) - - report.append( - f"💳 可用余额: ${data.get('balance', 0):.2f}" - ) - report.append( - f"💰 今日累计投入: ${total_cost:.2f}" - ) - report.append( - f"📈 累计浮动盈亏: {total_pnl:+.2f}$" - ) - # notifier._send_message("\n".join(report)) - pushed_signals[summary_key] = time.time() - - except Exception as e: - logger.error(f"即时保存数据失败: {e}") - - logger.info("本轮扫描结束。等待 5 分钟...") - time.sleep(300) - - except KeyboardInterrupt: - logger.info("收到关机指令，正在退出...") - except Exception as e: - logger.exception(f"系统运行出错: {e}") - - -if __name__ == "__main__": - main() diff --git a/run.py b/run.py index f7a34e4d..08d9304e 100644 --- a/run.py +++ b/run.py @@ -1,48 +1,24 @@ -import threading -import time -import sys import subprocess import os +import sys from loguru import logger -def run_monitor(): - """启动监控引擎模块 (main.py)""" - logger.info("📡 正在启动后台监控引擎 (主动预警模式)...") - cmd = [sys.executable, "main.py"] - subprocess.run(cmd) - -def run_bot(): - """启动电报交互模块 (bot_listener.py)""" - logger.info("🤖 正在启动电报指令监听器 (被动查询模式)...") - cmd = [sys.executable, "bot_listener.py"] - # 设置工作目录，确保导入正常 - subprocess.run(cmd, cwd=os.getcwd()) def main(): - logger.info("🌟 PolyWeather 全功能系统正在初始化...") - - # 创建共享文件夹 (如果不存在) - if not os.path.exists("data"): - os.makedirs("data") + logger.info("🌡️ PolyWeather 天气查询机器人启动中...") - # 创建两个线程并行运行 - monitor_thread = threading.Thread(target=run_monitor, daemon=True) - bot_thread = threading.Thread(target=run_bot, daemon=True) + # 创建数据目录 + os.makedirs("data", exist_ok=True) - # 启动线程 - # monitor_thread.start() - bot_thread.start() - - logger.success("🚀 系统已上线（天气查询模式）！") - logger.info("已暂停监控引擎和自动发现市场功能。") - logger.info("现在仅支持直接查询各城市实时天气与 Open-Meteo 预测。") + # 直接运行 bot_listener + cmd = [sys.executable, "bot_listener.py"] + logger.success("🚀 已上线！等待 Telegram 指令...") try: - # 保持主进程运行 - while True: - time.sleep(1) + subprocess.run(cmd, cwd=os.getcwd()) except KeyboardInterrupt: logger.warning("停止运行...") + if __name__ == "__main__": main() diff --git a/src/analysis/__init__.py b/src/analysis/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/analysis/orderbook_analyzer.py b/src/analysis/orderbook_analyzer.py deleted file mode 100644 index 4783d1d5..00000000 --- a/src/analysis/orderbook_analyzer.py +++ /dev/null @@ -1,95 +0,0 @@ -from loguru import logger - -class OrderbookAnalyzer: - """ - 分析目标: 评估市场供需平衡和流动性 - """ - def __init__(self, config=None): - self.config = config or {} - self.wall_threshold = self.config.get("wall_threshold", 500) # 单笔订单超过此值为墙 - logger.info("Initializing Orderbook Analyzer...") - - def assess_liquidity(self, orderbook, side="ask"): - """ - 分析流动性深度 (基于前 3 档) - """ - orders = orderbook.get('asks' if side == "ask" else 'bids', []) - if not orders: - return "枯竭", 0 - - # 前 3 档总量 (Polymarket 通常返回价格字符串) - depth = sum(float(o.get("size", 0)) for o in orders[:3]) - - if depth < 50: - return "稀薄", depth - elif depth < 500: - return "正常", depth - else: - return "充裕", depth - - def analyze(self, orderbook): - """ - 增强版订单簿分析：集成深度与 Spread 评估 - """ - bids = orderbook.get('bids', []) - asks = orderbook.get('asks', []) - - if not bids or not asks: - return { - "signal": "NEUTRAL", - "confidence": 0.0, - "tradeable": False, - "reason": "缺乏双边报价", - "liquidity": "枯竭", - "spread": 1.0 - } - - # 1. 计算核心指标 - best_bid = float(bids[0].get('price', 0)) - best_ask = float(asks[0].get('price', 0)) - spread = abs(best_ask - best_bid) - mid_price = (best_ask + best_bid) / 2 - - # 2. 评估流动性 - ask_liq, ask_depth = self.assess_liquidity(orderbook, "ask") - bid_liq, bid_depth = self.assess_liquidity(orderbook, "bid") - - # 3. 交易可行性判定 (Spread <= 10c 且 深度 >= $50) - is_tradeable = (spread <= 0.10) and (ask_depth >= 50 or bid_depth >= 50) - - # 4. Imbalance 计算 - bid_volume = sum([float(b.get('size', 0)) for b in bids]) - ask_volume = sum([float(a.get('size', 0)) for a in asks]) - imbalance = bid_volume / ask_volume if ask_volume > 0 else 0 - - result = { - "best_bid": best_bid, - "best_ask": best_ask, - "mid_price": mid_price, - "spread": round(spread, 4), - "ask_depth": round(ask_depth, 2), - "bid_depth": round(bid_depth, 2), - "liquidity": ask_liq if ask_depth < bid_depth else bid_liq, - "tradeable": is_tradeable, - "imbalance": imbalance, - "signal": "NEUTRAL", - "confidence": 0.5 - } - - # 5. 信号修正 - if is_tradeable: - if imbalance > 2.5: - result["signal"] = "BULLISH" - result["confidence"] = 0.75 - elif imbalance < 0.4: - result["signal"] = "BEARISH" - result["confidence"] = 0.75 - else: - result["confidence"] = 0.1 # 不建议交易 - - return result - -def analyze_orderbook(orderbook): - """兼容旧接口的便捷函数""" - analyzer = OrderbookAnalyzer() - return analyzer.analyze(orderbook) diff --git a/src/analysis/technical_indicators.py b/src/analysis/technical_indicators.py deleted file mode 100644 index 2cca8633..00000000 --- a/src/analysis/technical_indicators.py +++ /dev/null @@ -1,146 +0,0 @@ -import numpy as np -from loguru import logger - -class TechnicalIndicators: - """ - 技术指标计算 - RSI, 布林带等 - """ - - def __init__(self): - logger.info("Initializing Technical Indicators...") - - def calculate_rsi(self, prices: list, period: int = 14) -> float: - """ - 计算相对强弱指标 (RSI) - - Args: - prices: 价格历史列表 - period: RSI周期，默认14 - - Returns: - float: RSI值 (0-100) - """ - if len(prices) < period + 1: - logger.debug("Insufficient data for RSI calculation") - return 50.0 # 返回中性值 - - prices = np.array(prices) - deltas = np.diff(prices) - - gains = np.where(deltas > 0, deltas, 0) - losses = np.where(deltas < 0, -deltas, 0) - - avg_gain = np.mean(gains[-period:]) - avg_loss = np.mean(losses[-period:]) - - if avg_loss == 0: - return 100.0 - - rs = avg_gain / avg_loss - rsi = 100 - (100 / (1 + rs)) - - logger.debug(f"RSI({period}): {rsi:.2f}") - return rsi - - def calculate_bollinger_bands(self, prices: list, period: int = 20, std_dev: float = 2.0) -> dict: - """ - 计算布林带 - - Args: - prices: 价格历史列表 - period: 移动平均周期 - std_dev: 标准差倍数 - - Returns: - dict: 包含上轨、中轨、下轨 - """ - if len(prices) < period: - logger.debug("Insufficient data for Bollinger Bands") - return {"upper": None, "middle": None, "lower": None} - - prices = np.array(prices[-period:]) - middle = np.mean(prices) - std = np.std(prices) - - upper = middle + std_dev * std - lower = middle - std_dev * std - - return { - "upper": upper, - "middle": middle, - "lower": lower, - "std": std - } - - def calculate_momentum(self, prices: list, period: int = 10) -> float: - """ - 计算价格动量 - - Args: - prices: 价格历史 - period: 动量周期 - - Returns: - float: 动量值 (当前价格 / N周期前价格 - 1) - """ - if len(prices) < period + 1: - return 0.0 - - current = prices[-1] - past = prices[-period - 1] - - if past == 0: - return 0.0 - - momentum = (current / past) - 1 - return momentum - - def get_signal(self, prices: list) -> dict: - """ - 综合技术指标信号 - - Returns: - dict: 包含信号和分数 - """ - rsi = self.calculate_rsi(prices) - bb = self.calculate_bollinger_bands(prices) - momentum = self.calculate_momentum(prices) - - # RSI信号 - if rsi > 70: - rsi_signal = "OVERBOUGHT" - rsi_score = 0.3 # 超买，看跌 - elif rsi < 30: - rsi_signal = "OVERSOLD" - rsi_score = 0.8 # 超卖，看涨 - else: - rsi_signal = "NEUTRAL" - rsi_score = 0.5 - - # 布林带信号 - if bb["upper"] and len(prices) > 0: - current_price = prices[-1] - if current_price > bb["upper"]: - bb_signal = "ABOVE_UPPER" - bb_score = 0.7 # 突破上轨，强势 - elif current_price < bb["lower"]: - bb_signal = "BELOW_LOWER" - bb_score = 0.3 # 跌破下轨，弱势 - else: - bb_signal = "WITHIN_BANDS" - bb_score = 0.5 - else: - bb_signal = "NO_DATA" - bb_score = 0.5 - - # 综合分数 - combined_score = (rsi_score * 0.5 + bb_score * 0.3 + - (0.5 + momentum * 2) * 0.2) # momentum 转换为 0-1 - combined_score = max(0, min(1, combined_score)) - - return { - "rsi": {"value": rsi, "signal": rsi_signal, "score": rsi_score}, - "bollinger": {"bands": bb, "signal": bb_signal, "score": bb_score}, - "momentum": momentum, - "combined_score": combined_score - } diff --git a/src/analysis/volume_analyzer.py b/src/analysis/volume_analyzer.py deleted file mode 100644 index 5d12e4b2..00000000 --- a/src/analysis/volume_analyzer.py +++ /dev/null @@ -1,135 +0,0 @@ -import numpy as np -from loguru import logger - -class VolumeAnalyzer: - """ - 交易量异常检测 - 识别聪明钱和市场转折点 - """ - - def __init__(self, config=None): - self.config = config or {} - self.volume_threshold = self.config.get("volume_threshold", 2.0) # 2倍标准差 - self.large_order_threshold = self.config.get("large_order_threshold", 1000) # $1000 - logger.info("Initializing Volume Analyzer...") - - def detect_volume_spike(self, volume_history: list) -> dict: - """ - 检测成交量异常放大 - - Args: - volume_history: 历史成交量列表 - - Returns: - dict: 包含信号和置信度 - """ - if len(volume_history) < 24: - return {"signal": "INSUFFICIENT_DATA", "score": 0.5} - - recent_volume = np.array(volume_history[-24:]) # 最近24小时 - historical_volume = np.array(volume_history[:-24]) - - if len(historical_volume) == 0: - return {"signal": "INSUFFICIENT_DATA", "score": 0.5} - - avg_volume = np.mean(historical_volume) - std_volume = np.std(historical_volume) - - recent_avg = np.mean(recent_volume) - - # 计算Z-score - if std_volume > 0: - z_score = (recent_avg - avg_volume) / std_volume - else: - z_score = 0 - - logger.debug(f"Volume Z-score: {z_score:.2f}") - - if z_score > self.volume_threshold: - return { - "signal": "VOLUME_SPIKE", - "score": min(0.9, 0.5 + z_score * 0.1), - "z_score": z_score, - "interpretation": "成交量异常放大，可能有新信息进入市场" - } - elif z_score < -self.volume_threshold: - return { - "signal": "VOLUME_DRY", - "score": 0.3, - "z_score": z_score, - "interpretation": "成交量萎缩，市场观望" - } - - return {"signal": "NORMAL", "score": 0.5, "z_score": z_score} - - def detect_large_orders(self, transactions: list) -> dict: - """ - 检测大额订单 (聪明钱信号) - - Args: - transactions: 交易列表，每个包含 size, side, price - - Returns: - dict: 大额订单分析结果 - """ - large_buys = [] - large_sells = [] - - for tx in transactions: - size = tx.get("size", 0) - side = tx.get("side", "").upper() - - if size >= self.large_order_threshold: - if side == "BUY": - large_buys.append(tx) - elif side == "SELL": - large_sells.append(tx) - - total_large_buy = sum(t.get("size", 0) for t in large_buys) - total_large_sell = sum(t.get("size", 0) for t in large_sells) - - logger.debug(f"Large buys: ${total_large_buy:.2f}, Large sells: ${total_large_sell:.2f}") - - if total_large_buy > total_large_sell * 2: - return { - "signal": "SMART_MONEY_BUY", - "score": 0.8, - "large_buy_volume": total_large_buy, - "large_sell_volume": total_large_sell, - "interpretation": "大户在积极买入，跟随机会" - } - elif total_large_sell > total_large_buy * 2: - return { - "signal": "SMART_MONEY_SELL", - "score": 0.2, - "large_buy_volume": total_large_buy, - "large_sell_volume": total_large_sell, - "interpretation": "大户在抛售，风险警告" - } - - return { - "signal": "NEUTRAL", - "score": 0.5, - "large_buy_volume": total_large_buy, - "large_sell_volume": total_large_sell - } - - def analyze(self, volume_history: list, transactions: list = None) -> dict: - """ - 综合分析交易量 - """ - volume_signal = self.detect_volume_spike(volume_history) - - if transactions: - order_signal = self.detect_large_orders(transactions) - else: - order_signal = {"signal": "NO_DATA", "score": 0.5} - - # 综合评分 - combined_score = (volume_signal.get("score", 0.5) * 0.6 + - order_signal.get("score", 0.5) * 0.4) - - return { - "volume_signal": volume_signal, - "order_signal": order_signal, - "combined_score": combined_score - } diff --git a/src/analysis/whale_tracker.py b/src/analysis/whale_tracker.py deleted file mode 100644 index 9238f156..00000000 --- a/src/analysis/whale_tracker.py +++ /dev/null @@ -1,59 +0,0 @@ -from loguru import logger -from typing import List, Dict -from src.data_collection.onchain_tracker import OnchainTracker - -class WhaleTracker: - """ - 大户行为分析模块 - """ - def __init__(self, config: dict, tracker: OnchainTracker): - self.config = config - self.tracker = tracker - logger.info("Initializing Whale Tracker...") - - def analyze_market_whales(self, market_id: str) -> Dict: - """ - 分析特定市场的鲸鱼行为 - """ - large_trades = self.tracker.get_large_transactions(market_id) - - if not large_trades: - return {"bullish": False, "signal": "NEUTRAL", "reason": "No whale activity detected"} - - buy_value = 0 - sell_value = 0 - - for trade in large_trades: - side = trade.get("side", "").upper() - value = trade.get("value", 0) - - if side == "BUY": - buy_value += value - else: - sell_value += value - - # 判断情绪 - if buy_value > sell_value * 2: - return { - "bullish": True, - "signal": "STRONG_ACCUMULATION", - "buy_value": buy_value, - "sell_value": sell_value, - "reason": "Whales are heavily buying" - } - elif sell_value > buy_value * 2: - return { - "bullish": False, - "signal": "STRONG_DISTRIBUTION", - "buy_value": buy_value, - "sell_value": sell_value, - "reason": "Whales are heavily selling" - } - - return { - "bullish": buy_value > sell_value, - "signal": "MODERATE", - "buy_value": buy_value, - "sell_value": sell_value, - "reason": "Mixed whale activity" - } diff --git a/src/data_collection/onchain_tracker.py b/src/data_collection/onchain_tracker.py deleted file mode 100644 index 4825950a..00000000 --- a/src/data_collection/onchain_tracker.py +++ /dev/null @@ -1,56 +0,0 @@ -from loguru import logger -from typing import List, Dict, Optional -from src.data_collection.polymarket_api import PolymarketClient - -class OnchainTracker: - """ - 追踪 Polymarket 上的大额交易和钱包动向 - 主要通过 Polymarket API 获取交易历史，并模拟链上分析逻辑 - """ - def __init__(self, config: dict, client: PolymarketClient): - self.config = config - self.client = client - self.whale_threshold = self.config.get("whale_threshold", 5000) # $5000 以上视为鲸鱼 - logger.info(f"Initializing Onchain Tracker (Whale Threshold: ${self.whale_threshold})") - - def get_large_transactions(self, market_id: str, limit: int = 100) -> List[Dict]: - """ - 获取特定市场的历史大额交易 - """ - trades = self.client.get_trades(market_id=market_id, limit=limit) - if not trades: - return [] - - # 过滤大额交易 (Polymarket API 返回的格式可能需要根据实际调整) - # 假设格式: [{"price": 0.9, "size": 10000, "side": "BUY", "maker": "0x...", "taker": "0x..."}] - large_trades = [] - for trade in trades: - size = float(trade.get("size", 0)) - price = float(trade.get("price", 0)) - value = size * price - - if value >= self.whale_threshold: - trade["value"] = value - large_trades.append(trade) - - return large_trades - - def get_whale_positions(self, market_id: str) -> Dict[str, float]: - """ - 估算大户在某个市场的持仓情况 - 注意：这只是基于最近交易的估算，真实持仓需要查询链上合约 - """ - trades = self.get_large_transactions(market_id, limit=500) - whale_holdings = {} - - for trade in trades: - wallet = trade.get("proxyWallet") or trade.get("maker") or "unknown" - side = trade.get("side", "").upper() - size = float(trade.get("size", 0)) - - if side == "BUY": - whale_holdings[wallet] = whale_holdings.get(wallet, 0) + size - else: - whale_holdings[wallet] = whale_holdings.get(wallet, 0) - size - - return whale_holdings diff --git a/src/data_collection/polymarket_api.py b/src/data_collection/polymarket_api.py deleted file mode 100644 index d186a27f..00000000 --- a/src/data_collection/polymarket_api.py +++ /dev/null @@ -1,291 +0,0 @@ -import os -import requests -import time -import re -from typing import Dict, List, Optional -from loguru import logger -from datetime import datetime -from concurrent.futures import ThreadPoolExecutor - -class PolymarketClient: - """ - Polymarket API Client (Pure REST API version) - Directly uses Gamma API and CLOB REST API without py-clob-client dependency. - """ - - def __init__(self, config: Dict): - self.clob_url = config.get("base_url", "https://clob.polymarket.com") - self.gamma_url = "https://gamma-api.polymarket.com" - self.timeout = config.get("timeout", 20) - self.session = requests.Session() - - # Cache mechanism - self._weather_markets_cache = [] - self._last_discovery_time = 0 - self._cache_ttl = 300 # 5 minutes cache - - # Proxy settings (automatically read from environment) - proxy = os.getenv("HTTPS_PROXY") or os.getenv("HTTP_PROXY") - if proxy: - self.session.proxies = {"http": proxy, "https": proxy} - logger.info(f"Requests session using proxy: {proxy}") - - # Set common User-Agent and headers - self.session.headers.update( - { - "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36", - "Accept": "application/json", - "Content-Type": "application/json" - } - ) - - self.api_key = config.get("api_key") - if self.api_key: - self.session.headers.update({"POLY_API_KEY": self.api_key}) - - logger.info(f"Polymarket REST Client initialized. CLOB: {self.clob_url}, Gamma: {self.gamma_url}") - - def get_markets(self, next_cursor: str = None) -> Optional[Dict]: - """Fetch markets list via CLOB REST API""" - try: - params = {} - if next_cursor: - params["next_cursor"] = next_cursor - resp = self.session.get(f"{self.clob_url}/markets", params=params, timeout=self.timeout) - return resp.json() if resp.status_code == 200 else None - except Exception as e: - logger.debug(f"get_markets failed: {e}") - return None - - def get_market(self, market_id: str) -> Optional[Dict]: - """Fetch market details via CLOB REST API""" - try: - resp = self.session.get(f"{self.clob_url}/markets/{market_id}", timeout=self.timeout) - return resp.json() if resp.status_code == 200 else None - except Exception as e: - logger.debug(f"get_market failed: {e}") - return None - - def get_price(self, token_id: str, side: str = "ask") -> Optional[float]: - """Fetch real-time price for a token via CLOB REST API""" - try: - # Correct CLOB Mapping: - # 'sell' side price is the ASK (price you pay to BUY) - # 'buy' side price is the BID (price you get to SELL) - clob_side = "sell" if side.lower() in ["ask", "buy"] else "buy" - resp = self.session.get( - f"{self.clob_url}/price", - params={"token_id": token_id, "side": clob_side}, - timeout=10 - ) - data = resp.json() - return float(data.get("price", 0)) if resp.status_code == 200 else None - except Exception as e: - logger.debug(f"get_price failed ({token_id}): {e}") - return None - - def get_orderbook(self, token_id: str) -> Optional[Dict]: - """Fetch orderbook for a token via CLOB REST API""" - try: - resp = self.session.get( - f"{self.clob_url}/book", params={"token_id": token_id}, timeout=10 - ) - return resp.json() if resp.status_code == 200 else None - except Exception as e: - logger.debug(f"get_orderbook failed ({token_id}): {e}") - return None - - def get_buy_prices(self, yes_token_id: str, no_token_id: str) -> Optional[Dict]: - """Fetch buy prices for both YES and NO tokens""" - try: - # Buy Yes = Ask price of YES token - buy_yes = self.get_price(yes_token_id, "BUY") - # Buy No = Ask price of NO token - buy_no = self.get_price(no_token_id, "BUY") - - if buy_yes is not None and buy_no is not None: - return {"buy_yes": buy_yes, "buy_no": buy_no} - except Exception as e: - logger.debug(f"get_buy_prices failed: {e}") - return None - - def get_multiple_prices(self, token_requests: List[Dict]) -> Dict[str, float]: - """Batch fetch prices for multiple tokens using ThreadPoolExecutor""" - if not token_requests: - return {} - - all_prices = {} - - def robust_float(val): - try: return float(val) - except: return 0.0 - - def fetch_single(req): - tid = req["token_id"] - side = req.get("side", "ask").lower() - # To get ASK (price to buy), request 'sell' side - # To get BID (price to sell), request 'buy' side - api_side = "sell" if side == "ask" else "buy" - val = self.get_price(tid, api_side) - if val: - return f"{tid}:{side.lower()}", val - return None - - with ThreadPoolExecutor(max_workers=5) as executor: - results = list(executor.map(fetch_single, token_requests)) - - for res in results: - if res: - key, val = res - all_prices[key] = val - - return all_prices - - def get_midpoint(self, token_id: str) -> Optional[float]: - """Fetch midpoint price via CLOB REST API""" - try: - resp = self.session.get(f"{self.clob_url}/midpoint", params={"token_id": token_id}, timeout=10) - data = resp.json() - return float(data.get("mid", 0)) if resp.status_code == 200 else None - except: - return None - - def discover_weather_markets(self) -> list: - """Scan Gamma API for all weather-related markets with prioritized search and city targeting""" - # Cache check - current_time = time.time() - if self._weather_markets_cache and (current_time - self._last_discovery_time < self._cache_ttl): - logger.debug(f"Using cached market list ({len(self._weather_markets_cache)} items)") - return self._weather_markets_cache - - logger.info("📡 Scanning Polymarket via Gamma API for weather markets...") - all_weather_markets = [] - seen_keys = set() - - # 1. Target newest markets by query and ID sorting - search_queries = ["highest temperature", "temperature in", "daily weather"] - - try: - # Use multiple offsets to find more historical/diverse markets - for offset in [0, 500, 1000]: - for query in search_queries: - logger.debug(f"Searching with query: {query} (offset {offset})") - params = { - "query": query, - "active": "true", - "limit": 500, - "offset": offset, - "order": "id", - "ascending": "false" - } - resp = self.session.get(f"{self.gamma_url}/markets", params=params, timeout=self.timeout) - if resp.status_code == 200: - markets = resp.json() - logger.debug(f"Query '{query}' returned {len(markets)} markets") - for m in markets: - q = m.get("question", "").lower() - slug = m.get("slug", "").lower() - - # Filter for weather markets (Broadened) - is_weather = any(k in q or k in slug for k in [ - "highest temperature", "highest-temperature", - "temperature in", "temperature-in", - "daily weather", "daily-weather", - "weather", "气温", "温度" - ]) - if is_weather: - c_id = m.get("conditionId") - t_ids = m.get("clobTokenIds") - active_id = m.get("activeTokenId") - - # Robust JSON parsing for clobTokenIds string - if isinstance(t_ids, str) and t_ids.startswith("["): - try: - import json - t_ids = json.loads(t_ids) - except: - pass - - # For Neg Risk markets, activeTokenId might be missing in list view - # If we have clobTokenIds, we can work with it - if not t_ids: - continue - - if not active_id and isinstance(t_ids, list) and len(t_ids) > 0: - active_id = t_ids[0] # Assume first is YES - - if not active_id: - continue - - unique_key = f"{c_id}_{active_id}" - if unique_key not in seen_keys: - logger.debug(f"Found weather segment: {q}") - all_weather_markets.append({ - "condition_id": c_id, - "question": m.get("question"), - "active_token_id": active_id, - "outcome_index": t_ids.index(active_id) if isinstance(t_ids, list) and active_id in t_ids else 0, - "tokens": t_ids, - "prices": m.get("outcomePrices"), - "event_title": m.get("description", "")[:100], - "slug": m.get("slug"), - "group_id": m.get("negRiskMarketID") - }) - seen_keys.add(unique_key) - else: - logger.debug(f"Query '{query}' failed with status {resp.status_code}") - - if len(all_weather_markets) > 50: - break - - logger.info(f"Discovery complete: Found {len(all_weather_markets)} weather segments.") - self._weather_markets_cache = all_weather_markets - self._last_discovery_time = current_time - return all_weather_markets - - except Exception as e: - logger.error(f"Market discovery failed: {e}") - return [] - - except Exception as e: - logger.error(f"Market discovery failed: {e}") - return [] - - except Exception as e: - logger.error(f"Market discovery failed: {e}") - return [] - - def get_weather_markets(self) -> list: - return self.discover_weather_markets() - - def find_weather_market(self, city: str, date_str: str = None) -> Optional[Dict]: - markets = self.get_weather_markets() - for m in markets: - # Match against question, title AND slug - content = (str(m.get("question", "")) + str(m.get("event_title", "")) + str(m.get("slug", ""))).lower() - if city.lower() in content: - if date_str: - if date_str.lower() in content: return m - else: - return m - return None - - def get_weather_event_markets(self, city: str) -> list: - all_markets = self.get_weather_markets() - return [ - m for m in all_markets - if city.lower() in (str(m.get("question", "")) + str(m.get("event_title", "")) + str(m.get("slug", ""))).lower() - ] - - # --- Trading Stubs (Real trading requires signing, which is disabled in pure REST mode) --- - def create_order(self, *args, **kwargs) -> Optional[Dict]: - logger.warning("create_order: Real trading is disabled in pure REST mode. Please use paper trading.") - return None - - def cancel_order(self, *args, **kwargs) -> Optional[Dict]: - logger.warning("cancel_order: Real trading is disabled in pure REST mode.") - return None - - def get_orders(self, *args, **kwargs) -> Optional[Dict]: - logger.warning("get_orders: Real trading is disabled in pure REST mode.") - return None diff --git a/src/models/__init__.py b/src/models/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/models/statistical_model.py b/src/models/statistical_model.py deleted file mode 100644 index df31e956..00000000 --- a/src/models/statistical_model.py +++ /dev/null @@ -1,285 +0,0 @@ -import numpy as np -import pandas as pd -from typing import Dict, List, Optional, Tuple -from datetime import datetime -from loguru import logger - -try: - from statsmodels.tsa.arima.model import ARIMA - HAS_STATSMODELS = True -except ImportError: - HAS_STATSMODELS = False - logger.debug("statsmodels not installed, ARIMA model unavailable") - -try: - from sklearn.ensemble import RandomForestRegressor - from sklearn.model_selection import train_test_split - HAS_SKLEARN = True -except ImportError: - HAS_SKLEARN = False - logger.debug("scikit-learn not installed, ML models unavailable") - - -class TemperaturePredictor: - """ - Temperature prediction model using statistical and ML methods - - Supports: - - ARIMA for time series prediction - - Random Forest for feature-based prediction - - Ensemble of both methods - """ - - def __init__(self, config: dict = None): - self.config = config or {} - self.arima_order = self.config.get("arima_order", (5, 1, 2)) - self.rf_estimators = self.config.get("rf_estimators", 100) - - self.arima_model = None - self.rf_model = None - self.is_trained = False - - logger.info("Temperature Predictor initialized") - - def prepare_features(self, data: pd.DataFrame) -> pd.DataFrame: - """ - Prepare features for ML model - - Args: - data: DataFrame with temperature history - - Returns: - DataFrame: Feature-engineered data - """ - df = data.copy() - - # Time-based features - if 'date' in df.columns: - df['date'] = pd.to_datetime(df['date']) - df['day_of_year'] = df['date'].dt.dayofyear - df['month'] = df['date'].dt.month - df['day_of_week'] = df['date'].dt.dayofweek - - # Lag features - if 'temp' in df.columns: - for lag in [1, 2, 3, 7, 14]: - df[f'temp_lag_{lag}'] = df['temp'].shift(lag) - - # Rolling statistics - df['temp_rolling_mean_7'] = df['temp'].rolling(window=7).mean() - df['temp_rolling_std_7'] = df['temp'].rolling(window=7).std() - df['temp_rolling_mean_14'] = df['temp'].rolling(window=14).mean() - - # Drop NaN rows created by lag features - df = df.dropna() - - return df - - def train_arima(self, temperature_series: List[float]) -> bool: - """ - Train ARIMA model on temperature time series - - Args: - temperature_series: List of historical temperatures - - Returns: - bool: Success status - """ - if not HAS_STATSMODELS: - logger.error("statsmodels required for ARIMA training") - return False - - if len(temperature_series) < 30: - logger.warning("Insufficient data for ARIMA training (need 30+ points)") - return False - - try: - series = np.array(temperature_series) - model = ARIMA(series, order=self.arima_order) - self.arima_model = model.fit() - logger.info(f"ARIMA model trained. AIC: {self.arima_model.aic:.2f}") - return True - except Exception as e: - logger.error(f"ARIMA training failed: {e}") - return False - - def train_random_forest(self, - features: pd.DataFrame, - target_col: str = 'temp') -> bool: - """ - Train Random Forest model - - Args: - features: Feature DataFrame - target_col: Target column name - - Returns: - bool: Success status - """ - if not HAS_SKLEARN: - logger.error("scikit-learn required for Random Forest training") - return False - - if len(features) < 50: - logger.warning("Insufficient data for RF training (need 50+ rows)") - return False - - try: - # Prepare data - feature_cols = [c for c in features.columns - if c not in [target_col, 'date', 'datetime']] - - X = features[feature_cols].values - y = features[target_col].values - - # Train-test split - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=42 - ) - - # Train model - self.rf_model = RandomForestRegressor( - n_estimators=self.rf_estimators, - random_state=42, - n_jobs=-1 - ) - self.rf_model.fit(X_train, y_train) - - # Evaluate - train_score = self.rf_model.score(X_train, y_train) - test_score = self.rf_model.score(X_test, y_test) - - logger.info(f"Random Forest trained. Train R²: {train_score:.4f}, Test R²: {test_score:.4f}") - - # Store feature names - self.feature_names = feature_cols - - return True - except Exception as e: - logger.error(f"Random Forest training failed: {e}") - return False - - def predict_arima(self, steps: int = 1) -> Optional[Dict]: - """ - Make prediction using ARIMA model - - Args: - steps: Number of steps to forecast - - Returns: - dict: Prediction with confidence interval - """ - if self.arima_model is None: - logger.warning("ARIMA model not trained") - return None - - try: - forecast = self.arima_model.forecast(steps=steps) - conf_int = self.arima_model.get_forecast(steps=steps).conf_int() - - return { - "method": "ARIMA", - "predicted_temp": float(forecast[0]) if steps == 1 else [float(f) for f in forecast], - "confidence_interval": [float(conf_int.iloc[0, 0]), float(conf_int.iloc[0, 1])] if steps == 1 else conf_int.values.tolist() - } - except Exception as e: - logger.error(f"ARIMA prediction failed: {e}") - return None - - def predict_rf(self, features: np.ndarray) -> Optional[Dict]: - """ - Make prediction using Random Forest model - - Args: - features: Feature array for prediction - - Returns: - dict: Prediction result - """ - if self.rf_model is None: - logger.warning("Random Forest model not trained") - return None - - try: - prediction = self.rf_model.predict(features.reshape(1, -1))[0] - - # Estimate confidence using tree variance - tree_predictions = [tree.predict(features.reshape(1, -1))[0] - for tree in self.rf_model.estimators_] - std = np.std(tree_predictions) - - return { - "method": "RandomForest", - "predicted_temp": float(prediction), - "confidence_interval": [float(prediction - 1.96 * std), - float(prediction + 1.96 * std)], - "std": float(std) - } - except Exception as e: - logger.error(f"Random Forest prediction failed: {e}") - return None - - def predict_ensemble(self, - temperature_history: List[float], - feature_data: pd.DataFrame = None, - arima_weight: float = 0.4, - rf_weight: float = 0.6) -> Dict: - """ - Make ensemble prediction combining ARIMA and Random Forest - - Args: - temperature_history: Historical temperature series - feature_data: Feature data for RF prediction - arima_weight: Weight for ARIMA prediction - rf_weight: Weight for RF prediction - - Returns: - dict: Ensemble prediction - """ - predictions = [] - weights = [] - - # ARIMA prediction - if self.arima_model is not None: - arima_pred = self.predict_arima(steps=1) - if arima_pred: - predictions.append(arima_pred["predicted_temp"]) - weights.append(arima_weight) - - # Random Forest prediction - if self.rf_model is not None and feature_data is not None: - # Get latest features - prepared = self.prepare_features(feature_data) - if len(prepared) > 0 and hasattr(self, 'feature_names'): - latest_features = prepared[self.feature_names].iloc[-1].values - rf_pred = self.predict_rf(latest_features) - if rf_pred: - predictions.append(rf_pred["predicted_temp"]) - weights.append(rf_weight) - - if not predictions: - logger.debug("No predictions available (Model not trained)") - return { - "predicted_temp": None, - "confidence": 0.5, - "error": "No models available for prediction" - } - - # Weighted average - weights = np.array(weights) / np.sum(weights) # Normalize weights - ensemble_pred = np.average(predictions, weights=weights) - - # Estimate confidence based on model agreement - if len(predictions) > 1: - spread = abs(predictions[0] - predictions[1]) - confidence = max(0.5, 1.0 - spread / 5.0) # Lower confidence if predictions differ - else: - confidence = 0.7 - - return { - "predicted_temp": float(ensemble_pred), - "confidence": confidence, - "confidence_interval": [ensemble_pred - 2.0, ensemble_pred + 2.0], # Approximate - "individual_predictions": predictions, - "weights": weights.tolist() - } diff --git a/src/strategy/__init__.py b/src/strategy/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/strategy/decision_engine.py b/src/strategy/decision_engine.py deleted file mode 100644 index 19db1c4d..00000000 --- a/src/strategy/decision_engine.py +++ /dev/null @@ -1,198 +0,0 @@ -from loguru import logger -from src.analysis.volume_analyzer import VolumeAnalyzer -from src.analysis.orderbook_analyzer import analyze_orderbook -from src.analysis.technical_indicators import TechnicalIndicators - -class DecisionEngine: - """ - 综合决策引擎 - 多因子加权评分系统 - """ - - def __init__(self, config: dict = None): - self.config = config or {} - - # 因子权重 - self.weights = self.config.get("weights", { - "statistical_prediction": 0.50, - "data_source_consensus": 0.15, - "market_volume_signal": 0.15, - "orderbook_analysis": 0.10, - "technical_indicators": 0.05, - "onchain_whale_signal": 0.05 - }) - - # 初始化分析器 - self.volume_analyzer = VolumeAnalyzer(config) - self.tech_indicators = TechnicalIndicators() - - logger.info("决策引擎初始化完成。") - logger.debug(f"权重配置: {self.weights}") - - def calculate_signal(self, - model_prediction: dict, - market_data: dict, - weather_consensus: dict = None, - whale_activity: dict = None) -> dict: - """ - 综合多因子计算交易信号 - - Args: - model_prediction: 统计模型预测结果 - market_data: 市场数据 (价格历史、订单簿、交易量等) - weather_consensus: 天气数据源一致性检查结果 - whale_activity: 链上大户活动数据 - - Returns: - dict: 综合评分和交易建议 - """ - scores = {} - details = {} - - # 1. 统计模型预测得分 (权重: 50%) - stat_confidence = model_prediction.get("confidence", 0.5) - scores["statistical"] = stat_confidence - details["statistical"] = { - "score": stat_confidence, - "prediction": model_prediction.get("predicted_temp"), - "confidence_interval": model_prediction.get("confidence_interval") - } - - # 2. 多源数据一致性 (权重: 15%) - if weather_consensus: - is_consensus = weather_consensus.get("consensus", False) - consensus_score = 1.0 if is_consensus else 0.3 - else: - consensus_score = 0.5 - scores["consensus"] = consensus_score - details["consensus"] = weather_consensus - - # 3. 交易量信号 (权重: 15%) - volume_history = market_data.get("volume_history", []) - transactions = market_data.get("transactions", []) - volume_analysis = self.volume_analyzer.analyze(volume_history, transactions) - scores["volume"] = volume_analysis.get("combined_score", 0.5) - details["volume"] = volume_analysis - - # 4. 订单簿分析 (权重: 10%) - orderbook = market_data.get("orderbook", {}) - orderbook_signal = analyze_orderbook(orderbook) - scores["orderbook"] = orderbook_signal.get("confidence", 0.5) - details["orderbook"] = orderbook_signal - - # 5. 技术指标 (权重: 5%) - price_history = market_data.get("price_history", []) - if price_history: - tech_signal = self.tech_indicators.get_signal(price_history) - scores["technical"] = tech_signal.get("combined_score", 0.5) - details["technical"] = tech_signal - else: - scores["technical"] = 0.5 - details["technical"] = {"message": "No price history available"} - - # 6. 链上鲸鱼信号 (权重: 5%) - if whale_activity: - is_bullish = whale_activity.get("bullish", False) - whale_score = 0.8 if is_bullish else 0.2 - else: - whale_score = 0.5 - scores["whale"] = whale_score - details["whale"] = whale_activity - - # 加权计算最终分数 - final_score = ( - scores["statistical"] * self.weights["statistical_prediction"] + - scores["consensus"] * self.weights["data_source_consensus"] + - scores["volume"] * self.weights["market_volume_signal"] + - scores["orderbook"] * self.weights["orderbook_analysis"] + - scores["technical"] * self.weights["technical_indicators"] + - scores["whale"] * self.weights["onchain_whale_signal"] - ) - - # 生成建议 - recommendation = self._get_recommendation(final_score) - - result = { - "final_score": round(final_score, 4), - "recommendation": recommendation, - "factor_scores": scores, - "factor_details": details, - "weights": self.weights - } - - logger.info(f"Decision: {recommendation} (score: {final_score:.4f})") - return result - - def _get_recommendation(self, score: float) -> str: - """ - 根据评分生成交易建议 - - Args: - score: 综合评分 (0-1) - - Returns: - str: 交易建议 - """ - if score > 0.80: - return "STRONG_BUY" - elif score > 0.65: - return "BUY" - elif score > 0.50: - return "WEAK_BUY" - elif score > 0.35: - return "HOLD" - elif score > 0.20: - return "WEAK_SELL" - else: - return "NO_ACTION" - - def should_trade(self, - signal: dict, - current_price: float, - min_confidence: float = 0.65) -> dict: - """ - 判断是否应该执行交易 - - Args: - signal: calculate_signal返回的信号 - current_price: 当前市场价格 - min_confidence: 最低置信度阈值 - - Returns: - dict: 交易决策 - """ - final_score = signal.get("final_score", 0) - recommendation = signal.get("recommendation", "NO_ACTION") - - # 检查是否满足交易条件 - should_buy = ( - final_score >= min_confidence and - recommendation in ["STRONG_BUY", "BUY"] and - current_price >= 0.85 # 价格阈值 - ) - - should_sell = ( - final_score < 0.35 or - recommendation in ["WEAK_SELL", "NO_ACTION"] - ) - - if should_buy: - return { - "action": "BUY", - "confidence": final_score, - "price": current_price, - "reason": f"Score {final_score:.2f} >= threshold {min_confidence}" - } - elif should_sell: - return { - "action": "SELL", - "confidence": final_score, - "price": current_price, - "reason": f"Score {final_score:.2f} below threshold or bearish signal" - } - else: - return { - "action": "HOLD", - "confidence": final_score, - "price": current_price, - "reason": "Conditions not met for trading" - } diff --git a/src/strategy/position_manager.py b/src/strategy/position_manager.py deleted file mode 100644 index 0f0af2c1..00000000 --- a/src/strategy/position_manager.py +++ /dev/null @@ -1,153 +0,0 @@ -from loguru import logger - -class PositionManager: - """ - 仓位管理 - Kelly公式动态仓位计算 - """ - - def __init__(self, config=None): - self.config = config or {} - self.max_position_ratio = self.config.get("max_position_ratio", 0.25) # 最大单笔25% - self.max_total_exposure = self.config.get("max_total_exposure", 0.80) # 最大总仓位80% - self.min_trade_size = self.config.get("min_trade_size", 10) # 最小交易额$10 - logger.info("Initializing Position Manager...") - - def kelly_criterion(self, win_prob: float, odds: float) -> float: - """ - Kelly公式计算最优投资比例 - - f = (bp - q) / b - f: 应投资的资金比例 - b: 赔率 (盈利/亏损) - p: 胜率 - q: 败率 (1-p) - - Args: - win_prob: 预测胜率 (0-1) - odds: 赔率 - - Returns: - float: 建议投资比例 (0-1) - """ - if win_prob <= 0 or win_prob >= 1 or odds <= 0: - return 0.0 - - q = 1 - win_prob - f = (win_prob * odds - q) / odds - - # 限制最大仓位 - f = max(0, min(f, self.max_position_ratio)) - - logger.debug(f"Kelly ratio: {f:.4f} (win_prob={win_prob:.2f}, odds={odds:.2f})") - return f - - def calculate_position_size(self, - total_capital: float, - win_prob: float, - market_price: float, - current_exposure: float = 0) -> dict: - """ - 计算建议仓位大小 - - Args: - total_capital: 总资金 - win_prob: 模型预测胜率 - market_price: 当前市场价格 (0-1) - current_exposure: 当前已有仓位占比 - - Returns: - dict: 包含建议仓位大小和相关信息 - """ - # 计算赔率 - if market_price <= 0 or market_price >= 1: - return {"size": 0, "error": "Invalid market price"} - - odds = (1 - market_price) / market_price - - # Kelly计算 - kelly_ratio = self.kelly_criterion(win_prob, odds) - - # 检查总仓位限制 - available_ratio = self.max_total_exposure - current_exposure - if available_ratio <= 0: - return { - "size": 0, - "kelly_ratio": kelly_ratio, - "reason": "Max exposure reached" - } - - # 实际使用比例 - actual_ratio = min(kelly_ratio, available_ratio) - - # 计算金额 - position_size = total_capital * actual_ratio - - # 检查最小交易额 - if position_size < self.min_trade_size: - return { - "size": 0, - "kelly_ratio": kelly_ratio, - "reason": f"Below minimum trade size (${self.min_trade_size})" - } - - return { - "size": position_size, - "kelly_ratio": kelly_ratio, - "actual_ratio": actual_ratio, - "odds": odds, - "expected_return": (win_prob * odds - (1 - win_prob)) * position_size - } - - def should_exit(self, - entry_price: float, - current_price: float, - current_prediction: float, - stop_loss: float = 0.15, - take_profit: float = 0.30) -> dict: - """ - 判断是否应该平仓 - - Args: - entry_price: 入场价格 - current_price: 当前价格 - current_prediction: 当前模型预测 - stop_loss: 止损比例 - take_profit: 止盈比例 - - Returns: - dict: 退出建议 - """ - if entry_price <= 0: - return {"should_exit": False} - - pnl_ratio = (current_price - entry_price) / entry_price - - # 止损 - if pnl_ratio < -stop_loss: - return { - "should_exit": True, - "reason": "STOP_LOSS", - "pnl_ratio": pnl_ratio - } - - # 止盈 - if pnl_ratio > take_profit: - return { - "should_exit": True, - "reason": "TAKE_PROFIT", - "pnl_ratio": pnl_ratio - } - - # 模型预测反转 - if current_prediction < 0.4: # 预测胜率下降 - return { - "should_exit": True, - "reason": "PREDICTION_REVERSAL", - "pnl_ratio": pnl_ratio, - "current_prediction": current_prediction - } - - return { - "should_exit": False, - "pnl_ratio": pnl_ratio - } diff --git a/src/strategy/risk_manager.py b/src/strategy/risk_manager.py deleted file mode 100644 index 2feca543..00000000 --- a/src/strategy/risk_manager.py +++ /dev/null @@ -1,99 +0,0 @@ -from loguru import logger - - -class RiskManager: - """ - 风险控制系统 - """ - - def __init__(self, config=None): - self.config = config or {} - # 基础风控参数 - self.max_single_trade = self.config.get( - "max_single_trade", 50.0 - ) # 最大单笔调整为 $50 - self.max_daily_exposure = 50.0 # 每日最高投入上限 - self.daily_used_exposure = 0.0 - self.last_reset_date = "" - - self.min_confidence = 0.5 - self.peak_capital = 0 - self.is_trading_paused = False - - logger.info("Initializing Pro Risk Manager...") - - def _reset_daily_exposure(self): - """每日重置额度""" - from datetime import datetime - - today = datetime.now().strftime("%Y-%m-%d") - if self.last_reset_date != today: - self.daily_used_exposure = 0.0 - self.last_reset_date = today - logger.info(f"Daily exposure reset for {today}") - - def calculate_position_size( - self, - base_confidence_usd: float, - depth: float = 0, - hours_to_settle: float = 24, - is_high_relative_volume: bool = False, - ) -> tuple[float, str]: - """ - 仓位计算方法 (简化版，移除流动性过滤): - 仓位 = base_position(置信度) - × time_decay(离结算衰减) - × budget_limit - """ - self._reset_daily_exposure() - - final_pos = base_confidence_usd - reason = "Normal" - - # 1. 时间衰减因子 - # 离结算时间越近，预测越准但也存在剧烈博弈风险 - time_factor = 1.0 - if hours_to_settle <= 1.0: - time_factor = 0.0 # 最后 1 小时停止建仓 - reason = "🚫临近结算" - elif hours_to_settle <= 4.0: - time_factor = 0.4 # 1-4小时：缩小 60% - reason = "⏱️结算冲刺 (40%)" - elif hours_to_settle <= 12.0: - time_factor = 0.7 # 4-12小时：缩小 30% - reason = "⏳接近结算 (70%)" - - final_pos *= time_factor - if final_pos <= 0: - return 0.0, reason - - # 2. 预算上限过滤 - remaining_daily = self.max_daily_exposure - self.daily_used_exposure - if remaining_daily <= 0: - return 0.0, "🚫今日总额度已满 ($50)" - - if final_pos > remaining_daily: - final_pos = remaining_daily - reason = "🛑触及日风控上限" - - # 3. 高相对成交量加权 (如果是高成交量市场，且逻辑支持，可保持原状或微增) - # 这里逻辑设定为：如果不是高成交量，再次缩减 20% 防御 - if not is_high_relative_volume: - final_pos *= 0.8 - if reason == "Normal": - reason = "📉低活缩减" - - return round(final_pos, 2), reason - - def record_trade(self, amount: float): - """记录成交额以扣除额度""" - self.daily_used_exposure += amount - logger.debug( - f"Applied exposure: ${amount}. Daily Total: ${self.daily_used_exposure}" - ) - - def check_trade_risk( - self, trade_size: float, market_data: dict, model_confidence: float - ) -> dict: - """保持基础接口兼容""" - return {"passed": True, "risks": []} diff --git a/src/trading/__init__.py b/src/trading/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/trading/order_executor.py b/src/trading/order_executor.py deleted file mode 100644 index da0efe03..00000000 --- a/src/trading/order_executor.py +++ /dev/null @@ -1,219 +0,0 @@ -from loguru import logger -from typing import Optional, Dict -from src.data_collection.polymarket_api import PolymarketClient - -class OrderExecutor: - """ - 交易执行器 - 负责订单生成、提交和管理 - """ - - def __init__(self, config: dict, client: PolymarketClient): - self.config = config - self.client = client - self.pending_orders = {} - self.executed_orders = [] - - logger.info("Order Executor initialized") - - def execute_trade(self, - token_id: str, - side: str, - amount: float, - price: float, - order_type: str = "GTC") -> Dict: - """ - 执行交易 - - Args: - token_id: Token ID - side: "BUY" 或 "SELL" - amount: 交易金额 - price: 价格 - order_type: 订单类型 (GTC, GTD, FOK) - - Returns: - dict: 订单结果 - """ - logger.info(f"Executing {side} order: ${amount:.2f} @ {price:.4f}") - - # 计算数量 - if price <= 0: - return {"status": "error", "message": "Invalid price"} - - size = amount / price - - # 提交订单 - try: - result = self.client.create_order( - token_id=token_id, - side=side, - price=price, - size=size, - order_type=order_type - ) - - if result: - order_id = result.get("orderID", "unknown") - self.executed_orders.append({ - "order_id": order_id, - "token_id": token_id, - "side": side, - "price": price, - "size": size, - "amount": amount, - "result": result - }) - - logger.info(f"Order executed successfully: {order_id}") - return { - "status": "success", - "order_id": order_id, - "side": side, - "price": price, - "size": size, - "amount": amount - } - else: - return {"status": "error", "message": "Order submission failed"} - - except Exception as e: - logger.error(f"Order execution failed: {e}") - return {"status": "error", "message": str(e)} - - def cancel_order(self, order_id: str) -> Dict: - """ - 取消订单 - - Args: - order_id: 订单ID - - Returns: - dict: 取消结果 - """ - try: - result = self.client.cancel_order(order_id) - if result: - logger.info(f"Order {order_id} cancelled") - return {"status": "success", "order_id": order_id} - else: - return {"status": "error", "message": "Cancel failed"} - except Exception as e: - logger.error(f"Cancel order failed: {e}") - return {"status": "error", "message": str(e)} - - def get_open_orders(self, market_id: str = None) -> Optional[Dict]: - """ - 获取当前挂单 - - Args: - market_id: 可选的市场过滤 - - Returns: - dict: 挂单列表 - """ - return self.client.get_orders(market_id) - - def get_execution_history(self) -> list: - """ - 获取执行历史 - - Returns: - list: 已执行订单列表 - """ - return self.executed_orders - - -class PortfolioTracker: - """ - 持仓追踪器 - """ - - def __init__(self): - self.positions = {} - self.total_invested = 0 - self.total_pnl = 0 - - logger.info("Portfolio Tracker initialized") - - def add_position(self, - token_id: str, - side: str, - size: float, - entry_price: float, - amount: float): - """ - 添加持仓 - """ - if token_id not in self.positions: - self.positions[token_id] = { - "side": side, - "size": size, - "entry_price": entry_price, - "amount": amount, - "current_price": entry_price, - "unrealized_pnl": 0 - } - else: - # 加仓 - existing = self.positions[token_id] - total_size = existing["size"] + size - avg_price = (existing["size"] * existing["entry_price"] + size * entry_price) / total_size - existing["size"] = total_size - existing["entry_price"] = avg_price - existing["amount"] += amount - - self.total_invested += amount - logger.info(f"Position added: {token_id}, size={size}, price={entry_price}") - - def update_price(self, token_id: str, current_price: float): - """ - 更新持仓价格 - """ - if token_id in self.positions: - pos = self.positions[token_id] - pos["current_price"] = current_price - - # 计算未实现盈亏 - if pos["side"] == "BUY": - pos["unrealized_pnl"] = (current_price - pos["entry_price"]) * pos["size"] - else: - pos["unrealized_pnl"] = (pos["entry_price"] - current_price) * pos["size"] - - def close_position(self, token_id: str, exit_price: float) -> Dict: - """ - 平仓 - """ - if token_id not in self.positions: - return {"status": "error", "message": "Position not found"} - - pos = self.positions[token_id] - - if pos["side"] == "BUY": - realized_pnl = (exit_price - pos["entry_price"]) * pos["size"] - else: - realized_pnl = (pos["entry_price"] - exit_price) * pos["size"] - - self.total_pnl += realized_pnl - self.total_invested -= pos["amount"] - - del self.positions[token_id] - - return { - "status": "success", - "realized_pnl": realized_pnl, - "exit_price": exit_price - } - - def get_summary(self) -> Dict: - """ - 获取持仓汇总 - """ - total_unrealized = sum(p["unrealized_pnl"] for p in self.positions.values()) - - return { - "positions_count": len(self.positions), - "total_invested": self.total_invested, - "total_unrealized_pnl": total_unrealized, - "total_realized_pnl": self.total_pnl, - "positions": self.positions - } diff --git a/src/trading/paper_trader.py b/src/trading/paper_trader.py deleted file mode 100644 index 386bf6e4..00000000 --- a/src/trading/paper_trader.py +++ /dev/null @@ -1,158 +0,0 @@ -import json -import os -import time -from datetime import datetime, timedelta -from loguru import logger - - -class PaperTrader: - """ - 模拟交易系统 (Paper Trading System) - """ - - def __init__(self, storage_path="data/paper_positions.json", total_capital=1000.0): - self.storage_path = storage_path - self.initial_capital = total_capital - data = self._load_data() - self.positions = data.get("positions", {}) - self.history = data.get("history", []) # 历史结项记录 - self.trades = data.get("trades", []) # 原始买入/卖出记录 - self.balance = data.get("balance", total_capital) - logger.info(f"模拟交易系统初始化。累计成交: {len(self.history)} 笔, 买入记录: {len(self.trades)} 笔") - - def _load_data(self): - if os.path.exists(self.storage_path): - try: - with open(self.storage_path, "r", encoding="utf-8") as f: - return json.load(f) - except: - return {"positions": {}, "history": [], "trades": [], "balance": self.initial_capital} - return {"positions": {}, "history": [], "trades": [], "balance": self.initial_capital} - - def _save_data(self): - with open(self.storage_path, "w", encoding="utf-8") as f: - json.dump( - { - "positions": self.positions, - "history": self.history, - "trades": self.trades, - "balance": round(self.balance, 2), - }, - f, - ensure_ascii=False, - indent=2, - ) - - def open_position(self, market_id: str, city: str, option: str, price: int, side: str, amount_usd: float = 5.0, target_date: str = None, predicted_temp: float = None): - """ - 开仓进入模拟仓位 - """ - # 价格以美分计，转换为 0-1 比例 - price_decimal = price / 100.0 - - # 检查余额 - if self.balance < amount_usd: - logger.warning(f"余额不足，无法开仓 (余额: ${self.balance:.2f})") - return False - - # 计算持仓份额 - shares = amount_usd / price_decimal if price_decimal > 0 else 0 - - position_id = f"{market_id}_{side}" - - # 如果已经有相同方向的仓位，可以选择加仓或忽略（这里简单起见，不重复开仓） - if position_id in self.positions: - return False - - new_pos = { - "market_id": market_id, - "city": city, - "option": option, - "side": side, - "entry_price": price, - "shares": shares, - "cost_usd": amount_usd, - "current_price": price, - "pnl_usd": 0.0, - "pnl_pct": 0.0, - "status": "OPEN", - "target_date": target_date, - "predicted_temp": predicted_temp, - "opened_at": (datetime.utcnow() + timedelta(hours=8)).strftime("%Y-%m-%d %H:%M:%S") - } - - self.positions[position_id] = new_pos - self.balance -= amount_usd - - # 记录交易流水 - self.trades.append({ - "type": "BUY", - "city": city, - "option": option, - "side": side, - "price": price, - "amount": amount_usd, - "time": new_pos["opened_at"] - }) - - self._save_data() - - logger.success(f"【模拟开仓】{city} | {option} | {side} | 价格: {price}¢ | 投入: ${amount_usd}") - return True - - def update_pnl(self, current_prices: dict): - updated_report = [] - finished_ids = [] - - for pid, pos in self.positions.items(): - if pos["status"] != "OPEN": - continue - m_id = pos["market_id"] - - if m_id in current_prices: - curr_price = current_prices[m_id].get("price", 50) - if pos["side"] == "NO": - curr_price = 100 - curr_price - - # 更新当前价值 - value = pos["shares"] * (curr_price / 100.0) - pnl = value - pos["cost_usd"] - pnl_pct = (pnl / pos["cost_usd"]) * 100 if pos["cost_usd"] > 0 else 0 - - pos["current_price"] = curr_price - pos["pnl_usd"] = round(pnl, 2) - pos["pnl_pct"] = round(pnl_pct, 2) - - # --- 自动结项检测：如果价格变为 0 或 100 (Polymarket 已结算) --- - if curr_price >= 99.5 or curr_price <= 0.5: - pos["status"] = "CLOSED" - pos["closed_at"] = ( - datetime.utcnow() + timedelta(hours=8) - ).strftime("%Y-%m-%d %H:%M:%S") - self.balance += value # 资金回笼 - self.history.append(pos) - finished_ids.append(pid) - logger.success( - f"【模拟结项】{pos['city']} | {pos['option']} | 最终价格: {curr_price}¢ | 获利: ${pnl:+.2f}" - ) - else: - updated_report.append(pos) - - # 从活跃仓位中移除已结项的 - for pid in finished_ids: - # 在流水中添加卖出（结项）记录 - pos = self.positions[pid] - self.trades.append({ - "type": "SELL", - "city": pos["city"], - "option": pos["option"], - "side": pos["side"], - "price": pos["current_price"], - "amount": round(pos["shares"] * (pos["current_price"] / 100.0), 2), - "time": pos.get("closed_at") - }) - del self.positions[pid] - - self._save_data() - - return updated_report diff --git a/src/utils/notifier.py b/src/utils/notifier.py deleted file mode 100644 index be0d733d..00000000 --- a/src/utils/notifier.py +++ /dev/null @@ -1,285 +0,0 @@ -import requests -import html -from loguru import logger -from datetime import datetime - - -class TelegramNotifier: - """ - Telegram 消息推送模块 - 支持信号推送、预警推送和市场异常提醒 - """ - - def __init__(self, config: dict): - self.config = config - self.token = config.get("bot_token") - self.chat_id = config.get("chat_id") - self.proxy = config.get("proxy") - - self.session = requests.Session() - if self.proxy: - if not self.proxy.startswith("http"): - self.proxy = f"http://{self.proxy}" - self.session.proxies = {"http": self.proxy, "https": self.proxy} - - logger.info("Telegram 通知器初始化完成。") - - @staticmethod - def _escape_html(text: str) -> str: - """Escape HTML special characters""" - if not isinstance(text, str): - text = str(text) - return html.escape(text, quote=False) - - def _send_message(self, text: str): - """发送 Telegram 消息的主函数 (支持多个 ID)""" - if not self.token or not self.chat_id: - logger.warning("未配置 Telegram Token 或 Chat ID，无法发送消息。") - return False - - # 支持逗号分隔的多个 ID - chat_ids = str(self.chat_id).replace(" ", "").split(",") - url = f"https://api.telegram.org/bot{self.token}/sendMessage" - - all_successful = True - for cid in chat_ids: - if not cid: - continue - - payload = { - "chat_id": cid, - "text": text, - "parse_mode": "HTML", - "disable_web_page_preview": True, - } - - try: - response = self.session.post(url, json=payload, timeout=10) - if response.status_code != 200: - error_msg = response.text - if "chat not found" in error_msg.lower(): - logger.error( - f"Telegram 消息发送给 {cid} 失败 (400): Chat ID {cid} 无效或机器人尚未被加入该聊天。请在 Telegram 中发送 /id 给机器人确认正确的 Chat ID。" - ) - else: - logger.error( - f"Telegram 消息发送给 {cid} 失败 ({response.status_code}): {error_msg}" - ) - all_successful = False - else: - logger.info(f"Telegram 消息发送给 {cid} 成功。") - except Exception as e: - logger.error(f"Telegram 消息发送给 {cid} 异常: {e}") - all_successful = False - return all_successful - - def send_signal( - self, - market_name: str, - full_title: str, - option: str, - score: float, - prediction: str, - confidence: int, - analysis_list: list, - price: float, - market_url: str, - local_time: str = None, - target_date: str = None, - ): - """发送交易信号推送""" - stars = "⭐" * int(score) + "☆" * (5 - int(score)) - timestamp_utc = datetime.utcnow().strftime("%H:%M") - - analysis_text = "\n".join( - [ - f"✅ {self._escape_html(item)}" if "✅" not in item else item - for item in analysis_list - ] - ) - - local_time_text = ( - f"🕒 当地时间: {self._escape_html(local_time)}\n" - if local_time - else "" - ) - target_date_text = self._escape_html(target_date) if target_date else "待定" - - text = ( - f"🎯 交易信号 #{self._escape_html(market_name.split(' ')[0])}\n\n" - f"📍 城市: {self._escape_html(market_name)}\n" - f"🏆 市场: {self._escape_html(full_title)}\n" - f"📝 选项: {self._escape_html(option)}\n" - f"💰 当前价格: {price}¢\n" - f"═══════════════════\n" - f"📊 信号评分: {stars} ({score}/5)\n" - f"🤖 模型预测: {self._escape_html(prediction)}\n" - f"📈 置信度: {confidence}%\n\n" - f"分析汇总:\n" - f"{analysis_text}\n" - f"═══════════════════\n" - f"{local_time_text}" - f"📅 结算日期: {target_date_text}\n" - f"🔗 点击进入市场\n\n" - f"⏰ 信号时间: {timestamp_utc} UTC" - ) - return self._send_message(text) - - def send_combined_alert( - self, - city: str, - alerts: list, - local_time: str = None, - forecast_temp: str = None, - total_volume: float = 0, - brackets_count: int = 0, - strategy_tips: list = None, - metar_data: dict = None, - ): - """发送简约版合并预警 (含 METAR 航空气象数据)""" - if not alerts: - return - - from datetime import datetime, timedelta - - # UTC+8 北京时间 - now_bj = datetime.utcnow() + timedelta(hours=8) - timestamp_bj = now_bj.strftime("%H:%M") - - # 1. METAR 航空气象数据区块 - metar_text = "" - if metar_data and metar_data.get("current", {}).get("temp") is not None: - icao = metar_data.get("icao", "N/A") - temp = metar_data["current"]["temp"] - unit = "°F" if metar_data.get("unit") == "fahrenheit" else "°C" - - # 解析观测时间 (格式: 2026-02-07T11:00:00.000Z) - obs_time_raw = metar_data.get("observation_time", "") - if "T" in obs_time_raw: - obs_time = obs_time_raw.split("T")[1][:5] + " UTC" - else: - obs_time = obs_time_raw or "N/A" - - # 可选：风速信息 - wind_kt = metar_data["current"].get("wind_speed_kt") - wind_text = f" | 风速:{wind_kt}kt" if wind_kt else "" - - metar_text = ( - f"✈️ 机场实测 ({icao}):\n" - f" 🌡️ {temp:.1f}{unit}{wind_text}\n" - f" 🕐 观测: {obs_time}\n\n" - ) - - # 2. 信号详情构建 - items_text = "" - for a in alerts: - items_text += f"{a['msg']}\n\n" - - # 3. 策略建议（如果有） - tips_text = "" - if strategy_tips: - tips_text = ( - "💡 策略建议:\n" - + "\n".join([f"• {self._escape_html(tip)}" for tip in strategy_tips]) - + "\n\n" - ) - - # 4. 总体布局 - text = ( - f"🔔 城市监控报告 #{self._escape_html(city)}\n\n" - f"📍 城市: {self._escape_html(city)}\n" - f"{metar_text}" - f"📊 实时异动:\n" - f"{items_text}" - f"{tips_text}" - f"═══════════════════\n" - f"🕒 当地时间: {self._escape_html(local_time or 'N/A')}\n" - f"⏰ 预警时间: {timestamp_bj} (北京时间)" - ) - return self._send_message(text) - - def send_anomaly( - self, - city_tag: str, - market_name: str, - detected_anomaly: str, - stats: dict, - whales: list, - current_price: float, - local_time: str = None, - ): - """发送市场异常推送""" - from datetime import datetime, timedelta - - # UTC+8 北京时间 - timestamp_bj = (datetime.utcnow() + timedelta(hours=8)).strftime("%H:%M") - - whale_text = "\n".join([f"- {self._escape_html(w)}" for w in whales]) - stats_text = "\n".join( - [ - f"{self._escape_html(k)}: {self._escape_html(v)}" - for k, v in stats.items() - ] - ) - local_time_text = ( - f"🕒 当地时间: {self._escape_html(local_time)}\n" - if local_time - else "" - ) - - text = ( - f"👀 市场异常 #{self._escape_html(city_tag)}\n\n" - f"📍 城市: {self._escape_html(city_tag)}\n" - f"🏆 市场: {self._escape_html(market_name)}\n\n" - f"🚨 检测到异常:\n" - f"{self._escape_html(detected_anomaly)}\n" - f"{stats_text}\n\n" - f"🐋 大户动向:\n" - f"{whale_text}\n\n" - f"💰 当前价格: {current_price}¢\n" - f"═══════════════════\n" - f"{local_time_text}" - f"⏰ 信号时间: {timestamp_bj} (北京时间)" - ) - return self._send_message(text) - - def send_alert( - self, - city_tag: str, - market_name: str, - price: float, - trigger: str, - prev_price: float, - change: str, - quick_analysis: list, - local_time: str = None, - ): - """发送价格预警推送""" - from datetime import datetime, timedelta - - # UTC+8 北京时间 - timestamp_bj = (datetime.utcnow() + timedelta(hours=8)).strftime("%H:%M") - - analysis_text = "\n".join( - [f"- {self._escape_html(item)}" for item in quick_analysis] - ) - local_time_text = ( - f"🕒 当地时间: {self._escape_html(local_time)}\n" - if local_time - else "" - ) - - text = ( - f"⚡ 价格预警 #{self._escape_html(city_tag)}\n\n" - f"📍 城市: {self._escape_html(city_tag)}\n" - f"🏆 市场: {self._escape_html(market_name)}\n" - f"💰 报价: {price}¢ ↗️\n\n" - f"触发条件: {self._escape_html(trigger)}\n" - f"变动详情: {prev_price}¢ -> {price}¢ ({self._escape_html(change)})\n\n" - f"📊 快速分析:\n" - f"{analysis_text}\n\n" - f"═══════════════════\n" - f"{local_time_text}" - f"⏰ 预警时间: {timestamp_bj} (北京时间)" - ) - return self._send_message(text) diff --git a/tests/test_models.py b/tests/test_models.py deleted file mode 100644 index 0c932f31..00000000 --- a/tests/test_models.py +++ /dev/null @@ -1,29 +0,0 @@ -import unittest -import pandas as pd -import numpy as np -from src.models.statistical_model import TemperaturePredictor - -class TestStatisticalModel(unittest.TestCase): - def setUp(self): - self.predictor = TemperaturePredictor() - # Mock data - self.history = [5.0, 5.2, 5.5, 5.8, 6.0, 6.2, 6.5] * 10 - self.df = pd.DataFrame({ - 'date': pd.date_range(start='2023-01-01', periods=len(self.history)), - 'temp': self.history - }) - - def test_feature_preparation(self): - prepared = self.predictor.prepare_features(self.df) - self.assertIn('day_of_year', prepared.columns) - self.assertIn('temp_lag_1', prepared.columns) - self.assertGreater(len(prepared), 0) - - def test_prediction_output_format(self): - # Even without full training, check structure - pred = {"predicted_temp": 7.0, "confidence": 0.8} - self.assertIn('predicted_temp', pred) - self.assertIn('confidence', pred) - -if __name__ == '__main__': - unittest.main()