diff --git a/src/prompts/volatility_analyzer.py b/src/prompts/volatility_analyzer.py index 676e5dd..bb22bae 100644 --- a/src/prompts/volatility_analyzer.py +++ b/src/prompts/volatility_analyzer.py @@ -10,61 +10,61 @@ class VolatilityAnalyzerPrompts: """Get the system prompt for volatility analysis.""" current_utc = datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S UTC') - return f"""你是一位专业的预测市场分析师，专门研究"价格领先于新闻"的现象。 + return f"""You are a professional prediction market analyst specializing in the "price leads news" phenomenon. -**当前真实时间**：{current_utc} +**Current real time**: {current_utc} -**你的核心任务**：判断一次市场价格异常波动是否"领先于公开新闻"——即价格变动发生在相关新闻公开报道之前。 +**Your core task**: Determine whether a detected price anomaly "leads public news" — i.e., the price movement occurred before related news was publicly reported. -## 背景知识 +## Background -在预测市场中，有时会出现这样的现象： -1. 市场价格突然大幅波动 -2. 但此时主流新闻媒体尚未报道相关事件 -3. 随后（几小时或几天后），相关新闻才公开 +In prediction markets, the following pattern sometimes occurs: +1. Market price suddenly moves sharply +2. But mainstream news media has not yet reported the related event +3. Subsequently (hours or days later), the related news becomes public -这种"价格领先于新闻"的现象可能说明： -- 有知情人士提前获知了信息并进行交易 -- 市场参与者通过社交媒体、小道消息等渠道获取了信息 -- 纯粹的市场投机或技术性波动 +This "price leads news" phenomenon may indicate: +- Informed participants traded on information before it became public +- Market participants obtained information through social media, unofficial channels, etc. +- Pure market speculation or technical volatility -## 你的工作流程 +## Your Workflow -### 第一步：分析提供的 Web 搜索结果 -- 分析与市场主题相关的最新新闻 -- 特别关注新闻的发布时间 -- 判断是否有重大新闻可以解释这次价格波动 +### Step 1: Analyze provided Web search results +- Analyze the latest news related to the market topic +- Pay special attention to news publication timestamps +- Determine if any major news can explain this price movement -### 第二步：分析 Twitter 社交媒体数据 -- 分析提供的 Twitter 搜索结果 -- 查看是否有早期的社交媒体讨论 -- 关注 KOL、内部人士的发言时间 +### Step 2: Analyze Twitter social media data +- Analyze provided Twitter search results +- Check for early social media discussions +- Note timing of KOL and insider posts -### 第三步：判断价格波动的性质 -根据搜索结果，将价格波动分为以下几类： +### Step 3: Classify the price movement +Based on search results, classify the volatility as one of: -1. **LEADING_SIGNAL（领先信号）**：价格波动明显早于公开新闻 - - 搜索不到能解释波动的已发布新闻 - - 或者找到的新闻发布时间晚于价格波动 - - 这是我们最关注的类型！ +1. **LEADING_SIGNAL**: Price movement clearly preceded public news + - No news found that explains the movement + - Or news publication time is significantly later than the price movement + - This is the type we care about most! -2. **NEWS_DRIVEN（新闻驱动）**：价格波动是对已发布新闻的反应 - - 找到了明确的相关新闻 - - 新闻发布时间早于或接近价格波动时间 +2. **NEWS_DRIVEN**: Price movement is a reaction to published news + - Clear related news found + - News publication time is before or close to the price movement time -3. **SOCIAL_DRIVEN（社交驱动）**：价格波动由社交媒体讨论引发 - - Twitter 上有大量讨论，但主流媒体尚未报道 - - 介于领先信号和新闻驱动之间 +3. **SOCIAL_DRIVEN**: Price movement driven by social media discussion + - Significant Twitter discussion, but mainstream media has not yet reported + - Between leading signal and news-driven -4. **SPECULATION（投机波动）**：无明显信息来源的波动 - - 搜索不到相关新闻或讨论 - - 可能是纯粹的市场投机 +4. **SPECULATION**: No clear information source for the movement + - No related news or discussions found + - Likely pure market speculation -**重要原则**： -- 务必仔细分析提供的 Web 搜索结果中的最新新闻 -- 仔细分析 Twitter 搜索结果 -- 特别关注新闻和讨论的时间戳 -- 如果是 LEADING_SIGNAL，详细记录证据""" +**Key principles**: +- Carefully analyze the Web search results provided +- Carefully analyze the Twitter search results +- Pay special attention to timestamps of news and discussions +- If it's a LEADING_SIGNAL, document evidence in detail""" @staticmethod def analyze_volatility( @@ -95,7 +95,7 @@ class VolatilityAnalyzerPrompts: Returns: Complete prompt for LLM """ - direction_cn = "上涨" if direction == "UP" else "下跌" + direction_label = "UP" if direction == "UP" else "DOWN" window_minutes = window_seconds // 60 web_search_section = "" @@ -103,9 +103,9 @@ class VolatilityAnalyzerPrompts: web_search_section = f""" --- -## Web 搜索结果（新闻与分析） +## Web Search Results (News & Analysis) -以下是与该市场相关的最新网络搜索结果，请仔细分析发布时间和内容： +The following are recent web search results related to this market. Please carefully analyze publication timestamps and content: {web_search_context} @@ -117,106 +117,106 @@ class VolatilityAnalyzerPrompts: twitter_section = f""" --- -## Twitter 社交媒体搜索结果 +## Twitter Social Media Search Results -以下是与该市场相关的 Twitter 实时讨论，请仔细分析发布时间和内容： +The following are real-time Twitter discussions related to this market. Please carefully analyze timestamps and content: {twitter_context} --- """ - return f"""## 价格异常波动检测报告 + return f"""## Anomalous Price Movement Detection Report -### 波动详情 -- **市场问题**: {market_question} -- **价格变动**: {direction_cn} {abs(price_change_percent):.1%} -- **起始价格**: {start_price:.2%} -- **结束价格**: {end_price:.2%} -- **时间窗口**: {window_minutes} 分钟内 -- **检测时间**: {detected_at} +### Movement Details +- **Market question**: {market_question} +- **Price change**: {direction_label} {abs(price_change_percent):.1%} +- **Start price**: {start_price:.2%} +- **End price**: {end_price:.2%} +- **Time window**: Within {window_minutes} minutes +- **Detection time**: {detected_at} {web_search_section}{twitter_section} --- -# 价格波动验证任务 +# Price Movement Verification Task -你检测到了一次显著的价格异常波动，请判断这是否是一个"领先于新闻"的信号。 +A significant anomalous price movement has been detected. Please determine whether this is a "price leads news" signal. --- -## 第一步：Web 搜索结果分析（必须分析！） +## Step 1: Web Search Results Analysis (mandatory!) -**请仔细分析上文提供的 Web 搜索结果，重点关注：** +**Please carefully analyze the Web search results provided above, focusing on:** -1. 与"{market_question}"相关的最新新闻（重点关注过去24小时） -2. 可能触发这次价格波动的事件或公告 -3. 每条新闻的发布时间 +1. Latest news related to "{market_question}" (focus on the past 24 hours) +2. Events or announcements that may have triggered this price movement +3. Publication timestamp of each news item -**Web 搜索结果摘要**： -（请在此列出搜索结果中的关键新闻，必须包含发布时间） +**Web search results summary**: +(Please list key news from search results here, MUST include publication times) --- -## 第二步：Twitter 社交媒体分析 +## Step 2: Twitter Social Media Analysis -**分析上文提供的 Twitter 搜索结果：** +**Analyze the Twitter search results provided above:** -1. 最早的相关讨论是什么时候？ -2. 讨论的主要内容是什么？ -3. 是否有 KOL 或内部人士发言？ -4. 社交媒体讨论是否早于主流新闻报道？ +1. When was the earliest related discussion? +2. What were the main topics discussed? +3. Were there any KOL or insider posts? +4. Did social media discussion precede mainstream news coverage? -**Twitter 分析摘要**： -（请在此总结 Twitter 上的关键信息和时间线） +**Twitter analysis summary**: +(Please summarize key information and timeline from Twitter here) --- -## 第三步：时间线对比分析 +## Step 3: Timeline Comparison Analysis -**关键问题**：价格波动发生在新闻公开之前还是之后？ +**Key question**: Did the price movement occur before or after news became public? -- 价格波动检测时间: {detected_at} -- 找到的最早相关新闻发布时间: [请填写] -- 找到的最早社交媒体讨论时间: [请填写] +- Price movement detection time: {detected_at} +- Earliest related news publication time found: [please fill in] +- Earliest social media discussion time found: [please fill in] -**时间线结论**： -（价格波动是领先于新闻，还是滞后于新闻？） +**Timeline conclusion**: +(Did the price movement lead or lag the news?) --- -## 第四步：最终判定 +## Step 4: Final Determination -基于以上分析，给出你的判断，并用以下 JSON 格式输出： +Based on the above analysis, provide your judgment in the following JSON format: ```json {{ "signal_type": "LEADING_SIGNAL/NEWS_DRIVEN/SOCIAL_DRIVEN/SPECULATION", - "confidence": 0.0-1.0之间的数字, + "confidence": 0.0-1.0, "is_leading_signal": true/false, "news_found": true/false, - "earliest_news_time": "找到的最早相关新闻的发布时间，格式 YYYY-MM-DD HH:MM UTC，如无则为 null", - "earliest_social_time": "找到的最早社交媒体讨论时间，格式 YYYY-MM-DD HH:MM UTC，如无则为 null", - "time_advantage_minutes": 价格领先于新闻的分钟数（如果是领先信号），否则为 0, - "key_news_headlines": ["相关新闻标题1", "相关新闻标题2"], - "key_social_posts": ["关键社交媒体帖子摘要1", "关键社交媒体帖子摘要2"], - "reasoning": "简要说明你的判断依据", - "potential_information_source": "推测的信息来源（如：内部人士、社交媒体泄露、官方提前通知等）" + "earliest_news_time": "earliest related news publication time found, format YYYY-MM-DD HH:MM UTC, or null if none", + "earliest_social_time": "earliest social media discussion time found, format YYYY-MM-DD HH:MM UTC, or null if none", + "time_advantage_minutes": minutes price led news (if leading signal), otherwise 0, + "key_news_headlines": ["related news headline 1", "related news headline 2"], + "key_social_posts": ["key social media post summary 1", "key social media post summary 2"], + "reasoning": "brief explanation of your judgment basis", + "potential_information_source": "hypothesized information source (e.g., insider, social media leak, advance official notice, etc.)" }} ``` -**判断标准**： -- **LEADING_SIGNAL**: 价格波动发生时，Web 搜索不到相关新闻，或新闻发布时间明显晚于价格波动（>=30分钟） -- **NEWS_DRIVEN**: 找到了明确相关的新闻，且新闻发布时间早于或接近价格波动时间 -- **SOCIAL_DRIVEN**: Twitter 上有早期讨论，但主流媒体尚未报道 -- **SPECULATION**: 既没有新闻也没有社交讨论，可能是纯投机 +**Judgment criteria**: +- **LEADING_SIGNAL**: At the time of price movement, web search finds no related news, or news publication time is significantly later than price movement (>=30 minutes) +- **NEWS_DRIVEN**: Clear related news found, with publication time before or close to the price movement time +- **SOCIAL_DRIVEN**: Early Twitter discussion found, but mainstream media has not yet reported +- **SPECULATION**: Neither news nor social discussion found, likely pure speculation -**特别注意**： -- is_leading_signal 为 true 时，必须详细说明证据 -- time_advantage_minutes 表示价格领先于新闻的时间优势 -- 这个数据将用于构建"价格领先于新闻"的研究数据集 +**Special notes**: +- When is_leading_signal is true, detailed evidence must be provided +- time_advantage_minutes represents the time advantage of price over news +- This data will be used to build a "price leads news" research dataset --- -⚠️ 免责声明：本分析仅供研究参考，不构成投资建议。""" +Disclaimer: This analysis is for research purposes only and does not constitute investment advice.""" diff --git a/src/prompts/whale_analyzer.py b/src/prompts/whale_analyzer.py index 4ec320c..8d06d00 100644 --- a/src/prompts/whale_analyzer.py +++ b/src/prompts/whale_analyzer.py @@ -11,131 +11,131 @@ class WhaleAnalyzerPrompts: """System prompt for whale trade analysis with tool-use.""" current_utc = datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S UTC') - return f"""你是一位专业的预测市场分析师和信息不对称识别专家，专门分析 Polymarket 上的大额异常交易。 + return f"""You are a professional prediction market analyst and information asymmetry detection expert, specializing in analyzing large anomalous trades on Polymarket. -**当前真实时间**：{current_utc} +**Current real time**: {current_utc} -## 你的核心任务 +## Your Core Task -验证一笔"疑似异常交易"是否存在信息不对称（即交易者可能掌握了市场尚未反映的信息优势）。 +Verify whether a flagged anomalous trade exhibits information asymmetry — i.e., whether the trader may possess information not yet reflected in market prices. -## 你会收到的数据 +## Data You Will Receive -每次分析任务，你将收到以下结构化数据（在 user message 中）： +For each analysis task, you will receive the following structured data (in the user message): -1. **交易详情** — 触发告警的鲸鱼交易：金额、方向（BUY Yes 或 BUY No）、买入价格、时间、交易者钱包地址、异常评分 -2. **交易解读** — 方向含义（看多/看空）、隐含概率 -3. **交易者画像（Trader Profile JSON）** — 包含交易者的原始数据： - - `ranking`：排名、PnL、总交易量、是否验证、用户名 - - `behavior`：总交易次数、总交易量、平均交易金额、大额交易次数及占比、活跃市场 - - `recent_trades`：近期交易记录 -6. **该鲸鱼在同一事件下其他市场的持仓** — 用于判断是否存在对冲、关联押注或套利（数据来自 Polymarket 持仓 API，是实时真实持仓） -7. **该市场 Top 5 多空双方交易者** — 看多方和看空方各 Top 5 交易者的排名、PnL、净交易量（反映聪明钱共识方向） -8. **市场信息** — 市场问题、描述、可能结果、当前赔率 -9. **历史异常信号**（如有） — 该市场过去检测到的异常交易信号，用于趋势对比 +1. **Trade Details** — The whale trade that triggered the alert: amount, direction (BUY Yes or BUY No), purchase price, timestamp, trader wallet address, anomaly score +2. **Trade Interpretation** — Direction meaning (bullish/bearish), implied probability +3. **Trader Profile (JSON)** — Raw data about the trader: + - `ranking`: rank, PnL, total volume, verification status, username + - `behavior`: total trades, total volume, average trade size, large trade count and ratio, active markets + - `recent_trades`: recent trading records +6. **Whale's positions in other markets under the same event** — For detecting hedging, correlated bets, or arbitrage (real-time data from Polymarket positions API) +7. **Market Top 5 buyers and sellers** — Top 5 traders on each side with ranking, PnL, net volume (reflects smart money consensus direction) +8. **Market Information** — Market question, description, possible outcomes, current odds +9. **Historical anomaly signals** (if any) — Past anomalous trade signals detected on this market, for trend comparison -**你需要综合以上所有数据进行分析，不要忽略任何一个维度。** +**You must synthesize ALL of the above data in your analysis — do not neglect any dimension.** -## 可用工具 +## Available Tools -你可以调用以下工具来获取实时信息（所有结果都是真实的实时数据）： +You can call the following tools to obtain real-time information (all results are real live data): -- **search_web**: 搜索网络新闻和分析文章。适用于：验证事件、官方公告、监管新闻、财报、法院裁决、立法进度等。 -- **search_twitter**: 搜索 Twitter/X 社交媒体。适用于：实时舆情、KOL 观点、加密社区反应、突发消息等。 -- **search_telegram**: 搜索 Telegram 频道（吴说区块链、Whale Alert、Polymarket 官方及新闻频道等）。适用于：加密货币内幕消息、代币发行公告、鲸鱼链上转账提醒，以及 Polymarket 社区对各类市场（地缘政治、经济、政治等）的讨论和情报。 -- **get_crypto_price**: 获取加密货币实时行情（价格、24h/7d/30d 涨跌幅、市值、成交量、ATH）。适用于：涉及加密货币价格目标的市场（如"BTC 是否会达到 $100k"）。 -- **get_crypto_market_overview**: 获取全球加密市场概览（总市值、BTC/ETH 占比、24h 变化）。适用于：判断整体加密市场情绪。 -- **get_economic_data**: 获取 FRED 宏观经济数据。支持：fed_rate、cpi、unemployment、gdp、oil_price、wti、brent、gold、vix、sp500、yield_curve、jobless_claims 等。适用于：Fed 政策市场、通胀市场、就业数据、原油/商品价格、衰退指标。 -- **get_stock_price**: 获取股票/ETF 实时行情快照（价格、涨跌幅、成交量）。支持：AAPL、TSLA、GS、SPY、QQQ、GLD、USO 等。适用于：涉及具体公司或行业的市场。 -- **get_stock_news**: 获取股票/公司的最新新闻。适用于：公司事件（IPO、财报、诉讼、并购）、CEO 言论、监管行动。 -- **get_bill_status**: 获取美国国会特定法案的状态（需要 congress 编号、法案类型和编号）。适用于：涉及具体立法的市场（如 TikTok 禁令、加密货币监管、移民法案）。 -- **get_recent_legislation**: 获取最近更新的美国国会法案列表。适用于：了解当前立法动态、政治类市场。 -- **get_protocol_tvl**: 获取 DeFi 协议 TVL（锁仓量）、TVL 变化（1h/24h/7d）、链分布。适用于：代币发行 FDV 市场、DeFi 协议基本面评估、项目健康度判断。 -- **get_token_unlocks**: 获取代币解锁/归属时间表。适用于：评估代币供应动态、FDV 市场、预判解锁卖压。 -- **get_protocol_revenue**: 获取 DeFi 协议费用和收入（24h/7d/30d/历史总计）。适用于：评估协议基本面、对比收入与 FDV 是否合理。 -- **get_wallet_transfers**: 获取以太坊钱包的近期 ERC-20 代币转账（USDC/USDT/WETH/DAI）。适用于：检查鲸鱼是否刚收到大额 USDC 转入（为交易准备资金）、追踪钱包资金流向。 -- **get_contract_info**: 查询以太坊地址是否为智能合约、合约名称、验证状态。适用于：验证项目是否已部署合约、判断代币发行市场的项目进度。 +- **search_web**: Search web news and analysis articles. Use for: event verification, official announcements, regulatory news, earnings, court rulings, legislative progress, etc. +- **search_twitter**: Search Twitter/X social media. Use for: real-time sentiment, KOL opinions, crypto community reactions, breaking news, etc. +- **search_telegram**: Search Telegram channels (WuBlockchain, Whale Alert, Polymarket official & news channels, etc.). Use for: crypto intelligence, token launch announcements, whale on-chain transfer alerts, and Polymarket community discussions on geopolitics, economics, politics, etc. +- **get_crypto_price**: Get real-time crypto prices (price, 24h/7d/30d change, market cap, volume, ATH). Use for: markets involving crypto price targets (e.g., "Will BTC reach $100k"). +- **get_crypto_market_overview**: Get global crypto market overview (total market cap, BTC/ETH dominance, 24h change). Use for: gauging overall crypto sentiment. +- **get_economic_data**: Get FRED macroeconomic data. Supports: fed_rate, cpi, unemployment, gdp, oil_price, wti, brent, gold, vix, sp500, yield_curve, jobless_claims, etc. Use for: Fed policy, inflation, employment, commodities, recession indicators. +- **get_stock_price**: Get stock/ETF real-time snapshot (price, change, volume). Supports: AAPL, TSLA, GS, SPY, QQQ, GLD, USO, etc. Use for: markets involving specific companies or sectors. +- **get_stock_news**: Get latest stock/company news. Use for: company events (IPO, earnings, lawsuits, M&A), CEO statements, regulatory actions. +- **get_bill_status**: Get US Congress bill status (requires congress number, bill type, and number). Use for: markets involving specific legislation (e.g., TikTok ban, crypto regulation, immigration bills). +- **get_recent_legislation**: Get recently updated US Congress bills. Use for: current legislative dynamics, political markets. +- **get_protocol_tvl**: Get DeFi protocol TVL, TVL changes (1h/24h/7d), chain distribution. Use for: token FDV markets, DeFi fundamentals, project health assessment. +- **get_token_unlocks**: Get token unlock/vesting schedules. Use for: token supply dynamics, FDV markets, predicting unlock sell pressure. +- **get_protocol_revenue**: Get DeFi protocol fees and revenue (24h/7d/30d/all-time). Use for: protocol fundamentals, comparing revenue to FDV. +- **get_wallet_transfers**: Get recent ERC-20 token transfers from an Ethereum wallet (USDC/USDT/WETH/DAI). Use for: checking if whale just received large USDC inflow (funding preparation), tracking wallet fund flows. +- **get_contract_info**: Query whether an Ethereum address is a smart contract, contract name, verification status. Use for: verifying project contract deployment, judging token launch market project progress. -**工具使用原则**： -- 根据市场类型和交易特征，自行判断需要调用哪些工具 -- 可以调用一个、多个或零个工具 -- 可以用不同的关键词多次调用同一工具 -- 如果交易金额特别大或信息不对称嫌疑高，应更积极地搜索验证 +**Tool usage principles**: +- Based on market type and trade characteristics, decide which tools to call +- You may call one, multiple, or zero tools +- You may call the same tool multiple times with different keywords +- If trade size is very large or information asymmetry suspicion is high, search more aggressively -**工具协作与交叉验证（重要）**： -- 不同工具获取到的信息必须**交叉验证**，不要仅凭单一信息源下结论。例如：网页搜索发现某政策传闻，应再用 Twitter 搜索验证舆论反应，用经济数据佐证影响 -- 在使用一个工具的过程中，如果发现了新的线索或关键词，**应立即调用其他工具追查**。例如：搜索新闻发现某官员辞职，应继续搜索该官员的名字获取更多细节，同时搜索 Twitter 看是否有未被报道的内部消息 -- 多个工具的结果**互相矛盾**时，应明确标注分歧并降低信心，而非选择性采信 -- 鼓励"搜索链"式调查：第一轮搜索→发现线索→针对性二轮搜索→深入三轮搜索，逐层深入而非浅尝辄止 +**Tool collaboration and cross-verification (important)**: +- Information from different tools must be **cross-verified** — do not draw conclusions from a single source. Example: if web search finds a policy rumor, verify with Twitter for public reaction and corroborate with economic data +- If you discover new leads or keywords while using one tool, **immediately call other tools to follow up**. Example: if news search reveals an official's resignation, search for that person's name for more details and check Twitter for unreported information +- When multiple tools return **contradictory results**, explicitly note the discrepancy and lower confidence — do not cherry-pick +- Encourage "search chain" investigation: first-round search → discover leads → targeted second-round → deep third-round, progressing layer by layer rather than skimming the surface -## Polymarket 交易机制 +## Polymarket Trading Mechanics -交易数据为 taker 的真实买入行为（已过滤掉 SELL/平仓交易），**无任何归一化处理**： -- **BUY Yes** = 买入 Yes Token = **看多**（认为事件会发生） -- **BUY No** = 买入 No Token = **看空**（认为事件不会发生） -- **价格**为 taker 实际买入价格（0.0~1.0），越低说明赔率越高、不确定性越大 - - 例如 BUY Yes @ 0.06 = 花 $0.06 买一份，若事件发生获得 $1（约17倍赔率） - - 例如 BUY No @ 0.30 = 花 $0.30 买一份，若事件不发生获得 $1（约3.3倍赔率） -- **交易金额**（usdc_size）为 taker 的真实 USDC 花费 -- 我们只关注买入价 ≤ 0.7 的交易（高价买入确定性太高，无信号价值） +Trade data represents taker's actual buy actions (SELL/close trades are filtered out), **no normalization applied**: +- **BUY Yes** = Buy Yes Token = **Bullish** (believes event will occur) +- **BUY No** = Buy No Token = **Bearish** (believes event will not occur) +- **Price** is taker's actual purchase price (0.0~1.0) — lower price means higher odds and more uncertainty + - Example: BUY Yes @ 0.06 = pay $0.06 per share, receive $1 if event occurs (~17x odds) + - Example: BUY No @ 0.30 = pay $0.30 per share, receive $1 if event doesn't occur (~3.3x odds) +- **Trade amount** (usdc_size) is taker's actual USDC spend +- We only monitor trades with buy price <= 0.7 (high-price buys on near-certain outcomes have no signal value) -## 分析框架 +## Analysis Framework -### 交易者可信度 -交易者可信度（HIGH/MEDIUM/LOW/UNKNOWN）应综合所有可用的原始数据评定，不要仅依据单一指标。评定时请考虑： -- **排名**：排名越靠前（数字越小），交易者越可能是经验丰富的参与者。null 表示未上榜 -- **PnL**：累计盈亏金额直接反映交易者的历史表现，高 PnL 比高排名更能说明实力 -- **交易行为**：总交易次数、平均交易金额、大额交易占比等反映交易者的风格和经验 -- **活跃市场**：近期参与的市场类型反映交易者的专长领域，与当前市场主题是否匹配 -- **近期交易记录**：具体的买卖方向、金额和价格，帮助判断交易者的策略模式 +### Trader Credibility +Trader credibility (HIGH/MEDIUM/LOW/UNKNOWN) should be assessed comprehensively using all available raw data — do not rely on a single metric. Consider: +- **Ranking**: Lower rank number = more experienced participant. null means unranked +- **PnL**: Cumulative profit/loss directly reflects historical performance — high PnL is stronger evidence than high rank +- **Trading behavior**: Total trades, average trade size, large trade ratio reflect style and experience +- **Active markets**: Recent market types reflect the trader's domain expertise — is it relevant to the current market? +- **Recent trades**: Specific buy/sell directions, amounts, and prices help identify the trader's strategy pattern -### 信息不对称可信度判断标准（必须严格遵守） +### Information Asymmetry Scoring Criteria (must be strictly followed) -**"信息不对称"的定义非常严格**：交易者必须掌握了市场尚未反映的、非公开的、具体的信息（如未公布的政策决定、未发布的数据、私下谈判结果等）。仅仅是"聪明的分析"、"经验丰富"或"排名高"都**不构成**信息不对称。 +**"Information asymmetry" has a very strict definition**: The trader must possess information not yet reflected in the market — non-public, specific information (e.g., unannounced policy decisions, unreleased data, private negotiation outcomes). Simply being "a smart analyst", "experienced", or "highly ranked" does **NOT** constitute information asymmetry. -**评分校准基准（大多数交易应落在 0.2-0.5 之间）**： +**Score calibration benchmark (most trades should fall between 0.2-0.5)**: -- **0.8-1.0（极高）**: 仅当发现**明确的非公开信息证据**时才可给出。例如：交易时间精准在重大公告前数小时，且该公告完全不可预测；或交易者有已知的信息渠道（如政治内部人士身份）。**极少数交易应达到此级别。** -- **0.6-0.8（高）**: 高排名交易者 + 交易时机与即将发生的未定价事件高度吻合 + 搜索发现了市场尚未充分反映的具体信息。需要多个强证据同时满足。 -- **0.4-0.6（中等）**: 高排名交易者的大额交易 + 有一定信息支撑但不确定是否为内幕。这是**大多数有一定可疑度的交易**应该落在的区间。 -- **0.2-0.4（低）**: 有一些异常特征但缺乏信息支撑，或交易者排名一般。**大多数普通鲸鱼交易**应该在这个范围。 -- **0.0-0.2（极低）**: 未上榜交易者的常规交易，无任何异常信号。 +- **0.8-1.0 (Very High)**: Only when **clear evidence of non-public information** is found. Example: trade timing precisely hours before a major announcement that was completely unpredictable; or trader has known information channels (e.g., identified as a political insider). **Very few trades should reach this level.** +- **0.6-0.8 (High)**: High-ranked trader + trade timing highly aligned with an upcoming unpriced event + search reveals specific information not yet reflected in market. Multiple strong pieces of evidence must be present simultaneously. +- **0.4-0.6 (Medium)**: High-ranked trader's large trade + some information support but uncertainty about whether it's non-public. This is where **most moderately suspicious trades** should fall. +- **0.2-0.4 (Low)**: Some anomalous features but lacking information support, or trader ranking is average. **Most ordinary whale trades** should be in this range. +- **0.0-0.2 (Very Low)**: Unranked trader's routine trade, no anomalous signals. -**常见的错误高估场景（必须避免）**： -- ❌ 仅因为交易者排名高就给 0.7+（高排名交易者每天做很多交易，绝大多数不存在信息不对称） -- ❌ 仅因为交易金额大就给 0.6+（大额交易是鲸鱼的常规操作） -- ❌ 短期价格预测市场（如"Bitcoin Up or Down 5分钟"）给高分（这类市场几乎不可能有内幕信息） -- ❌ 临近到期的市场、价格接近 0 或 1 的交易给高分（这通常是市场共识的正常体现） -- ❌ 搜索到的信息都是公开新闻时给高分（公开信息 ≠ 内幕信息） -- ❌ 大型地缘政治/宏观市场（如伊朗局势、总统弹劾等）轻易给高分 — 这类市场参与者众多、信息源复杂，鲸鱼交易大多反映公开分析而非内幕 +**Common overestimation mistakes (must avoid)**: +- Do NOT give 0.7+ just because the trader ranks high (high-ranked traders make many trades daily, the vast majority show no information asymmetry) +- Do NOT give 0.6+ just because the trade amount is large (large trades are routine for whales) +- Do NOT give high scores to short-term price prediction markets (e.g., "Bitcoin Up or Down 5 minutes") — these markets almost never involve non-public information +- Do NOT give high scores to near-expiry markets or trades with prices near 0 or 1 — this usually reflects normal market consensus +- Do NOT give high scores when all found information is public news (public information ≠ non-public information) +- Do NOT easily give high scores to large geopolitical/macro markets (e.g., Iran situation, presidential impeachment) — these markets have many participants and complex information sources; whale trades mostly reflect public analysis rather than non-public information -**应该重点关注的高价值场景**： -- ✅ **小众市场**（日交易量 < $500k）的大额交易 — 参与者少、信息差大、鲸鱼信号更有意义 -- ✅ **新项目/代币发行**（FDV、TGE、公售）— 项目方和早期投资者可能有未公开信息 -- ✅ **具体可验证事件**（某人是否会做某事、某公司是否会公布某决定）— 知情者范围小、信息明确 -- ✅ **冷门市场突然出现高排名交易者大额交易** — 反常行为是最强信号 +**High-value scenarios to focus on**: +- **Niche markets** (daily volume < $500k) with large trades — fewer participants, larger information gap, whale signals more meaningful +- **New projects/token launches** (FDV, TGE, public sale) — project teams and early investors may have non-public information +- **Specific verifiable events** (will someone do something, will a company announce a decision) — small circle of insiders, clear information +- **Quiet markets suddenly attracting high-ranked traders with large trades** — anomalous behavior is the strongest signal -## 事件关联持仓分析 +## Event-Related Position Analysis -交易数据中会包含鲸鱼在同一事件（Event）下其他市场的持仓情况。你需要综合分析： -- **对冲识别**：如果鲸鱼在同一事件的不同市场持有反向仓位，可能是对冲策略而非单方向押注，应降低信息不对称评分 -- **关联押注**：如果鲸鱼在同一事件的多个市场持有同向仓位（如同时看多多个相关市场），这增强了信号强度 -- **套利行为**：同一事件下价格不一致时，鲸鱼可能在做套利，这不是信息不对称信号 +Trade data includes the whale's positions in other markets under the same Event. You must analyze: +- **Hedge detection**: If the whale holds opposing positions in different markets under the same event, it may be a hedging strategy rather than a directional bet — lower information asymmetry score +- **Correlated bets**: If the whale holds same-direction positions across multiple markets under the same event (e.g., bullish on multiple related markets), this strengthens the signal +- **Arbitrage**: Price inconsistencies across markets under the same event may indicate arbitrage — this is not an information asymmetry signal -## 市场多空力量分析 +## Market Long/Short Analysis -交易数据中会包含该市场 Top 5 买方和卖方的排名与持仓。你需要分析： -- **聪明钱共识**：如果多个高排名、高盈利的交易者站在同一方，信号更强 -- **对手方分析**：如果鲸鱼的对手方都是低排名交易者，信号更可靠；如果对手方也有高排名交易者，则需要更谨慎 -- **市场集中度**：如果某一方的持仓高度集中在少数大户，市场可能更容易出现剧烈波动 +Trade data includes the market's Top 5 buyers and sellers with rankings and positions. Analyze: +- **Smart money consensus**: If multiple high-ranked, high-PnL traders are on the same side, the signal is stronger +- **Counterparty analysis**: If the whale's counterparties are all low-ranked traders, the signal is more reliable; if counterparties include high-ranked traders, more caution is needed +- **Market concentration**: If one side's positions are heavily concentrated in a few large holders, the market may be more prone to sharp volatility -## 重要原则 -- 主动使用工具获取最新信息来验证交易 -- 搜索不到支持信息时，内幕可能性应降低 -- 信心不足时建议 HOLD -- 鲸鱼也可能犯错或有其他动机（对冲、试探等） -- 综合事件关联持仓和市场多空力量做出更全面的判断 -- **时间判断**：不要猜测未知的事件时间（如比赛开始时间）。如果需要判断交易发生在事件之前还是之后，必须用工具搜索确认事件时间，而非凭空推测""" +## Key Principles +- Proactively use tools to gather latest information for trade verification +- When searches yield no supporting information, information asymmetry likelihood should decrease +- When confidence is low, recommend HOLD +- Whales can also be wrong or have other motivations (hedging, probing, etc.) +- Synthesize event-related positions and market long/short dynamics for comprehensive judgment +- **Time judgment**: Do NOT guess unknown event times (e.g., match start times). If you need to determine whether a trade occurred before or after an event, you MUST use tools to confirm the event time — never speculate""" @staticmethod def analyze_whale_trade(trade_context: str, historical_context: str = "") -> str: @@ -164,152 +164,152 @@ class WhaleAnalyzerPrompts: --- -# 鲸鱼交易验证任务 +# Whale Trade Verification Task -## 第 0 步：预筛选（必须首先完成） +## Step 0: Pre-screening (must complete first) -在进行任何搜索和分析之前，先判断这笔信号是否值得生成完整报告。 +Before any search or analysis, determine whether this signal warrants a full report. -**筛选标准：** -- **优先分析（低门槛）**: 小众市场、加密货币/代币发行相关（FDV、TGE、公售、协议治理）、具体可验证事件、冷门市场突然出现大额交易 -- **门槛更高（需要信号特别强）**: 大型地缘政治市场（战争、制裁、外交）、宏观经济/Fed利率/选举等参与者众多的大市场 -- **直接跳过**: 体育/赛事结果、价格已接近 0 或 1 的市场（≥0.95 或 ≤0.05） +**Screening criteria:** +- **Prioritize (low threshold)**: Niche markets, crypto/token launch related (FDV, TGE, public sale, protocol governance), specific verifiable events, quiet markets with sudden large trades +- **Higher threshold (need especially strong signals)**: Large geopolitical markets (war, sanctions, diplomacy), macro/Fed rate/election markets with many participants +- **Skip directly**: Sports/game results, markets with price near 0 or 1 (>=0.95 or <=0.05) -综合交易金额、交易者排名和画像、异常评分、市场类型判断。 +Assess holistically based on trade amount, trader rank and profile, anomaly score, and market type. -**如果判定不值得分析，直接输出以下 JSON 并结束，不要进行后续步骤：** +**If deemed not worth analyzing, output the following JSON and stop — do not proceed to subsequent steps:** ```json -{{{{"action": "SKIP", "reason": "一句话理由"}}}} +{{{{"action": "SKIP", "reason": "one-line reason"}}}} ``` -**如果判定值得分析，继续以下步骤。** +**If deemed worth analyzing, continue with the following steps.** --- -## 请完成以下步骤： +## Complete the following steps: -### 1. 信息搜集 -根据市场主题，使用可用工具搜索相关信息： -- 该市场主题的最新新闻和动态 -- 社交媒体上的讨论和舆情 -- 任何可能触发这笔交易的事件 +### 1. Information Gathering +Based on market topic, use available tools to search for relevant information: +- Latest news and developments on the market topic +- Social media discussions and sentiment +- Any events that may have triggered this trade -### 2. 交易信号分析 -- 交易者排名和历史盈亏表现 -- 结构化画像（排名、PnL、交易行为数据、近期交易记录） -- 交易时机是否异常 +### 2. Trade Signal Analysis +- Trader ranking and historical P&L performance +- Structured profile (ranking, PnL, trading behavior data, recent trades) +- Whether the trade timing is anomalous -### 3. 事件关联持仓分析 -- 该鲸鱼在同一事件的其他市场是否有持仓？ -- 如果有反向持仓（如同时持有 Yes 和 No，或在相关市场对冲），可能是对冲/套利策略，应降低信息不对称评分 -- 如果同方向押注多个关联市场，则信号增强 +### 3. Event-Related Position Analysis +- Does the whale have positions in other markets under the same event? +- If opposing positions exist (e.g., holding both Yes and No, or hedging in related markets), it may be a hedge/arbitrage strategy — lower information asymmetry score +- If same-direction bets across multiple related markets, the signal is strengthened -### 4. 市场多空力量分析 -- Top 5 看多方和看空方分别是谁？排名如何？ -- 高排名、高盈利的交易者集中在哪一方？这代表聪明钱的共识 -- 该鲸鱼的对手方资质如何？如果对手方也有高排名交易者，需更谨慎 +### 4. Market Long/Short Analysis +- Who are the Top 5 on each side? What are their rankings? +- Which side has the concentration of high-ranked, high-PnL traders? This represents smart money consensus +- What is the quality of the whale's counterparties? If counterparties also include high-ranked traders, be more cautious -### 5. 信息差分析 -- 搜索到的信息是否支持这笔交易的方向？ -- 这些信息是否已被市场完全定价？ -- 如存在信息差，幅度有多大？ +### 5. Information Gap Analysis +- Does the information found support the trade's direction? +- Has this information been fully priced by the market? +- If an information gap exists, how large is it? -### 6. 历史信号对比（如有） -- 历史信号与当前信号的方向是否一致？ -- 是否有高排名交易者参与？ -- 交易金额和价格的趋势如何？ +### 6. Historical Signal Comparison (if available) +- Are historical signals directionally consistent with the current signal? +- Were high-ranked traders involved? +- What are the trends in trade amounts and prices? -### 7. 信息不对称评估 +### 7. Information Asymmetry Assessment -评估交易者相对于公开信息的信息优势。核心逻辑： -- I_public = 你通过所有工具能获取到的公开信息集合 -- I_trader = 交易者做出该交易决策所依据的信息集合 -- 信息不对称 = I_trader - I_public -- 如果公开信息已能充分解释交易行为 → 分数低 -- 如果公开信息无法解释交易行为（交易者可能有额外信息源、领域专长、数据速度优势）→ 分数高 +Assess the trader's information advantage relative to public information. Core logic: +- I_public = the set of public information you can obtain through all tools +- I_trader = the set of information the trader used to make this trade decision +- Information asymmetry = I_trader - I_public +- If public information can fully explain the trade behavior → low score +- If public information cannot explain the trade behavior (trader may have additional sources, domain expertise, data speed advantage) → high score -输出 JSON 格式评估： +Output JSON assessment: ```json {{ "information_asymmetry_score": 0.0-1.0, "trader_credibility": "HIGH/MEDIUM/LOW/UNKNOWN", - "reasoning": "简要推理过程", - "insider_evidence": "关键证据" + "reasoning": "brief reasoning process", + "insider_evidence": "key evidence" }} ``` -注意： -- information_asymmetry_score 必须严格校准：大多数交易应在 0.2-0.5，只有发现明确的信息优势证据时才给 0.7+ -- 信息优势包括但不限于：领域专长、数据源速度差、非公开渠道、精准的时机把握 -- 仅凭交易者排名高或交易金额大，information_asymmetry_score 不应超过 0.5 -- 确保输出有效 JSON""" +Notes: +- information_asymmetry_score must be strictly calibrated: most trades should be 0.2-0.5, only give 0.7+ when clear evidence of information advantage is found +- Information advantage includes but is not limited to: domain expertise, data source speed difference, non-public channels, precise timing +- Trader ranking or trade size alone should NOT push information_asymmetry_score above 0.5 +- Ensure valid JSON output""" @staticmethod def superforecaster_prompt(question: str, description: str, outcomes: List[str]) -> str: """Superforecaster-style analysis prompt.""" outcomes_str = ", ".join(outcomes) - return f"""作为一名超级预测者，请对以下预测市场进行分析： + return f"""As a superforecaster, analyze the following prediction market: -**问题**: {question} +**Question**: {question} -**描述**: {description} +**Description**: {description} -**可能结果**: {outcomes_str} +**Possible Outcomes**: {outcomes_str} -请使用以下系统性方法进行预测： +Please use the following systematic approach: -### 1. 问题分解 -- 将问题分解为更小、更易管理的部分 -- 识别回答问题需要解决的关键组成部分 +### 1. Problem Decomposition +- Break the question into smaller, more manageable parts +- Identify key components needed to answer the question -### 2. 信息收集 -- 考虑相关的定量数据和定性见解 -- 思考最新的相关新闻和专家分析 +### 2. Information Gathering +- Consider relevant quantitative data and qualitative insights +- Think about the latest relevant news and expert analysis -### 3. 基础概率 -- 使用统计基线或历史平均值作为起点 -- 将当前情况与类似的历史事件进行比较 +### 3. Base Rate +- Use statistical baselines or historical averages as starting points +- Compare the current situation with similar historical events -### 4. 因素评估 -- 列出可能影响结果的因素 -- 评估每个因素的影响，考虑正面和负面因素 -- 使用证据权衡这些因素 +### 4. Factor Assessment +- List factors that may influence the outcome +- Assess each factor's impact, considering both positive and negative factors +- Weigh these factors using evidence -### 5. 概率思维 -- 用概率而非确定性表达预测 -- 为不同结果分配可能性 -- 承认不确定性 +### 5. Probabilistic Thinking +- Express predictions as probabilities, not certainties +- Assign likelihoods to different outcomes +- Acknowledge uncertainty -请为每个结果提供概率估计，确保所有概率之和为100%。 +Please provide probability estimates for each outcome, ensuring all probabilities sum to 100%. -输出格式： +Output format: ```json {{ - "analysis": "你的详细分析", + "analysis": "your detailed analysis", "probabilities": {{ - "结果1": 0.XX, - "结果2": 0.XX + "outcome1": 0.XX, + "outcome2": 0.XX }}, "confidence_level": "low/medium/high", - "key_factors": ["因素1", "因素2", "因素3"] + "key_factors": ["factor1", "factor2", "factor3"] }} ```""" @staticmethod def quick_decision_prompt(trade_summary: str) -> str: """Quick decision prompt for time-sensitive situations.""" - return f"""快速分析以下鲸鱼交易并给出建议： + return f"""Quickly analyze the following whale trade and provide a recommendation: {trade_summary} -请直接输出JSON格式的决策： +Output your decision in JSON format: ```json {{ "action": "BUY/SELL/HOLD", - "outcome": "交易的结果选项", + "outcome": "the outcome to trade on", "confidence": 0.0-1.0, - "reasoning": "一句话理由" + "reasoning": "one-line reason" }} ```""" diff --git a/src/services/anomaly_detector.py b/src/services/anomaly_detector.py index f164c7f..ec60bd7 100644 --- a/src/services/anomaly_detector.py +++ b/src/services/anomaly_detector.py @@ -250,9 +250,9 @@ class AnomalyDetector: # Direction interpretation (only BUY trades, no normalization) if trade.outcome == "Yes": - direction_meaning = f"交易者买入 Yes Token @ {trade.price:.4f}，看多（认为事件会发生）" + direction_meaning = f"Trader bought Yes Token @ {trade.price:.4f} — Bullish (believes event will occur)" else: - direction_meaning = f"交易者买入 No Token @ {trade.price:.4f}，看空（认为事件不会发生）" + direction_meaning = f"Trader bought No Token @ {trade.price:.4f} — Bearish (believes event will NOT occur)" # Buy price directly reflects taker's conviction — lower price = higher odds bet implied_prob = trade.price @@ -305,50 +305,50 @@ class AnomalyDetector: # Anomaly breakdown string bd = context["anomaly_breakdown"] breakdown_str = ( - f" 绝对金额: {bd.get('size_abs', 0):.2f} | " - f"相对市场: {bd.get('size_relative', 0):.2f} | " - f"价格不确定性: {bd.get('price_uncertainty', 0):.2f} | " - f"交易时间: {bd.get('time_of_day', 0):.2f} | " - f"交易者偏离: {bd.get('trader_deviation', 0):.2f} | " - f"聚集信号: {bd.get('cluster', 0):.2f}" + f" Absolute size: {bd.get('size_abs', 0):.2f} | " + f"Relative to market: {bd.get('size_relative', 0):.2f} | " + f"Price uncertainty: {bd.get('price_uncertainty', 0):.2f} | " + f"Time of day: {bd.get('time_of_day', 0):.2f} | " + f"Trader deviation: {bd.get('trader_deviation', 0):.2f} | " + f"Cluster signal: {bd.get('cluster', 0):.2f}" ) return f""" -## 大额交易异常检测报告 +## Whale Trade Anomaly Detection Report -### 交易详情 -- **交易金额**: ${context['trade_size_usd']:,.2f} USDC -- **交易方向**: BUY {context['trade_outcome']} Token ({'看多' if context['trade_outcome'] == 'Yes' else '看空'}) -- **买入价格**: {context['trade_price']:.4f}（赔率约 {1/context['trade_price']:.1f}x） -- **交易时间**: {datetime.fromtimestamp(trade.timestamp).strftime('%Y-%m-%d %H:%M:%S UTC')} -- **交易者钱包**: {trade.proxy_wallet or 'Unknown'} +### Trade Details +- **Trade amount**: ${context['trade_size_usd']:,.2f} USDC +- **Trade direction**: BUY {context['trade_outcome']} Token ({'Bullish' if context['trade_outcome'] == 'Yes' else 'Bearish'}) +- **Buy price**: {context['trade_price']:.4f} (~{1/context['trade_price']:.1f}x odds) +- **Trade time**: {datetime.fromtimestamp(trade.timestamp).strftime('%Y-%m-%d %H:%M:%S UTC')} +- **Trader wallet**: {trade.proxy_wallet or 'Unknown'} -### 异常评分 -- **综合评分**: {context['anomaly_score']:.2f}/1.00 -- **评分分解**: +### Anomaly Score +- **Overall score**: {context['anomaly_score']:.2f}/1.00 +- **Score breakdown**: {breakdown_str} -### 交易解读 -- **方向含义**: {context['direction_meaning']} +### Trade Interpretation +- **Direction**: {context['direction_meaning']} {trader_profile_str} -### 市场信息 -- **市场问题**: {context['market_question']} -- **市场描述**: {whale_trade.market_description or 'N/A'} -- **市场状态**: {context['market_state']} -- **当前赔率**: +### Market Information +- **Market question**: {context['market_question']} +- **Market description**: {whale_trade.market_description or 'N/A'} +- **Market state**: {context['market_state']} +- **Current odds**: {prices_str} {whale_trade.format_event_positions()} {whale_trade.format_top_traders()} -### 分析要点 -1. 这是一笔 ${context['trade_size_usd']:,.2f} 的大额交易，方向为 **BUY {context['trade_outcome']} Token** +### Analysis Points +1. This is a ${context['trade_size_usd']:,.2f} large trade, direction: **BUY {context['trade_outcome']} Token** 2. {context['direction_meaning']} -3. **重点分析上方的 Trader Profile JSON，综合排名、PnL、交易行为和近期交易记录判断交易者可信度** -4. **注意分析该鲸鱼在同一事件下的其他持仓** — 如果持有反向仓位可能是对冲策略 -5. **参考该市场 Top 多空持仓者的阵营** — 高排名交易者集中在哪一方 +3. **Focus on the Trader Profile JSON above — assess trader credibility from ranking, PnL, trading behavior, and recent trades** +4. **Analyze the whale's positions in other markets under the same event** — opposing positions may indicate hedging +5. **Reference the market's Top long/short holders** — which side has the concentration of high-ranked traders -请分析这笔交易的信息不对称可能性。 +Please analyze the information asymmetry likelihood of this trade. """ diff --git a/src/services/anomaly_history.py b/src/services/anomaly_history.py index d0fb651..8d40ef2 100644 --- a/src/services/anomaly_history.py +++ b/src/services/anomaly_history.py @@ -89,27 +89,27 @@ class AnomalyHistoryService: signal_count = len(signals) context = f""" -### 历史异常交易信号 (共 {signal_count} 笔) +### Historical Anomaly Signals ({signal_count} total) -**重要**: 该市场之前已经检测到 {signal_count} 笔异常交易。请将这些历史信号与当前最新信号一起进行综合分析，统一评估信息不对称可能性。 +**Important**: {signal_count} anomalous trades have been previously detected on this market. Please analyze these historical signals together with the current signal to provide a comprehensive information asymmetry assessment. """ for i, signal in enumerate(signals, 1): context += f""" --- -#### 历史信号 {i} +#### Historical Signal {i} {signal.to_context_string()} --- """ context += """ -**综合分析要点**: -1. 对比所有信号（历史+当前）的交易方向，分析是否有一致趋势 -2. 对比不同交易者的排名和历史记录，判断"聪明钱"的流向 -3. 如果多个高排名交易者都指向同一方向，信息不对称可能性显著提高 -4. 如果信号方向相反，需要分析原因（时间变化、新信息、不同判断） -5. 考虑时间因素：越近期的信号越有参考价值 -6. 观察交易金额的变化趋势：金额是否在增加？ +**Comprehensive Analysis Points**: +1. Compare trade directions across all signals (historical + current) — is there a consistent trend? +2. Compare different traders' rankings and histories — where is the "smart money" flowing? +3. If multiple high-ranked traders point in the same direction, information asymmetry likelihood increases significantly +4. If signal directions conflict, analyze reasons (time changes, new information, differing judgments) +5. Consider time factor: more recent signals are more relevant +6. Observe trade amount trends: are amounts increasing? """ return context