> ## Documentation Index > Fetch the complete documentation index at: https://aisa.one/docs/llms.txt > Use this file to discover all available pages before exploring further. # Trend Forecast > Forecast trends from prediction markets, social signals, news, and market data. [View on GitHub ->](https://github.com/AIsa-team/agent-skills/tree/main/trend-forecast) **Trend forecasts from multiple signals.** Combine prediction markets, social data, news, and market movement into confidence-scored analysis. ## Install First, install the AIsa CLI if you have not already: ```bash theme={null} npm install -g @aisa-one/cli ``` Then install the skill: ```bash theme={null} aisa skills install trend-forecast ``` ## What can agents do with it? Turn mixed signals into a concise trend forecast. Use Polymarket and Kalshi odds as probability evidence. Read X/Twitter movement around topics, entities, and narratives. Add stocks, crypto, and macro context where relevant. ## Context You are a trend forecasting agent. When the user asks about a topic's trajectory, outlook, or probability, you gather signals from five independent data sources through AIsa's unified API, then synthesize a forecast with a confidence score. This skill is NOT a web search tool. It is a **multi-signal aggregation engine** that pulls structured data from prediction markets, social media, news, and financial markets — then uses an LLM to synthesize a trend report. All endpoints share one auth header: `Authorization: Bearer $AISA_API_KEY`. The REST surface lives under `https://api.aisa.one/apis/v1`; the OpenAI-compatible LLM gateway lives under `https://api.aisa.one/v1` (note: no `/apis`). ## Example Prompts * "What's the outlook on the AI chip market over the next 6 months?" * "Will the Fed cut rates before September?" * "Forecast the trend for Tesla stock based on current sentiment" * "What are prediction markets saying about the 2026 midterms?" * "Trend analysis for remote work adoption — combine social, news, and market data" ## Environment ```bash theme={null} export AISA_API_KEY="your-aisa-api-key" ``` ## Architecture ``` ┌─────────────────────────────────────────────────────────┐ │ USER QUERY │ │ "What's the outlook on X?" │ └──────────────────────┬──────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────┐ │ QUERY DECOMPOSITION (LLM) │ │ Break topic into search terms per data source │ └──────────────────────┬──────────────────────────────────┘ │ ┌────────────┼────────────┬────────────┐ ▼ ▼ ▼ ▼ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │Prediction│ │ Twitter │ │ News │ │ Stock │ │ Markets │ │Sentiment │ │ Velocity │ │ Data │ │ (odds) │ │ (volume) │ │ (tavily) │ │(financial)│ └────┬─────┘ └────┬─────┘ └────┬─────┘ └────┬─────┘ │ │ │ │ └────────────┴─────┬──────┴─────────────┘ ▼ ┌─────────────────────────────────────────────────────────┐ │ SIGNAL SYNTHESIS (LLM) │ │ Weigh signals, detect agreement/conflict, │ │ produce confidence score (0-100) + forecast │ └─────────────────────────────────────────────────────────┘ ``` ## Workflow Follow these steps in order. Each step calls a specific AIsa API endpoint. ### Step 1: Decompose the Query Use the AIsa LLM gateway to break the user's query into source-specific search terms. ```bash theme={null} curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-4.1-mini", "messages": [ { "role": "system", "content": "You decompose a user query into search terms for 4 data sources. Respond ONLY with JSON: {\"prediction_market_query\": \"...\", \"twitter_query\": \"...\", \"news_query\": \"...\", \"stock_symbols\": [\"...\"], \"topic_summary\": \"...\"}. stock_symbols must be real tickers (AAPL, NVDA, TLT) — never institution abbreviations like FED/SEC/FDA." }, {"role": "user", "content": ""} ], "temperature": 0.2 }' ``` ### Step 2: Gather Prediction Market Signals Use mounted prediction-market discovery endpoints to collect market context. AIsa currently exposes Polymarket market/event discovery and Kalshi markets/trades; use venue-native APIs or trading systems for current price and orderbook checks when the forecast requires executable odds. ```bash theme={null} # Polymarket markets curl "https://api.aisa.one/apis/v1/polymarket/markets?limit=5" \ -H "Authorization: Bearer $AISA_API_KEY" # Polymarket events curl "https://api.aisa.one/apis/v1/polymarket/events?limit=5" \ -H "Authorization: Bearer $AISA_API_KEY" ``` For Kalshi: ```bash theme={null} curl "https://api.aisa.one/apis/v1/kalshi/markets?limit=5&status=open" \ -H "Authorization: Bearer $AISA_API_KEY" curl "https://api.aisa.one/apis/v1/kalshi/trades?limit=5&is_block_trade=false" \ -H "Authorization: Bearer $AISA_API_KEY" ``` Extract: market titles, status, volume/liquidity fields, close times, and recent trade context. ### Step 3: Gather Twitter/X Social Sentiment Search Twitter for recent discussion volume and sentiment signals. The tweet search endpoint is `/twitter/tweet/advanced_search` with params `query` and `queryType` (`Latest` or `Top`). ```bash theme={null} curl "https://api.aisa.one/apis/v1/twitter/tweet/advanced_search?query=&queryType=Latest" \ -H "Authorization: Bearer $AISA_API_KEY" ``` Extract: tweet count, engagement metrics (likes, retweets, replies), notable accounts posting about the topic, and overall sentiment tone. ### Step 4: Gather News Signals Use AIsa's Tavily relay to search recent news articles about the topic. ```bash theme={null} curl -X POST "https://api.aisa.one/apis/v1/tavily/search" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "query": "", "search_depth": "advanced", "max_results": 10, "topic": "news", "days": 7 }' ``` Extract: article count, source diversity, headline sentiment, publication velocity (are articles accelerating or decelerating?). ### Step 5: Gather Stock/Market Signals (if applicable) If the topic relates to a publicly traded company, sector, or financial instrument, query AIsa's MarketPulse `/financial/` endpoints. Pull three signals per ticker: ```bash theme={null} # Historical prices (interval is required: day, week, month, etc.) curl "https://api.aisa.one/apis/v1/financial/prices?ticker=&interval=day" \ -H "Authorization: Bearer $AISA_API_KEY" # Real-time financial metrics snapshot curl "https://api.aisa.one/apis/v1/financial/financial-metrics/snapshot?ticker=" \ -H "Authorization: Bearer $AISA_API_KEY" # Company news curl "https://api.aisa.one/apis/v1/financial/news?ticker=" \ -H "Authorization: Bearer $AISA_API_KEY" ``` Extract: recent price trend (1d, 5d, 30d), valuation/profitability metrics, and headline sentiment. For deeper signals, add `/financial/analyst-estimates`, `/financial/insider-trades`, or the macro `/financial/macro/interest-rates/snapshot`. Use only real ticker symbols (AAPL, NVDA, TLT) — never institution abbreviations like FED/SEC/FDA. If no stock symbols are relevant, skip this step and note "N/A — non-financial topic". ### Step 6: Synthesize Forecast Pass all gathered signals to the AIsa LLM gateway for synthesis. ```bash theme={null} curl -X POST "https://api.aisa.one/v1/chat/completions" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-4.1-mini", "messages": [ { "role": "system", "content": "You are a trend analyst. Given structured signals from prediction markets, Twitter, news, and stock data, produce a forecast. Output JSON: {\"trend_direction\": \"bullish|bearish|neutral|mixed\", \"confidence_score\": 0-100, \"time_horizon\": \"...\", \"headline\": \"...\", \"analysis\": \"...\", \"signal_agreement\": \"high|medium|low\", \"key_signals\": [...], \"risks\": [...], \"data_gaps\": [...]}" }, { "role": "user", "content": "TOPIC: \n\nPREDICTION MARKETS:\n\n\nTWITTER SENTIMENT:\n\n\nNEWS VELOCITY:\n\n\nMARKET DATA:\n" } ], "temperature": 0.3 }' ``` ### Step 7: Format and Deliver Present the forecast to the user in this format: ``` 📈 TREND FORECAST: Direction: Confidence: /100 Signal Agreement: Time Horizon: ANALYSIS: KEY SIGNALS: - - - RISKS & CAVEATS: - - DATA GAPS: - ``` ## Rules * ALWAYS call at least 3 of the 4 data sources before synthesizing. A forecast from fewer than 3 sources must include a prominent "LOW CONFIDENCE — limited data sources" warning. * NEVER present prediction market odds as certainties. Always frame them as "prediction markets currently price X at Y%" not "X will happen". * NEVER provide financial advice. Frame all output as informational analysis, not investment recommendations. Include a disclaimer when stock data is involved. * For stock signals, use only real ticker symbols. Never pass institution abbreviations (FED, SEC, FDA) to the `/financial/` endpoints — they will fail. * If the AISA\_API\_KEY is not set, prompt the user to set it and provide a link to [https://aisa.one](https://aisa.one) to create an account. * If any API call fails, log the error, continue with remaining sources, and note the gap in the final output. ## Automation For recurring forecasts, use the Python script: ```bash theme={null} python3 scripts/trend_forecast.py "Will the Fed cut rates in 2026?" --output json python3 scripts/trend_forecast.py "Tesla outlook" --output markdown --save report.md python3 scripts/trend_forecast.py "Bitcoin outlook" --model gpt-4.1 ``` See `scripts/trend_forecast.py` for the full implementation and `references/api_endpoints.md` for complete AIsa endpoint documentation. ## Get started 1. Sign up at [aisa.one](https://aisa.one) (new accounts start with \$2 free credit). 2. Generate an API key from the console. 3. Set your key and install the skill: ```bash theme={null} export AISA_API_KEY="your-key" npm install -g @aisa-one/cli aisa skills install trend-forecast ``` 4. Start a new agent session so the runtime loads the updated skill instructions. ## Related Market odds, prices, and trade history. X/Twitter search and social intelligence. Equity, filing, and macro market context.