Trend Forecast

View on GitHub -> Trend forecasts from multiple signals. Combine prediction markets, social data, news, and market movement into confidence-scored analysis.

Install

aisa skills install trend-forecast

What can agents do with it?

Forecast briefs

Turn mixed signals into a concise trend forecast.

Prediction market signal

Use Polymarket and Kalshi odds as probability evidence.

Social sentiment

Read X/Twitter movement around topics, entities, and narratives.

Market context

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

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.

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": "<USER_QUERY>"}
    ],
    "temperature": 0.2
  }'

Step 2: Gather Prediction Market Signals

Prices come in two steps: first query /markets to find the market and its ID, then pass that ID to /market-price/ to get the current odds. Prices are decimals 0–1 representing probability (0.65 = 65%).

# 1. Find Polymarket markets (params: search, status, market_slug, limit)
curl "https://api.aisa.one/apis/v1/polymarket/markets?search=<PREDICTION_MARKET_QUERY>&status=open&limit=5" \
  -H "Authorization: Bearer $AISA_API_KEY"

# 2. Price a token (token_id = side_a.id or side_b.id from step 1)
curl "https://api.aisa.one/apis/v1/polymarket/market-price/<TOKEN_ID>" \
  -H "Authorization: Bearer $AISA_API_KEY"

For Kalshi, the flow is the same but keyed on market_ticker:

curl "https://api.aisa.one/apis/v1/kalshi/markets?search=<PREDICTION_MARKET_QUERY>&limit=5" \
  -H "Authorization: Bearer $AISA_API_KEY"

curl "https://api.aisa.one/apis/v1/kalshi/market-price/<MARKET_TICKER>" \
  -H "Authorization: Bearer $AISA_API_KEY"

Extract: market titles, current YES/NO prices (decimal probability), volume, and recency. 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).

curl "https://api.aisa.one/apis/v1/twitter/tweet/advanced_search?query=<TWITTER_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.

curl -X POST "https://api.aisa.one/apis/v1/tavily/search" \
  -H "Authorization: Bearer $AISA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "<NEWS_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:

# Historical prices (interval is required: day, week, month, etc.)
curl "https://api.aisa.one/apis/v1/financial/prices?ticker=<STOCK_SYMBOL>&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=<STOCK_SYMBOL>" \
  -H "Authorization: Bearer $AISA_API_KEY"

# Company news
curl "https://api.aisa.one/apis/v1/financial/news?ticker=<STOCK_SYMBOL>" \
  -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.

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: <TOPIC_SUMMARY>\n\nPREDICTION MARKETS:\n<PM_DATA>\n\nTWITTER SENTIMENT:\n<TWITTER_DATA>\n\nNEWS VELOCITY:\n<NEWS_DATA>\n\nMARKET DATA:\n<STOCK_DATA>"
      }
    ],
    "temperature": 0.3
  }'

Step 7: Format and Deliver

Present the forecast to the user in this format:

📈 TREND FORECAST: <headline>

Direction: <trend_direction>
Confidence: <confidence_score>/100
Signal Agreement: <signal_agreement>
Time Horizon: <time_horizon>

ANALYSIS:
<analysis paragraph>

KEY SIGNALS:
- <signal 1>
- <signal 2>
- <signal 3>

RISKS & CAVEATS:
- <risk 1>
- <risk 2>

DATA GAPS:
- <any sources that returned no data>

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 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:

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

Sign up at aisa.one (new accounts start with $2 free credit).
Generate an API key from the console.

Set your key and install the skill:

export AISA_API_KEY="your-key"
aisa skills install trend-forecast

Start a new agent session so the runtime loads the updated skill instructions.

Prediction Market Data

Market odds, prices, and trade history.

Twitter Autopilot

X/Twitter search and social intelligence.

MarketPulse

Equity, filing, and macro market context.

Documentation Index

​Install

​What can agents do with it?

Forecast briefs

Prediction market signal

Social sentiment

Market context

​Context

​Example Prompts

​Environment

​Architecture

​Workflow

​Step 1: Decompose the Query

​Step 2: Gather Prediction Market Signals

​Step 3: Gather Twitter/X Social Sentiment

​Step 4: Gather News Signals

​Step 5: Gather Stock/Market Signals (if applicable)

​Step 6: Synthesize Forecast

​Step 7: Format and Deliver

​Rules

​Automation

​Get started

​Related