n8n AI-Powered Stock Market Summary Bot: Hourly RSI & MACD Alerts to Slack

Input: stock ticker list; Process: scheduled trigger → Alpaca data fetch → Python technical indicator calculation → OpenAI summary generation; Output: categorized Buy/Hold/Sell Slack message

Who

n8n users who want automated US stock monitoring with Slack alerts

You get

Hourly automated data fetch, RSI/MACD calculation, signal generation, and Slack delivery during US market hours

You need

n8n · slack

Steps

1. Configure Schedule Trigger and Market Status Check

   Add a Schedule Trigger node with Cron expression `0 30 6-14 * * 1-5` (hourly Mon–Fri 6:30 AM–2:30 PM PT). Then add an HTTP Request node calling `https://paper-api.alpaca.markets/v2/clock` to check if the market is open, followed by an If node evaluating `{{ $json.is_open }}`: if true continue, if false exit gracefully via a NoOp node.

2. Set Up Ticker List

   Add an Edit Fields (Set) node and define the stock symbols to monitor in the JSON output. Default list: AAPL, MSFT, NVDA, TSLA, AMZN, GOOGL, META, JPM, XOM, UNH, GME. Modify as needed.

3. Configure Alpaca Stock Data Fetch

   Add an HTTP Request node with URL `=https://data.alpaca.markets/v2/stocks/bars`, authentication set to httpCustomAuth. Query parameters: symbols from `{{ $json.symbols }}`, timeframe=`1Day`, limit=`1000`, feed=`iex` (avoids SIP permission error), start=100 days ago, end=today.

4. Add Python Technical Indicator Calculation Node

   Add a Code node with language set to Python. This node receives Alpaca bars data, calculates RSI(14) and MACD(12,26,9), and generates Buy/Hold/Sell signals based on thresholds. Outputs include a stocks list and a summary JSON string for GPT.

   Execution prompt · Source text。Interpret Data node (n8n-nodes-base.code), language: python

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   import pandas as pd
   import numpy as np
   import json
   
   bars_by_symbol = items[0]['json']['bars']
   stocks = []
   
   for symbol, bars in bars_by_symbol.items():
       closes = [bar['c'] for bar in bars if 'c' in bar]
       if len(closes) < 30:
           continue
   
       df = pd.DataFrame({'close': closes})
   
       # RSI(14)
       delta = df['close'].diff()
       gain = delta.clip(lower=0)
       loss = -delta.clip(upper=0)
       avg_gain = gain.rolling(14).mean()
       avg_loss = loss.rolling(14).mean()
       rs = avg_gain / avg_loss
       df['rsi'] = 100 - (100 / (1 + rs))
   
       # MACD (12,26,9)
       ema12 = df['close'].ewm(span=12, adjust=False).mean()
       ema26 = df['close'].ewm(span=26, adjust=False).mean()
       df['macd'] = ema12 - ema26
       df['signal'] = df['macd'].ewm(span=9, adjust=False).mean()
   
       latest = df.iloc[-1]
       rsi = latest['rsi']
       macd = latest['macd']
       signal = latest['signal']
   
       status = "Hold"
       if rsi < 30 and macd > signal:
           status = "Buy"
       elif rsi > 70 and macd < signal:
           status = "Sell"
   
       stocks.append({
           "ticker": symbol,
           "rsi": round(float(rsi), 2),
           "macd": round(float(macd), 2),
           "signal": round(float(signal), 2),
           "status": status
       })
   
   return [{
       "json": {
           "summary": json.dumps({
               "stocks": stocks
           }, separators=(',', ':')),
         "stocks": stocks
       }
   }]

5. Configure OpenAI Stock Analysis Assistant

   Add an OpenAI node with resource set to assistant. Enter dynamic content in the text field referencing technical indicator data and timestamp. You must pre-create an Assistant in the OpenAI platform and configure the system prompt to generate Slack-friendly Markdown with stocks grouped into Buy Watchlist / Neutral Hold / Caution Sell categories.

   Execution prompt · Source text。Stock Analysis Assistant node text field (OpenAI Assistant message input)

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   =Here is the technical indicator data as JSON:
   
   {{ $json.summary }}
   
   Pulled as of {{ $now }}

   Execution prompt · Source text。OpenAI Assistant system/instruction prompt (configure in OpenAI platform when setting up the Assistant; Sticky Note10 documents intended behavior)

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   You are a financial assistant writing a quick, readable market update for a general audience. Your job is to help people understand how well-known stocks are behaving — even if they aren’t professional traders.
   
   You’ll be given:
   
   A list of stocks with technical indicators (e.g., momentum data)
   A timestamp (in RFC 3339 or ISO format) for when the data was pulled.
   📌 Your task:
   Group the stocks into three categories:
   🟢 Buy Watchlist – Stocks showing signs of recovery or upward momentum
   ⚪ Neutral Hold – Stocks with steady or unclear direction
   🔴 Caution / Sell – Stocks that appear overbought or may pull back
   For each stock:
   Write a short, plain-language insight about what’s happening
   Use familiar terms like “gaining steam,” “cooling off,” or “showing hesitation”
   Avoid technical jargon like RSI or MACD unless context makes it helpful
   Add a helpful tip or comment for each stock (e.g.,
   “This pattern often signals hesitation” or
   “This dip might attract bargain hunters”)
   Finish with a summary line using the timestamp like this:
   Summary as of October 2, 2025 – Most stocks were stable with one or two worth watching.
   📦 Respond in Slack Markdown Only:
   Example Format:
   
   *📊 Market Summary (as of October 2, 2025)*
   
   🟢 *Buy Watchlist*  
   • TSLA – Recovering after a dip; gaining steam. This type of rebound often attracts early buyers.
   
   ⚪ *Neutral Hold*  
   • AAPL – Holding steady. This often means the market is waiting on new developments.  
   • GOOGL – Moving sideways. A sign of consolidation before potential breakout.  
   • MSFT – Little movement. Could be digesting prior gains.  
   • NVDA – Slight back-and-forth. May indicate indecision in the market.
   
   🔴 *Caution / Sell*  
   • None at this time.
   
   _Summary as of October 2, 2025: Most stocks appear steady. TSLA could be one to watch if momentum holds._
   🚫 Do NOT:
   Return raw JSON
   Use code blocks unless formatting Slack markdown (no YAML or tags)
   Use technical finance language unless simplified

6. Configure Slack Message Delivery

   Add a Slack node, select user or channel for the select parameter, and set the text field to `{{ $json.output }}` to send the OpenAI-generated summary. Configure Slack API credentials in advance and ensure the bot has permission to post to the target channel or user.

7. Save and Activate Workflow

   Confirm the workflow timezone is set to America/Los_Angeles, verify all credentials are properly configured, then save and activate the workflow. Run a manual test first to validate Alpaca data retrieval and OpenAI output formatting.

You are a stock analysis workflow setup assistant. The user will provide access to an n8n instance, Alpaca API key, OpenAI API key, and Slack bot token. Help them complete the setup in this order: 1) Configure schedule trigger and market status check; 2) Set up ticker list; 3) Configure Alpaca data fetch node; 4) Add Python technical indicator calculation; 5) Configure OpenAI Assistant node (pre-create Assistant in OpenAI platform and paste system prompt); 6) Configure Slack send node; 7) Test and activate. Note: OpenAI Assistant ID and Slack user/channel ID must be provided by the user.