trading212 Skill

Analyzes Trading212 portfolio, generates daily summaries with P&L and top gainers/losers, makes trade proposals based on configurable rules, and can place or...

Connects to the Trading212 API to provide portfolio analysis, trade proposals, and order execution.

Important: By default all operations run against the demo (paper-trading) environment. Set TRADING212_DEMO=false only when you are absolutely sure the user wants to trade with real money.

Prerequisites

Install dependencies once from the skill's script directory:

pip install -r {baseDir}/requirements.txt

Available Modes

1. summary -- Daily portfolio overview

python3 {baseDir}/scripts/trading212_skill.py --mode summary

Returns structured JSON with:

• Total portfolio value, cash, daily change (EUR + %)
• Per-position details (quantity, avg price, current price, unrealised P&L)
• Top gainers and top losers
• Notable events (orders filled today, dividends received)
• Multi-period performance (1 week, 1 month, 3 months, 1 year)

Use this when the user asks: "How did my portfolio do today?", "Give me a summary", "What happened in my portfolio?"

Present the JSON output as a readable English summary. Highlight the daily change prominently, list top gainers and losers, and mention notable events.

2. propose -- Trade proposals

python3 {baseDir}/scripts/trading212_skill.py --mode propose
python3 {baseDir}/scripts/trading212_skill.py --mode propose --risk low
python3 {baseDir}/scripts/trading212_skill.py --mode propose --risk high

Returns a list of suggested actions (buy, sell, reduce, hold) with quantities and reasons. Rules are configured in config/rules.yaml.

Active rules:

• Reduce on drop: Propose reducing positions that dropped significantly today with large weight
• Take profit: Propose selling small positions with high unrealised gain
• DCA buy: Propose buying tickers on the DCA list when enough cash is available
• Stop-loss: Propose selling when price drops below stop-loss threshold vs average purchase price
• Max exposure: Propose reducing when a single position exceeds maximum portfolio weight
• Cost averaging: Propose buying more when price is significantly below average purchase price
• Cash reserve: Warn when cash falls below minimum percentage of portfolio

Use this when the user asks: "What should I do?", "Any trade suggestions?", "Should I buy or sell anything?"

Present proposals clearly. Always ask the user for confirmation before executing any proposed trade. Never execute trades automatically.

3. execute_trade -- Place an order

python3 {baseDir}/scripts/trading212_skill.py --mode execute_trade --params '{"symbol":"AAPL_US_EQ","side":"buy","quantity":5,"order_type":"market"}'

Parameters (JSON):

• symbol (required): Trading212 ticker, e.g. "AAPL_US_EQ"
• side (required): "buy" or "sell"
• quantity (required): positive number of shares
• order_type: "market" (default) or "limit"
• limit_price: required when order_type is "limit"

Pre-trade validation is performed automatically:

• Buy orders: checks if enough cash is available
• Sell orders: checks if enough shares are held

CRITICAL SAFETY RULES:

1. NEVER execute a trade without explicit user confirmation.
2. Always show the user exactly what will be executed (symbol, side, quantity, order type) and ask "Shall I place this order?" before running.
3. If TRADING212_DEMO=true (the default), remind the user this is a paper-trade.
4. If TRADING212_DEMO=false, warn the user clearly that this is a REAL order with real money.

4. dividends -- Dividend overview

python3 {baseDir}/scripts/trading212_skill.py --mode dividends

Returns structured JSON with:

• Total dividends received (all time and last 12 months)
• Per-ticker breakdown with totals, last payment date, and estimated annual yield
• Dividend calendar (most recent payment per ticker)

Use this when the user asks: "How much dividend did I receive?", "What are my dividends?", "When was my last dividend?"

5. history -- Order history

python3 {baseDir}/scripts/trading212_skill.py --mode history
python3 {baseDir}/scripts/trading212_skill.py --mode history --params '{"ticker":"AAPL_US_EQ","days":30}'

Returns structured JSON with:

• Total number of historical orders
• Realized P&L per ticker and overall
• Full order list with dates, prices, and quantities

Optional parameters (JSON):

• ticker: Filter by specific ticker
• days: Limit to orders from the last N days

Use this when the user asks: "Show my order history", "How much profit did I realize?", "What did I trade last month?"

6. watchlist -- Price monitoring

python3 {baseDir}/scripts/trading212_skill.py --mode watchlist

Reads config/watchlist.yaml and checks price alerts for each ticker. Returns:

• List of watchlist items with current prices (if held)
• Triggered alerts (price above or below configured thresholds)

Configure the watchlist in config/watchlist.yaml:

watchlist:
  - ticker: "NVDA_US_EQ"
    alert_below: 100.0
    alert_above: 150.0

Use this when the user asks: "Check my watchlist", "Any price alerts?", "What are my watched stocks doing?"

7. allocation -- Portfolio allocation analysis

python3 {baseDir}/scripts/trading212_skill.py --mode allocation
python3 {baseDir}/scripts/trading212_skill.py --mode allocation --rebalance

Returns structured JSON with:

• Current weight per position vs target allocation
• Deviation from target per position
• Missing target tickers (in target but not held)
• Cash allocation vs target

With --rebalance flag, also generates buy/sell proposals to move toward target allocation.

Configure target allocation in config/allocation.yaml:

target_allocation:
  "VWCE.UK": 40.0
  "IWDA.UK": 30.0
  _cash: 5.0

Use this when the user asks: "How is my portfolio allocated?", "Am I balanced?", "What should I rebalance?"

Output Format

All modes return structured JSON to stdout. Parse it and present a human-readable English summary to the user.

Additional Resources

For full output schemas and API details, see reference.md.