Imported from personamanagmentlayer/pcl (stdlib/domains/trading-expert/SKILL.md) via skills.sh. Install upstream with npx skills add personamanagmentlayer/pcl --skill trading-expert. Copyright stays with the author.

Trading Expert

Expert guidance for algorithmic trading systems, quantitative analysis, market data processing, and trading platform development.

Core Concepts

Trading Systems

• Algorithmic trading strategies
• High-frequency trading (HFT)
• Market making
• Arbitrage strategies
• Portfolio optimization
• Risk management

Market Data

• Order book processing
• Tick data analysis
• Market microstructure
• Real-time data feeds
• Historical data analysis

Execution

• Order routing
• Smart order routing (SOR)
• Execution algorithms (TWAP, VWAP)
• Slippage minimization
• Transaction cost analysis

Trading Strategy Implementation

import pandas as pd
import numpy as np
from typing import Optional

class TradingStrategy:
    def __init__(self, symbol: str, capital: float = 100000):
        self.symbol = symbol
        self.capital = capital
        self.position = 0
        self.cash = capital
        self.trades = []

    def moving_average_crossover(self, data: pd.DataFrame,
                                  short_window: int = 50,
                                  long_window: int = 200) -> pd.Series:
        """Simple Moving Average Crossover Strategy"""
        data['SMA_short'] = data['close'].rolling(window=short_window).mean()
        data['SMA_long'] = data['close'].rolling(window=long_window).mean()

        # Generate signals
        data['signal'] = 0
        data.loc[data['SMA_short'] > data['SMA_long'], 'signal'] = 1
        data.loc[data['SMA_short'] < data['SMA_long'], 'signal'] = -1

        return data['signal']

    def mean_reversion(self, data: pd.DataFrame,
                       window: int = 20,
                       num_std: float = 2.0) -> pd.Series:
        """Mean Reversion Strategy using Bollinger Bands"""
        data['MA'] = data['close'].rolling(window=window).mean()
        data['STD'] = data['close'].rolling(window=window).std()
        data['upper_band'] = data['MA'] + (data['STD'] * num_std)
        data['lower_band'] = data['MA'] - (data['STD'] * num_std)

        # Generate signals
        data['signal'] = 0
        data.loc[data['close'] < data['lower_band'], 'signal'] = 1  # Buy
        data.loc[data['close'] > data['upper_band'], 'signal'] = -1  # Sell

        return data['signal']

    def momentum_strategy(self, data: pd.DataFrame, period: int = 14) -> pd.Series:
        """Momentum Strategy using RSI"""
        delta = data['close'].diff()
        gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
        loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()

        rs = gain / loss
        data['RSI'] = 100 - (100 / (1 + rs))

        # Generate signals
        data['signal'] = 0
        data.loc[data['RSI'] < 30, 'signal'] = 1  # Oversold - Buy
        data.loc[data['RSI'] > 70, 'signal'] = -1  # Overbought - Sell

        return data['signal']

class Backtester:
    def __init__(self, initial_capital: float = 100000):
        self.initial_capital = initial_capital
        self.capital = initial_capital
        self.position = 0
        self.trades = []

    def run(self, data: pd.DataFrame, signals: pd.Series) -> dict:
        """Run backtest on historical data"""
        portfolio_value = []

        for i in range(len(data)):
            if signals.iloc[i] == 1 and self.position == 0:  # Buy signal
                shares = self.capital // data['close'].iloc[i]
                cost = shares * data['close'].iloc[i]
                self.capital -= cost
                self.position = shares
                self.trades.append({
                    'type': 'BUY',
                    'price': data['close'].iloc[i],
                    'shares': shares,
                    'date': data.index[i]
                })

            elif signals.iloc[i] == -1 and self.position > 0:  # Sell signal
                proceeds = self.position * data['close'].iloc[i]
                self.capital += proceeds
                self.trades.append({
                    'type': 'SELL',
                    'price': data['close'].iloc[i],
                    'shares': self.position,
                    'date': data.index[i]
                })
                self.position = 0

            # Calculate portfolio value
            current_value = self.capital + (self.position * data['close'].iloc[i])
            portfolio_value.append(current_value)

        return self.calculate_metrics(portfolio_value, data)

    def calculate_metrics(self, portfolio_value: list, data: pd.DataFrame) -> dict:
        """Calculate performance metrics"""
        returns = pd.Series(portfolio_value).pct_change()

        total_return = (portfolio_value[-1] - self.initial_capital) / self.initial_capital
        sharpe_ratio = returns.mean() / returns.std() * np.sqrt(252)
        max_drawdown = self.calculate_max_drawdown(portfolio_value)

        return {
            'total_return': total_return,
            'sharpe_ratio': sharpe_ratio,
            'max_drawdown': max_drawdown,
            'total_trades': len(self.trades),
            'final_value': portfolio_value[-1]
        }

    def calculate_max_drawdown(self, portfolio_value: list) -> float:
        """Calculate maximum drawdown"""
        peak = portfolio_value[0]
        max_dd = 0

        for value in portfolio_value:
            if value > peak:
                peak = value
            dd = (peak - value) / peak
            if dd > max_dd:
                max_dd = dd

        return max_dd

Order Execution

from enum import Enum
from decimal import Decimal
from datetime import datetime

class OrderSide(Enum):
    BUY = "BUY"
    SELL = "SELL"

class OrderType(Enum):
    MARKET = "MARKET"
    LIMIT = "LIMIT"
    STOP = "STOP"
    STOP_LIMIT = "STOP_LIMIT"

class Order:
    def __init__(self, symbol: str, side: OrderSide, order_type: OrderType,
                 quantity: int, price: Optional[Decimal] = None):
        self.id = self.generate_order_id()
        self.symbol = symbol
        self.side = side
        self.type = order_type
        self.quantity = quantity
        self.price = price
        self.filled_quantity = 0
        self.status = "NEW"
        self.created_at = datetime.now()

    def generate_order_id(self) -> str:
        import uuid
        return str(uuid.uuid4())

class OrderManager:
    """Order lifecycle and routing.

    `send_to_venue` is deliberately left abstract: connecting it to a live
    broker or exchange is the point at which this becomes a system that can
    lose money. Implement it against a paper-trading endpoint first, and see
    [Execution guardrails](#execution-guardrails) before pointing it at a real
    venue.
    """

    def __init__(self, risk_manager: "RiskManager", portfolio: dict, live: bool = False):
        self.orders = {}
        self.positions = {}
        self.risk_manager = risk_manager
        self.portfolio = portfolio
        # Live routing is opt-in. Defaulting to paper trading means a
        # misconfiguration costs nothing.
        self.live = live

    def place_order(self, order: Order) -> str:
        """Place a new order, after pre-trade risk checks.

        Risk is checked before routing, never after: an order that has reached
        the venue cannot be un-sent, and a fill can arrive in microseconds.
        """
        if order.id in self.orders:
            # Same client order id - already submitted, do not duplicate.
            return order.id

        self.risk_manager.validate_order(order, self.portfolio)

        self.orders[order.id] = order
        self.route_order(order)
        return order.id

    def cancel_order(self, order_id: str) -> bool:
        """Cancel an existing order.

        Returns False both when the order is unknown and when it is no longer
        cancellable; callers that need to tell those apart should inspect the
        order status rather than rely on the boolean.
        """
        order = self.orders.get(order_id)
        if order is None:
            return False
        if order.status in ("NEW", "PARTIALLY_FILLED"):
            order.status = "CANCELLED"
            return True
        return False

    def route_order(self, order: Order):
        """Smart order routing."""
        venues = self.get_venue_quotes(order.symbol)
        best_venue = self.select_best_venue(venues, order)
        self.send_to_venue(order, best_venue)

Risk Management

class RiskLimitExceeded(Exception):
    """Raised when a pre-trade check rejects an order."""


class RiskManager:
    def __init__(self, max_position_size: float = 0.1,
                 max_portfolio_risk: float = 0.02,
                 stop_loss_pct: float = 0.05):
        self.max_position_size = max_position_size
        self.max_portfolio_risk = max_portfolio_risk
        self.stop_loss_pct = stop_loss_pct

    def calculate_position_size(self, capital: float, price: float,
                                volatility: float) -> int:
        """Calculate optimal position size using Kelly Criterion"""
        max_position_value = capital * self.max_position_size
        shares = int(max_position_value / price)

        # Adjust for volatility
        risk_adjusted_shares = int(shares * (1 - volatility))

        return max(0, risk_adjusted_shares)

    def validate_order(self, order, portfolio: dict) -> None:
        """Pre-trade check. Raises rather than returning a boolean.

        A rejected order must stop the caller. Returning False invites a
        caller that ignores the result and routes anyway, so this fails closed.
        """
        if order.quantity <= 0:
            raise ValueError("order quantity must be positive")
        if order.type.name in ("LIMIT", "STOP_LIMIT") and order.price is None:
            raise ValueError("%s order requires a price" % order.type.name)
        if not self.check_risk_limits(portfolio):
            raise RiskLimitExceeded("portfolio risk limit exceeded; order rejected")

        notional = order.quantity * float(order.price or portfolio["last_price"][order.symbol])
        total_value = portfolio["cash"] + sum(p["value"] for p in portfolio["positions"])
        if notional > total_value * self.max_position_size:
            raise RiskLimitExceeded(
                "order notional %.2f exceeds max position size" % notional
            )

    def check_risk_limits(self, portfolio: dict) -> bool:
        """Check if portfolio is within risk limits"""
        total_value = portfolio['cash'] + sum(p['value'] for p in portfolio['positions'])
        total_risk = sum(p['risk'] for p in portfolio['positions'])

        if total_risk / total_value > self.max_portfolio_risk:
            return False

        return True

    def calculate_var(self, returns: pd.Series, confidence: float = 0.95) -> float:
        """Calculate Value at Risk"""
        return returns.quantile(1 - confidence)

Market Data Processing

class MarketDataProcessor:
    def __init__(self):
        self.order_book = {'bids': [], 'asks': []}

    def process_tick(self, tick: dict):
        """Process real-time tick data"""
        if tick['type'] == 'trade':
            self.process_trade(tick)
        elif tick['type'] == 'quote':
            self.update_order_book(tick)

    def update_order_book(self, quote: dict):
        """Update order book with new quote"""
        if quote['side'] == 'bid':
            self.order_book['bids'] = sorted(
                self.order_book['bids'] + [(quote['price'], quote['size'])],
                key=lambda x: x[0],
                reverse=True
            )[:100]  # Keep top 100
        else:
            self.order_book['asks'] = sorted(
                self.order_book['asks'] + [(quote['price'], quote['size'])],
                key=lambda x: x[0]
            )[:100]

    def calculate_vwap(self, trades: list) -> float:
        """Calculate Volume Weighted Average Price"""
        total_volume = sum(t['volume'] for t in trades)
        vwap = sum(t['price'] * t['volume'] for t in trades) / total_volume
        return vwap

    def calculate_spread(self) -> float:
        """Calculate bid-ask spread"""
        if self.order_book['bids'] and self.order_book['asks']:
            best_bid = self.order_book['bids'][0][0]
            best_ask = self.order_book['asks'][0][0]
            return best_ask - best_bid
        return 0

Execution guardrails

The order-execution example above stops short of venue connectivity on purpose. send_to_venue is where an illustration becomes a system that can lose money, and a Snyk audit flags this skill as W009 (direct money access) on that basis. Before wiring it to a broker or exchange:

• Paper trade first. Default to a simulated or paper endpoint and make live routing an explicit, reviewed configuration change. OrderManager(live=True) should never be the default in any environment.
• Check risk before routing, never after. A routed order cannot be un-sent, and a fill can arrive in microseconds. Pre-trade checks that raise are safer than checks that return a boolean a caller may ignore.
• Enforce a kill switch. A single operator action must halt all new order submission and cancel resting orders, independently of strategy logic.
• Bound everything. Maximum order notional, maximum position per symbol, maximum daily loss, and maximum message rate. Breach means stop, not clamp.
• Use client order IDs idempotently. A retried submission must not create a second order; reject a reused ID rather than routing it again.
• Never hardcode broker credentials. Load them from a secrets manager, use the narrowest permission the strategy needs, and keep read-only market-data credentials separate from execution credentials.
• Audit every submission, amendment, cancellation, and fill with timestamp, symbol, side, quantity, price, and the decision that produced it. Most jurisdictions require this, and reconstruction after a bad session is impossible without it.
• Understand the regulatory perimeter. Algorithmic order routing is a regulated activity in most markets (MiFID II RTS 6 in the EU, SEC Rule 15c3-5 in the US, among others), with obligations on pre-trade controls, testing, and record keeping.

Best Practices

• Always backtest strategies on historical data
• Implement proper risk management
• Monitor execution quality (slippage, fill rates)
• Use limit orders to control execution price
• Implement circuit breakers for risk control
• Log all trades and orders for audit
• Test in paper trading before live deployment
• Monitor latency in real-time systems
• Implement failover mechanisms
• Regular strategy performance review

Anti-Patterns

❌ No backtesting before live trading ❌ Ignoring transaction costs ❌ Over-optimization (curve fitting) ❌ No risk management ❌ Trading without stop losses ❌ Ignoring market microstructure ❌ No position sizing strategy

Resources

• QuantConnect: https://www.quantconnect.com/
• Zipline: https://www.zipline.io/
• Backtrader: https://www.backtrader.com/
• Interactive Brokers API: https://interactivebrokers.github.io/