Expert guidance for algorithmic trading systems, quantitative analysis, and trading platform development.
Works with
Covers core trading domains: algorithmic strategies (moving average crossover, mean reversion, momentum), order execution (market, limit, stop orders), and smart order routing
Includes backtesting framework with performance metrics (Sharpe ratio, max drawdown, total return) and trade logging for strategy validation
Provides risk management tools: position sizing via Kelly Criteri
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontrading-expertExecute the skills CLI command in your project's root directory to begin installation:
Fetches trading-expert from personamanagmentlayer/pcl and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate trading-expert. Access via /trading-expert in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Expert guidance for algorithmic trading systems, quantitative analysis, market data processing, and trading platform development.
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]
})
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
erichowens/some_claude_skills
huynguyen03dev/xauusd-trading
sickn33/antigravity-awesome-skills
erichowens/some_claude_skills
mattpocock/skills
parcadei/continuous-claude-v3
Solid pick for teams standardizing on skills: trading-expert is focused, and the summary matches what you get after install.
We added trading-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
trading-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.
trading-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for trading-expert matched our evaluation — installs cleanly and behaves as described in the markdown.
trading-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
trading-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in trading-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
trading-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.
trading-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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