Trader - Master Trading Skill

Trader - Master Trading Skill

Routes when user mentions trades, setups, positions, charts, patterns, or brokerage operations.

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Sub-Skill Routing

Trigger	Route To	Purpose
portfolio, quote, schwab, account, order, buy, sell	Schwab/SKILL.md	Brokerage operations
market data, historical, aggregates, indicators, sma, rsi, macd	Massive/SKILL.md	Market data API
setup, pattern, breakout, chart, evaluate	This file	Technical analysis
journal, log trade	This file + Data/JOURNAL.md	Trade logging
watchlist	This file + Data/WATCHLIST.md	Ticker monitoring
positions, holdings	This file + Data/POSITIONS.md	Position management

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Directory Structure

~/.claude/skills/Trader/
├── SKILL.md              # This file - master routing + methodology
├── Schwab/               # Brokerage integration
│   ├── SKILL.md
│   ├── schwab_client.py
│   └── auth.py
├── Massive/              # Market data API
│   ├── SKILL.md
│   ├── massive_client.py # REST API wrapper
│   └── flat_files.py     # S3 bulk data downloader
├── Data/
│   ├── WATCHLIST.md      # Tickers being monitored
│   ├── POSITIONS.md      # Open positions
│   └── JOURNAL.md        # Trade log
├── PLAYBOOK.md           # Pattern definitions
└── Research/             # Per-ticker notes
    └── [TICKER].md

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Trading Philosophy

Principle	Description
Style	Swing trading (days to weeks, sometimes months)
Direction	Long only - NO shorts
Markets	Equities and Crypto
Edge	Price action + pattern breakouts from extended consolidation

What I DON'T do:

• Day trading / Scalping
• Short selling
• Options trading
• Leverage/margin

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Required Indicators

1. Ichimoku Cloud

• Trend: Price above/below cloud
• S/R: Cloud edges (Senkou Span A/B)
• Momentum: Tenkan/Kijun cross
• Future: Kumo twist

2. Pivot Points

• Monthly and weekly pivots
• Target setting and stop placement

3. Moving Averages (20/50/200)

MA	Purpose
20	Near-term trend, entry timing
50	Intermediate trend
200	Major trend direction

Ideal: Price > 20 > 50 > 200

4. Williams Fractals

• Swing highs/lows
• Breakout confirmation
• Stop placement

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Preferred Patterns

Pattern	Type	Signal
Inverse H&S	Reversal	Neckline break
Cup & Handle	Continuation	Handle breakout
Falling Wedge	Both	Upper line break
Adam & Eve	Reversal	Neckline break
Ascending Triangle	Continuation	Resistance break
Bull Flag	Continuation	Flag break

See PLAYBOOK.md for detailed definitions.

Consolidation Requirements

• Minimum 4+ weeks
• Decreasing volume
• Tightening range
• Clean, obvious structure

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Setup Scoring (1-10)

Criterion	Weight
Pattern Clarity	2x
Consolidation Length	1.5x
Indicator Alignment	1.5x
Risk/Reward	2x
Volume Profile	1x
Trend Context	1x

Interpretation:

• 8-10: A+ setup, full size
• 6-7: Solid, standard size
• 4-5: Marginal, reduce or pass
• <4: No trade

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Position Sizing

Size = (Account Risk %) / (Stop Distance %)

Risk Rules:

• Max per trade: 1-2%
• Max position: 10% of account
• Max correlated: 20%

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Workflow Commands

Command	Action
"Evaluate [TICKER] setup"	Score against criteria
"Review positions"	Check vs stops/targets
"Plan trade for [TICKER]"	Entry/stop/target with R:R
"Log trade [TICKER]"	Journal entry
"Research [TICKER]"	Fundamentals + catalysts
"Show watchlist"	Current monitoring list

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Integration: Schwab

Full brokerage access via Schwab/ sub-skill:

from schwab_client import SchwabClient
client = SchwabClient()

# Account operations
client.get_positions("brokerage")
client.get_account("brokerage")
client.get_portfolio_summary()

# Market data
client.get_quote("NVDA")
client.get_quotes(["AAPL", "MSFT", "GOOGL"])

# Orders (dry_run=True by default)
client.place_limit_order(symbol, qty, price, dry_run=True)

See Schwab/SKILL.md for full API documentation.

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Integration: Massive (Market Data)

Historical data, real-time quotes, and technical indicators via Massive/ sub-skill:

from massive_client import MassiveClient
client = MassiveClient()

# OHLC Aggregates
bars = client.get_aggregates("AAPL", "day", "2024-01-01", "2024-12-31")
daily = client.get_daily_bars("NVDA", days=252)

# Real-time Data
snapshot = client.get_snapshot("TSLA")
quote = client.get_quote("MSFT")

# Technical Indicators
sma_20 = client.get_sma("AAPL", window=20)
rsi = client.get_rsi("NVDA", window=14)
macd = client.get_macd("TSLA")

# Trading Helper
alignment = client.check_ma_alignment("AAPL")  # Returns bullish_stack status

# Crypto
btc_bars = client.get_crypto_aggregates("BTC", "USD", "day")

Bulk Downloads (Flat Files):

from flat_files import MassiveFlatFiles
downloader = MassiveFlatFiles()

# Download daily aggregates
downloader.download_day_aggs("2024-01-01", "2024-01-31")

# Load into pandas
df = downloader.load_day_aggs_csv("2024-01-15", ticker="AAPL")

See Massive/SKILL.md for full API documentation.

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Response Format

## [TICKER] Setup Analysis

**Pattern**: [Name]
**Timeframe**: [Daily/Weekly]
**Consolidation**: [Duration]

### Indicators
- Ichimoku: [Status]
- MAs: [Alignment]
- Pivots: [Key levels]
- Fractals: [Swing points]

### Levels
- Entry: $XX.XX
- Stop: $XX.XX (reason)
- T1: $XX.XX (reason)
- T2: $XX.XX (reason)
- R:R: X.X:1

### Score: X/10

### Recommendation
[PASS / WATCHLIST / ENTER]