Trader Hand Skill

Advanced RSI Signals:

Best practices: Never use RSI as a sole signal. Combine with trend direction (moving averages) and volume. In strong trends, RSI can stay overbought/oversold for extended periods.

MACD (Moving Average Convergence Divergence)

MACD Line   = EMA(12) - EMA(26)
Signal Line = EMA(9) of MACD Line
Histogram   = MACD Line - Signal Line

EMA formula: EMA(t) = Price(t) * k + EMA(t-1) * (1 - k)
Where: k = 2 / (N + 1)
  For EMA(12): k = 2/13 = 0.1538
  For EMA(26): k = 2/27 = 0.0741

Worked example:
  EMA(12) = 155.20
  EMA(26) = 152.80
  MACD Line = 155.20 - 152.80 = 2.40
  Previous Signal Line = 1.80
  Signal Line = 2.40 * (2/10) + 1.80 * (8/10) = 0.48 + 1.44 = 1.92
  Histogram = 2.40 - 1.92 = 0.48 (positive = bullish momentum increasing)

Interpretation:

Bollinger Bands

Middle Band = SMA(20)
Upper Band  = SMA(20) + 2 * StdDev(20)
Lower Band  = SMA(20) - 2 * StdDev(20)
Bandwidth   = (Upper - Lower) / Middle
%B          = (Price - Lower) / (Upper - Lower)

Worked example:
  SMA(20) = 150.00
  StdDev(20) = 3.50
  Upper = 150.00 + 2 * 3.50 = 157.00
  Lower = 150.00 - 2 * 3.50 = 143.00
  Bandwidth = (157.00 - 143.00) / 150.00 = 0.0933 (9.33%)
  Current price = 155.00
  %B = (155.00 - 143.00) / (157.00 - 143.00) = 12/14 = 0.857
  Interpretation: Price is 85.7% of the way from lower to upper band — near upper band

Key Bollinger Band Signals:

VWAP (Volume Weighted Average Price)

VWAP = Cumulative(Typical Price * Volume) / Cumulative(Volume)
Typical Price = (High + Low + Close) / 3

Worked example (first 3 bars of the day):
  Bar 1: TP = (101+99+100)/3 = 100.00, Vol = 10,000 -> cumTP*V = 1,000,000
  Bar 2: TP = (102+100+101)/3 = 101.00, Vol = 15,000 -> cumTP*V = 2,515,000
  Bar 3: TP = (103+101+102)/3 = 102.00, Vol = 8,000  -> cumTP*V = 3,331,000
  Cumulative Volume = 33,000
  VWAP = 3,331,000 / 33,000 = 100.94

Usage:

• Institutional benchmark: If price > VWAP, buyers dominate; price < VWAP, sellers dominate
• Intraday S/R: VWAP acts as dynamic support in uptrends, resistance in downtrends
• Entry filter: Buy only when price pulls back to VWAP (not chasing extended moves)
• Standard deviations: VWAP +1/-1 and +2/-2 StdDev bands serve as profit targets
• Resets daily: Do NOT carry VWAP across sessions — it is an intraday metric

Moving Averages

SMA(N) = (Close_1 + Close_2 + ... + Close_N) / N
EMA(N) = Close * (2/(N+1)) + PrevEMA * (1 - 2/(N+1))

Key Moving Averages:
  EMA(9)   — very short-term trend (scalping, day trading)
  EMA(20)  — short-term trend
  EMA(50)  — medium-term trend
  SMA(100) — intermediate trend
  SMA(200) — long-term trend (institutional benchmark)

Critical Cross Signals:

Moving Average Ribbon (20/50/100/200 MAs all fanning out): Indicates a very strong trend. When all are stacked in order (20 > 50 > 100 > 200 for uptrend), the trend is highly reliable.

ATR (Average True Range)

True Range = max(High - Low, |High - PrevClose|, |Low - PrevClose|)
ATR(14) = Simple or Wilder Moving Average of True Range over 14 periods

Worked example:
  Today: High = 105, Low = 101, PrevClose = 102
  TR = max(105-101, |105-102|, |101-102|) = max(4, 3, 1) = 4
  If ATR(14) was 3.50 yesterday:
  ATR(14) = (3.50 * 13 + 4) / 14 = (45.50 + 4) / 14 = 3.536

Practical Applications:

Volume Analysis

OBV (On-Balance Volume):
  If Close > PrevClose: OBV = PrevOBV + Volume
  If Close < PrevClose: OBV = PrevOBV - Volume
  If Close = PrevClose: OBV = PrevOBV

Volume Rate of Change: VROC = (Volume - Volume_N_ago) / Volume_N_ago * 100

Volume Confirmation Rules:

Support & Resistance

Fibonacci Retracement Levels:

After a move from Low (L) to High (H):
  23.6% level = H - (H - L) * 0.236
  38.2% level = H - (H - L) * 0.382
  50.0% level = H - (H - L) * 0.500
  61.8% level = H - (H - L) * 0.618  (Golden Ratio — strongest level)
  78.6% level = H - (H - L) * 0.786

Worked example (move from $80 to $120):
  Range = $40
  23.6% = 120 - 40 * 0.236 = 120 - 9.44  = $110.56
  38.2% = 120 - 40 * 0.382 = 120 - 15.28 = $104.72
  50.0% = 120 - 40 * 0.500 = 120 - 20.00 = $100.00
  61.8% = 120 - 40 * 0.618 = 120 - 24.72 = $95.28  (most likely bounce)
  78.6% = 120 - 40 * 0.786 = 120 - 31.44 = $88.56

Pivot Points (Standard):

PP = (High + Low + Close) / 3
S1 = 2 * PP - High
S2 = PP - (High - Low)
R1 = 2 * PP - Low
R2 = PP + (High - Low)

Worked example (prev day: High=155, Low=148, Close=152):
  PP = (155 + 148 + 152) / 3 = 151.67
  S1 = 2 * 151.67 - 155 = 148.33
  S2 = 151.67 - (155 - 148) = 144.67
  R1 = 2 * 151.67 - 148 = 155.33
  R2 = 151.67 + (155 - 148) = 158.67

2. Candlestick Patterns

Single-Candle Patterns

Multi-Candle Patterns

3. Risk Management Formulas

Position Sizing (Fixed Fractional)

Position Size (shares) = Account Risk Amount / (Entry Price - Stop Loss Price)
Account Risk Amount    = Portfolio Value * Risk Per Trade %

RULE: Never risk more than 1-2% of portfolio on a single trade.

Worked example:
  Portfolio Value = $10,000
  Risk Per Trade  = 2% ($200)
  Entry Price     = $100.00
  Stop Loss       = $95.00 (based on 2x ATR below entry)
  Risk per share  = $100.00 - $95.00 = $5.00
  Position Size   = $200 / $5.00 = 40 shares
  Position Value  = 40 * $100 = $4,000 (40% of portfolio)

  CONCENTRATION CHECK: If position value > 10% of portfolio, reduce size.
  Adjusted: max position = $1,000 / $100 = 10 shares
  Adjusted risk = 10 * $5.00 = $50 (only 0.5% of portfolio — acceptable)

Kelly Criterion (Optimal Bet Size)

Kelly % = W - ((1 - W) / R)
Where:
  W = win rate (decimal)
  R = average win / average loss ratio (reward-to-risk)

Worked example:
  Win rate: 60% (W = 0.60)
  Average win: $300, Average loss: $200
  R = 300 / 200 = 1.5
  Kelly = 0.60 - (0.40 / 1.5) = 0.60 - 0.267 = 0.333 (33.3%)

  Full Kelly is too aggressive for real trading. Use fractions:
  Half-Kelly  = 0.333 / 2 = 16.7% of portfolio per trade
  Quarter-Kelly = 0.333 / 4 = 8.3% of portfolio per trade (recommended)

  If Kelly is negative, the system has NEGATIVE expectancy — do not trade it.

Value at Risk (VaR)

Parametric VaR = Portfolio Value * Portfolio Volatility * Z-score * sqrt(Time Horizon)

Z-scores:  90% confidence = 1.282
           95% confidence = 1.645
           99% confidence = 2.326

Worked example (daily VaR, 95% confidence):
  Portfolio = $10,000
  Daily volatility (stddev of daily returns) = 2.0%
  VaR = $10,000 * 0.02 * 1.645 * sqrt(1) = $329.00
  Meaning: 95% confident daily loss will not exceed $329.

  Weekly VaR = $329 * sqrt(5) = $329 * 2.236 = $735.65
  Monthly VaR = $329 * sqrt(21) = $329 * 4.583 = $1,507.81

Sharpe Ratio

Sharpe = (Rp - Rf) / StdDev(Rp) * sqrt(252)
Where:
  Rp = mean daily portfolio return
  Rf = daily risk-free rate (Treasury yield / 252)
  StdDev(Rp) = standard deviation of daily returns
  252 = trading days per year (annualization factor)

Worked example:
  Mean daily return = 0.10% (0.001)
  Annual Treasury yield = 5.0% -> daily Rf = 0.05/252 = 0.000198
  StdDev of daily returns = 0.80% (0.008)
  Daily Sharpe = (0.001 - 0.000198) / 0.008 = 0.100
  Annualized Sharpe = 0.100 * sqrt(252) = 0.100 * 15.875 = 1.59

  Ratings:
    < 0.5  = Poor (not compensated for risk)
    0.5-1.0 = Acceptable
    1.0-2.0 = Good
    2.0-3.0 = Very Good
    > 3.0   = Excellent (verify — may indicate overfitting)

Sortino Ratio (Downside-Only Risk)

Sortino = (Rp - Rf) / DownsideDeviation * sqrt(252)
DownsideDeviation = sqrt(mean(min(Ri - Rf, 0)^2))

Better than Sharpe because it only penalizes downside volatility, not upside.
Sortino > 2.0 is considered very good.

Maximum Drawdown

For each point t in equity curve:
  Peak(t)     = max(Equity[0..t])
  Drawdown(t) = (Peak(t) - Equity(t)) / Peak(t) * 100%
  MaxDrawdown = max(Drawdown(t)) for all t

Worked example:
  Equity curve: $10,000 -> $12,000 -> $9,600 -> $11,500
  Peak at $12,000
  Drawdown at $9,600 = (12,000 - 9,600) / 12,000 = 20.0%
  Max Drawdown = 20.0%

Recovery Factor = Total Net Profit / Max Drawdown
  If total profit = $3,000, MaxDD = $2,400 -> RF = 3,000/2,400 = 1.25

Calmar Ratio = Annual Return / Max Drawdown
  If annual return = 25%, MaxDD = 20% -> Calmar = 1.25 (target > 1.0)

Profit Factor

Profit Factor = Gross Winning Trades / Gross Losing Trades

Worked example:
  10 winning trades totaling $5,000
  8 losing trades totaling $3,200
  Profit Factor = 5,000 / 3,200 = 1.5625

  Ratings: < 1.0 = losing system, 1.0-1.5 = marginal, 1.5-2.0 = good,
           2.0-3.0 = very good, > 3.0 = excellent (verify with enough trades)

Expectancy Per Trade

Expectancy = (Win% * AvgWin) - (Loss% * AvgLoss)

Worked example:
  Win rate: 55%, Average win: $150, Average loss: $100
  Expectancy = (0.55 * 150) - (0.45 * 100) = 82.50 - 45.00 = $37.50/trade
  Over 100 trades: expected profit = $3,750

  Minimum for a viable system: Expectancy > 0 with at least 30 sample trades.

Risk/Reward Ratio

R:R = (Target Price - Entry Price) / (Entry Price - Stop Loss Price)

Worked example:
  Entry = $100, Stop = $95, Target = $112
  R:R = (112 - 100) / (100 - 95) = 12 / 5 = 2.4:1

  Minimum acceptable R:R = 1.5:1
  With 40% win rate and 2:1 R:R: Expectancy = 0.40*2 - 0.60*1 = +0.20 (profitable!)
  With 40% win rate and 1:1 R:R: Expectancy = 0.40*1 - 0.60*1 = -0.20 (losing!)

4. Alpaca Trading API Reference

Authentication

# Paper trading (ALWAYS start here)
BASE_URL="https://paper-api.alpaca.markets"

# Live trading (only after paper validation)
# BASE_URL="https://api.alpaca.markets"

# Data API (same for both paper and live)
DATA_URL="https://data.alpaca.markets"

# Auth headers (required on every request)
HEADERS="-H 'APCA-API-KEY-ID: $ALPACA_API_KEY' -H 'APCA-API-SECRET-KEY: $ALPACA_SECRET_KEY'"

Account Information

# Get account details
curl -s "$BASE_URL/v2/account" $HEADERS
# Key fields: id, status, equity, cash, buying_power, portfolio_value,
#   pattern_day_trader (bool), daytrade_count, last_equity

Get Current Positions

# All positions
curl -s "$BASE_URL/v2/positions" $HEADERS
# Returns array: symbol, qty, side, avg_entry_price, current_price,
#   unrealized_pl, unrealized_plpc, market_value, cost_basis

# Single position
curl -s "$BASE_URL/v2/positions/AAPL" $HEADERS

Place Orders

# Market order (fills immediately at best available price)
curl -s -X POST "$BASE_URL/v2/orders" $HEADERS \
  -H "Content-Type: application/json" \
  -d '{"symbol":"AAPL","qty":"10","side":"buy","type":"market","time_in_force":"day"}'

# Limit order (fills only at your price or better)
curl -s -X POST "$BASE_URL/v2/orders" $HEADERS \
  -H "Content-Type: application/json" \
  -d '{"symbol":"AAPL","qty":"10","side":"buy","type":"limit","time_in_force":"gtc","limit_price":"150.00"}'

# Stop order (triggers market order when stop price hit)
curl -s -X POST "$BASE_URL/v2/orders" $HEADERS \
  -H "Content-Type: application/json" \
  -d '{"symbol":"AAPL","qty":"10","side":"sell","type":"stop","time_in_force":"gtc","stop_price":"145.00"}'

# Stop-limit order (triggers limit order when stop price hit)
curl -s -X POST "$BASE_URL/v2/orders" $HEADERS \
  -H "Content-Type: application/json" \
  -d '{"symbol":"AAPL","qty":"10","side":"sell","type":"stop_limit","time_in_force":"gtc","stop_price":"145.00","limit_price":"144.50"}'

# Trailing stop (dynamic stop that trails price by dollar or percent amount)
curl -s -X POST "$BASE_URL/v2/orders" $HEADERS \
  -H "Content-Type: application/json" \
  -d '{"symbol":"AAPL","qty":"10","side":"sell","type":"trailing_stop","time_in_force":"gtc","trail_percent":"5"}'

# Bracket order (entry + stop loss + take profit as one atomic order)
curl -s -X POST "$BASE_URL/v2/orders" $HEADERS \
  -H "Content-Type: application/json" \
  -d '{
    "symbol": "AAPL",
    "qty": "10",
    "side": "buy",
    "type": "limit",
    "time_in_force": "day",
    "limit_price": "150.00",
    "order_class": "bracket",
    "stop_loss": {"stop_price": "145.00"},
    "take_profit": {"limit_price": "165.00"}
  }'

# OCO order (one-cancels-other: stop loss OR take profit, whichever hits first)
curl -s -X POST "$BASE_URL/v2/orders" $HEADERS \
  -H "Content-Type: application/json" \
  -d '{
    "symbol": "AAPL",
    "qty": "10",
    "side": "sell",
    "type": "limit",
    "time_in_force": "gtc",
    "limit_price": "165.00",
    "order_class": "oco",
    "stop_loss": {"stop_price": "145.00"}
  }'

Order parameters reference:

Manage Orders

# List open orders
curl -s "$BASE_URL/v2/orders?status=open" $HEADERS

# Get specific order
curl -s "$BASE_URL/v2/orders/{order_id}" $HEADERS

# Cancel specific order
curl -s -X DELETE "$BASE_URL/v2/orders/{order_id}" $HEADERS

# Cancel ALL open orders
curl -s -X DELETE "$BASE_URL/v2/orders" $HEADERS

Close Positions

# Close entire position in a symbol
curl -s -X DELETE "$BASE_URL/v2/positions/AAPL" $HEADERS

# Partially close (sell 5 of 10 shares)
curl -s -X DELETE "$BASE_URL/v2/positions/AAPL?qty=5" $HEADERS

# EMERGENCY: Close ALL positions
curl -s -X DELETE "$BASE_URL/v2/positions" $HEADERS

Market Data (free with Alpaca account)

# Latest quote (bid/ask)
curl -s "$DATA_URL/v2/stocks/AAPL/quotes/latest" $HEADERS

# Latest trade (last fill)
curl -s "$DATA_URL/v2/stocks/AAPL/trades/latest" $HEADERS

# Historical bars (OHLCV) — daily
curl -s "$DATA_URL/v2/stocks/AAPL/bars?timeframe=1Day&start=2024-01-01&limit=100" $HEADERS

# Intraday bars — 5-minute
curl -s "$DATA_URL/v2/stocks/AAPL/bars?timeframe=5Min&start=$(date -d 'today' +%Y-%m-%d)&limit=78" $HEADERS

# Multi-symbol snapshot
curl -s "$DATA_URL/v2/stocks/snapshots?symbols=AAPL,MSFT,GOOGL" $HEADERS

# Crypto bars
curl -s "$DATA_URL/v1beta3/crypto/us/bars?symbols=BTC/USD&timeframe=1Day&limit=30" $HEADERS

# Crypto latest quote
curl -s "$DATA_URL/v1beta3/crypto/us/latest/quotes?symbols=BTC/USD,ETH/USD" $HEADERS

Market Clock & Calendar

# Is market open right now?
curl -s "$BASE_URL/v2/clock" $HEADERS
# Returns: timestamp, is_open (bool), next_open, next_close

# Upcoming market calendar
curl -s "$BASE_URL/v2/calendar?start=$(date +%Y-%m-%d)&end=$(date -d '+7 days' +%Y-%m-%d)" $HEADERS

Crypto Trading Notes

• Symbols use slash format: BTC/USD, ETH/USD, SOL/USD, DOGE/USD
• 24/7 trading (no market hours restriction)
• Fractional quantities allowed (e.g., "qty": "0.001" for BTC)
• Paper trading works identically to live
• Use notional for dollar-based crypto orders: "notional": "100.00" buys $100 worth

Account Activity & History

# Trade history
curl -s "$BASE_URL/v2/account/activities/FILL?after=2024-01-01" $HEADERS

# Portfolio history
curl -s "$BASE_URL/v2/account/portfolio/history?period=1M&timeframe=1D" $HEADERS
# Returns: timestamp[], equity[], profit_loss[], profit_loss_pct[]

5. Free Financial Data Sources

Price Data (via web_search + web_fetch)

Fundamental Data

Sentiment & Alternative Data

Macro Economic Data

Crypto-Specific Sources

6. Confidence Calibration Guide (Superforecasting)

Calibration Principles (Philip Tetlock)

• A "70% confident" prediction should be right about 70% of the time
• Most people are overconfident: their "90%" predictions are right only ~70%
• Track your predictions systematically and compare predicted vs actual frequency
• Update incrementally (2-5% per new piece of evidence), not dramatically

Confidence Level Guide

Brier Score for Trade Predictions

Brier Score = mean((predicted_probability - actual_outcome)^2)
actual_outcome: 1 if prediction was correct, 0 if wrong

Worked example (5 predictions):
  Pred 1: 80% confident -> correct (1)  -> (0.80 - 1)^2 = 0.04
  Pred 2: 60% confident -> wrong (0)    -> (0.60 - 0)^2 = 0.36
  Pred 3: 70% confident -> correct (1)  -> (0.70 - 1)^2 = 0.09
  Pred 4: 90% confident -> correct (1)  -> (0.90 - 1)^2 = 0.01
  Pred 5: 55% confident -> wrong (0)    -> (0.55 - 0)^2 = 0.30
  Brier Score = (0.04 + 0.36 + 0.09 + 0.01 + 0.30) / 5 = 0.16

  Ratings: 0.00 = perfect, < 0.15 = excellent, 0.15-0.25 = good,
           0.25 = coin flip, > 0.25 = worse than random

Calibration Self-Check Protocol

After accumulating 20+ trade predictions, group by confidence bucket:

1. Are your 60% predictions right ~60% of the time?
2. If your 60% predictions are right 80% of the time, you are underconfident — adjust up
3. If your 80% predictions are right 55% of the time, you are overconfident — adjust down
4. Recalibrate your confidence scale after every 50 resolved predictions

7. Trading Psychology & Cognitive Biases

Biases to Watch For

Discipline Rules

1. Every trade has a written plan BEFORE entry: entry price, stop loss, target, position size, thesis
2. Write down your reasoning BEFORE entering — if you cannot articulate the edge, do not trade
3. Set stop-losses at order entry time, not "in your head"
4. Review your journal weekly — look for patterns in wins AND losses
5. Take breaks after big wins (overconfidence risk) AND big losses (emotional risk)
6. Never average down on a losing position unless the original thesis explicitly planned for it
7. Never move a stop-loss further away from your entry (only tighten, never widen)
8. The market will be there tomorrow — missing a trade is not a loss, but a blown account is

8. Portfolio Construction

Asset Allocation Guidelines

Sector Diversification

Maximum 30% in any single sector:

• Technology, Healthcare, Financials, Consumer Discretionary, Consumer Staples
• Energy, Industrials, Utilities, Real Estate, Materials, Communication Services

Correlation Awareness

Highly correlated positions amplify risk. Check correlations before adding:

Rebalancing Rules

• Calendar: Rebalance quarterly (first trading day of quarter)
• Threshold: Rebalance when any allocation drifts > 5% from target
• Tax-aware: Prefer rebalancing via new contributions rather than selling (taxable accounts)

9. Cross-Platform Commands

Windows (PowerShell / Git Bash)

# Python might be `python` not `python3` on Windows
python -c "import json; ..."

# Use forward slashes in file paths or escape backslashes
# curl is available via Git Bash, PowerShell, or WSL

# Check if market is open (Windows Git Bash)
curl -s "$BASE_URL/v2/clock" -H "APCA-API-KEY-ID: $ALPACA_API_KEY" \
  -H "APCA-API-SECRET-KEY: $ALPACA_SECRET_KEY" | python -c "
import sys, json
d = json.load(sys.stdin)
print('OPEN' if d['is_open'] else 'CLOSED', '| Next:', d.get('next_open','') or d.get('next_close',''))
"

macOS / Linux

python3 -c "import json; ..."
# curl, jq typically available by default
# Use jq for JSON processing:
curl -s URL | jq '.equity'

JSON Processing Without jq

# Pretty-print JSON
python3 -c "import sys,json; print(json.dumps(json.load(sys.stdin),indent=2))" < file.json

# Extract specific field
curl -s URL | python3 -c "import sys,json; d=json.load(sys.stdin); print(d['equity'])"

# Parse Alpaca positions into readable table
curl -s "$BASE_URL/v2/positions" $HEADERS | python3 -c "
import sys, json
positions = json.load(sys.stdin)
fmt = '{:<8} {:>6} {:>10} {:>10} {:>12} {:>8}'
print(fmt.format('Symbol','Qty','Entry','Current','P/L','P/L pct'))
print('-' * 60)
for p in positions:
    print(fmt.format(p['symbol'], p['qty'], float(p['avg_entry_price']),
          float(p['current_price']), float(p['unrealized_pl']),
          round(float(p['unrealized_plpc'])*100,2)))
"

# Calculate RSI from historical bars
curl -s "$DATA_URL/v2/stocks/AAPL/bars?timeframe=1Day&limit=30" $HEADERS | python3 -c "
import sys, json
data = json.load(sys.stdin)
closes = [float(b['c']) for b in data['bars']]
changes = [closes[i]-closes[i-1] for i in range(1, len(closes))]
gains = [max(c,0) for c in changes[-14:]]
losses = [abs(min(c,0)) for c in changes[-14:]]
avg_gain = sum(gains)/14
avg_loss = sum(losses)/14
rs = avg_gain/avg_loss if avg_loss > 0 else 999
rsi = 100 - (100/(1+rs))
print(f'RSI(14) = {rsi:.1f}')
"

10. Pre-Trade Checklist

Before every trade, verify ALL of the following:

PRE-TRADE CHECKLIST
====================
[ ] 1. TREND: What is the higher-timeframe trend? (Daily chart 200 SMA)
       - Trading WITH the trend? (preferred)
       - Counter-trend? (requires stronger signal + tighter stops)

[ ] 2. SIGNAL: What specific setup triggered this trade?
       - Indicator signal (RSI, MACD, etc.)
       - Pattern (candlestick, chart pattern)
       - Catalyst (earnings, news, sector rotation)

[ ] 3. ENTRY: Exact entry price or condition
       - Limit order at specific level? Market order on breakout?

[ ] 4. STOP LOSS: Exact stop price
       - Based on ATR (2-3x ATR from entry)
       - Below key support (long) or above key resistance (short)
       - NEVER wider than 2% of portfolio

[ ] 5. TARGET: Exact take-profit price
       - Risk/Reward at least 1.5:1 (preferably 2:1+)
       - At logical resistance (long) or support (short)

[ ] 6. POSITION SIZE: Calculated from risk management rules
       - Risk amount = Portfolio * 1-2%
       - Shares = Risk amount / (Entry - Stop)
       - Total position < 10% of portfolio

[ ] 7. CORRELATION CHECK: Does this overlap with existing positions?
       - Not adding to concentrated sector exposure
       - Total portfolio heat (sum of open risk) < 6%

[ ] 8. CATALYST CHECK: Any upcoming events that could gap through stops?
       - Earnings date? Fed meeting? CPI release?
       - If yes: reduce size or wait until after event

[ ] 9. MARKET CONTEXT: Is the overall market favorable?
       - Fear & Greed index level
       - VIX level (>30 = caution, <15 = complacency risk)
       - Market trend (SPY vs 200 SMA)

[ ] 10. CONFIDENCE: Rate 1-10 honestly
        - Below 6? Skip the trade
        - Record confidence for calibration tracking

11. Trade Journal Template

{
  "trade_id": "T001",
  "date_opened": "2025-01-15",
  "date_closed": null,
  "symbol": "AAPL",
  "side": "long",
  "entry_price": 150.00,
  "stop_loss": 145.00,
  "target": 162.00,
  "position_size": 40,
  "risk_amount": 200.00,
  "risk_reward": 2.4,
  "setup": "Bullish engulfing at 50 EMA + RSI divergence",
  "confidence": 7,
  "market_context": "SPY above 200 SMA, VIX at 18, F&G neutral (52)",
  "pre_trade_thesis": "AAPL pulled back to 50 EMA support, RSI showing bullish divergence, earnings in 3 weeks should provide catalyst. Sector (tech) is leading.",
  "result": {
    "exit_price": null,
    "exit_reason": null,
    "pnl": null,
    "pnl_percent": null,
    "held_days": null,
    "lessons": null
  }
}

Store trade journals using memory_store for tracking and calibration review.