trader-hand-skill

Data Community v1.0.0

Repo stars 17,717
Author repo openfang

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Expert knowledge for autonomous market intelligence and trading — technical analysis, risk management, Alpaca API, financial dat…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “Reference Knowledge”, “1. Technical Analysis Indicators Reference”, “RSI (Relative Strength Index)”, “MACD (Moving Average Convergence Divergence)”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name trader-hand-skill directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “Reference Knowledge / 1. Technical Analysis Indicators Reference / RSI (Relative Strength Index)” before expanding.

Boundaries And Review

Dependencies: Prepare Vendor-specific API keys before running a full task.
Permissions: Declared permissions include read / write / shell-exec; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Trading Expert Knowledge

Reference Knowledge

1. Technical Analysis Indicators Reference

RSI (Relative Strength Index)

Formula: RSI = 100 - (100 / (1 + RS))
Where:  RS = Average Gain / Average Loss over N periods (default N = 14)

Step-by-step calculation:
  1. For each period, compute change = Close(t) - Close(t-1)
  2. Gains = max(change, 0), Losses = abs(min(change, 0))
  3. First average: simple mean of first 14 gains/losses
  4. Subsequent: AvgGain = (PrevAvgGain * 13 + CurrentGain) / 14  (Wilder smoothing)
  5. RS = AvgGain / AvgLoss
  6. RSI = 100 - (100 / (1 + RS))

Worked example (14-period):
  Avg Gain over 14 periods = 1.02
  Avg Loss over 14 periods = 0.68
  RS = 1.02 / 0.68 = 1.50
  RSI = 100 - (100 / (1 + 1.50)) = 100 - 40 = 60.0

Interpretation:

RSI < 30: Oversold territory (potential buy signal)
RSI > 70: Overbought territory (potential sell signal)
RSI = 50: Neutral — price momentum balanced

Advanced RSI Signals:

Signal	Description	Strength
Bearish divergence	Price makes new high, RSI makes lower high	Strong reversal warning
Bullish divergence	Price makes new low, RSI makes higher low	Strong reversal warning
Bullish failure swing	RSI drops below 30, bounces, pulls back above 30, breaks prior RSI high	Very strong buy
Bearish failure swing	RSI rises above 70, drops, bounces below 70, breaks prior RSI low	Very strong sell
Range shift	RSI oscillates 40-80 in uptrend, 20-60 in downtrend	Trend confirmation

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:

Signal	Condition	Strength
Bullish crossover	MACD crosses above Signal Line	Moderate buy
Bearish crossover	MACD crosses below Signal Line	Moderate sell
Zero-line bullish cross	MACD crosses above zero	Trend change to bullish
Zero-line bearish cross	MACD crosses below zero	Trend change to bearish
Histogram expansion	Bars growing taller	Momentum accelerating
Histogram contraction	Bars shrinking	Momentum weakening, reversal may come
Bullish divergence	Price new low, MACD higher low	Strong reversal signal
Bearish divergence	Price new high, MACD lower high	Strong reversal signal

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:

Signal	Condition	Meaning
Squeeze	Bandwidth at 6-month low	Volatility contraction, big move imminent
Squeeze breakout up	Price breaks above upper band after squeeze	Strong bullish breakout
Squeeze breakout down	Price breaks below lower band after squeeze	Strong bearish breakout
Walking the upper band	Price hugs upper band with middle band rising	Strong uptrend — do NOT short
Walking the lower band	Price hugs lower band with middle band falling	Strong downtrend — do NOT buy
Mean reversion touch	Price touches outer band, %B reverses	Potential reversion to middle band
W-bottom	Price hits lower band twice, second low has higher %B	Bullish reversal pattern
M-top	Price hits upper band twice, second high has lower %B	Bearish reversal pattern

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:

Cross	Name	Meaning	Reliability
50 MA > 200 MA	Golden Cross	Bullish trend reversal	High (lag ~2 weeks)
50 MA < 200 MA	Death Cross	Bearish trend reversal	High (lag ~2 weeks)
9 EMA > 21 EMA	Fast bullish cross	Short-term momentum shift	Moderate
Price > 200 SMA	Above long-term trend	Bullish regime	Very High
Price < 200 SMA	Below long-term trend	Bearish regime	Very High

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:

Use Case	Formula	Example
Stop-loss placement	Entry - 2 * ATR	Entry $100, ATR $2.50 -> Stop at $95.00
Take-profit target	Entry + 3 * ATR	Entry $100, ATR $2.50 -> Target $107.50
Position sizing	Risk$ / ATR	$200 risk / $2.50 ATR = 80 shares
Volatility filter	ATR > threshold	Only trade when ATR > daily average (avoid dead markets)
Trailing stop	Highest close - 3 * ATR	Locks in profit as price rises

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:

Price Action	Volume	Interpretation
Price up	Volume up	Strong bullish — legitimate move
Price up	Volume down	Weak rally — likely to reverse
Price down	Volume up	Strong bearish — capitulation or breakdown
Price down	Volume down	Weak decline — may be nearing bottom
Breakout	Volume > 150% of 20-day avg	Confirmed breakout — take the trade
Breakout	Volume < average	Failed breakout likely — wait or fade
Volume climax	Extreme volume spike (3x+ average)	Potential exhaustion/reversal point

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

Pattern	Signal	Body	Wicks	Context Required
Doji	Indecision	Open = Close (or nearly)	Long both sides	At S/R level = reversal
Hammer	Bullish reversal	Small, at top of candle	Lower wick > 2x body	Must appear at bottom of downtrend
Inverted Hammer	Bullish reversal	Small, at bottom of candle	Upper wick > 2x body	At bottom of downtrend, needs confirmation
Shooting Star	Bearish reversal	Small, at bottom of candle	Upper wick > 2x body	Must appear at top of uptrend
Hanging Man	Bearish reversal	Small, at top of candle	Lower wick > 2x body	At top of uptrend (same shape as Hammer)
Marubozu (Bullish)	Strong continuation	Full green body, no wicks	None	Strong buying pressure
Marubozu (Bearish)	Strong continuation	Full red body, no wicks	None	Strong selling pressure
Spinning Top	Indecision	Small body centered	Equal wicks both sides	Trend may be losing steam
Dragonfly Doji	Bullish reversal	Open = Close = High	Long lower wick only	At support = strong reversal signal
Gravestone Doji	Bearish reversal	Open = Close = Low	Long upper wick only	At resistance = strong reversal signal

Multi-Candle Patterns

Pattern	Signal	Description	Reliability
Bullish Engulfing	Reversal up	Large green candle fully engulfs prior red candle	High at support
Bearish Engulfing	Reversal down	Large red candle fully engulfs prior green candle	High at resistance
Morning Star	Bullish reversal	Red candle, small body/doji with gap, large green candle	Very High
Evening Star	Bearish reversal	Green candle, small body/doji with gap, large red candle	Very High
Three White Soldiers	Strong bullish	Three consecutive large green candles, each closing higher	Very High
Three Black Crows	Strong bearish	Three consecutive large red candles, each closing lower	Very High
Bullish Harami	Potential reversal	Large red, then small green contained within red's body	Moderate (needs confirmation)
Bearish Harami	Potential reversal	Large green, then small red contained within green's body	Moderate (needs confirmation)
Tweezer Bottom	Bullish reversal	Two candles with matching lows at support	High
Tweezer Top	Bearish reversal	Two candles with matching highs at resistance	High
Piercing Line	Bullish reversal	Red candle, then green opens below red's low and closes above 50% of red's body	Moderate-High
Dark Cloud Cover	Bearish reversal	Green candle, then red opens above green's high and closes below 50% of green's body	Moderate-High

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:

Parameter	Values	Notes
`side`	`buy`, `sell`
`type`	`market`, `limit`, `stop`, `stop_limit`, `trailing_stop`
`time_in_force`	`day`, `gtc`, `ioc`, `fok`	day = cancel at close, gtc = good til canceled
`order_class`	`simple`, `bracket`, `oco`, `oto`	bracket = entry + stop + target
`qty`	String number	Whole shares for stocks
`notional`	String dollar amount	Alternative to qty (fractional shares)

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)

Source	URL Pattern	Data Available
Yahoo Finance	`finance.yahoo.com/quote/AAPL`	Realtime quotes, charts, financials, analyst ratings
Google Finance	`google.com/finance/quote/AAPL:NASDAQ`	Quotes, news, related stocks, earnings
CoinGecko	`coingecko.com/en/coins/bitcoin`	Crypto prices, market cap, volume, 24h change
CoinMarketCap	`coinmarketcap.com/currencies/bitcoin/`	Crypto prices, rankings, dominance, supply
MarketWatch	`marketwatch.com/investing/stock/AAPL`	Quotes, news, analysis, options data
Finviz	`finviz.com/quote.ashx?t=AAPL`	Technical + fundamental screener, charts
TradingView	`tradingview.com/symbols/NASDAQ-AAPL/`	Charts, technicals, community ideas

Fundamental Data

Source	URL Pattern	Data Available
Macrotrends	`macrotrends.net/stocks/charts/AAPL/apple/pe-ratio`	P/E, revenue, margins, historical
Simply Wall St	Web search: `"AAPL simply wall st"`	Visual fundamental analysis, fair value
SEC EDGAR	`sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=AAPL&type=10-K`	Official 10-K, 10-Q, 8-K filings
Earnings Whispers	`earningswhispers.com/stocks/AAPL`	Earnings estimates, surprise history, calendar
Stock Analysis	`stockanalysis.com/stocks/AAPL/financials/`	Clean financial statements, ratios
Wisesheets	Web search: `"AAPL income statement"`	Financial data in spreadsheet format

Sentiment & Alternative Data

Source	URL	Data Available
CNN Fear & Greed	`money.cnn.com/data/fear-and-greed/`	Market sentiment index 0-100 (Extreme Fear to Extreme Greed)
CBOE VIX	Web search: `"VIX index today"`	Volatility index (>30 = fear, <15 = complacency)
Finviz Map	`finviz.com/map.ashx`	Market heatmap by sector/size
StockTwits	`stocktwits.com/symbol/AAPL`	Social sentiment (bullish/bearish ratio)
Put/Call Ratio	Web search: `"CBOE put call ratio today"`	Options sentiment (>1.0 = bearish, <0.7 = bullish)
Short Interest	`finviz.com/quote.ashx?t=AAPL` -> Short Float	Percent of float sold short
Insider Trading	`openinsider.com/screener`	CEO/CFO buy/sell patterns

Macro Economic Data

Source	URL	Data Available
FRED	`fred.stlouisfed.org`	Interest rates, CPI, employment, GDP, M2, yield curve
Treasury.gov	`treasury.gov/resource-center/data-chart-center/interest-rates/`	Daily Treasury yield curve
CME FedWatch	Web search: `"CME FedWatch tool"`	Federal funds rate probabilities
BLS	`bls.gov/news.release/`	Employment situation, CPI, PPI
ISM	Web search: `"ISM manufacturing PMI"`	PMI (>50 = expansion, <50 = contraction)
Conference Board	Web search: `"consumer confidence index"`	Consumer confidence, leading indicators
Earnings Calendar	`earningswhispers.com/calendar`	Upcoming earnings dates
Economic Calendar	Web search: `"economic calendar this week"`	Scheduled data releases

Crypto-Specific Sources

Source	URL	Data Available
CoinGecko	`coingecko.com`	Prices, market cap, volume, DeFi TVL
DefiLlama	`defillama.com`	Total Value Locked across all chains
Glassnode (free tier)	Web search: `"bitcoin on-chain metrics"`	On-chain analytics (NUPL, MVRV, exchange flows)
Bitcoin Fear & Greed	`alternative.me/crypto/fear-and-greed-index/`	Crypto-specific sentiment 0-100
Ultrasound Money	`ultrasound.money`	ETH supply/burn metrics

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

Level	Meaning	Evidence Required	Trading Action
20-30%	Slight lean	Single weak signal, limited data	No trade — insufficient edge
40-50%	Toss-up with slight edge	Conflicting signals, moderate evidence	No trade — coin flip
55-65%	Moderate conviction	Multiple aligned signals, historical precedent	Small position, wide stops
70-80%	Strong conviction	Strong multi-factor alignment, catalyst identified	Standard position size
85-95%	Very high conviction	Overwhelming evidence — be suspicious of yourself	Full position, but NEVER all-in

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:

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

7. Trading Psychology & Cognitive Biases

Biases to Watch For

Bias	Description	Mitigation
Confirmation Bias	Seeking info that confirms your thesis	Always build the opposing case first (adversarial debate)
Anchoring	Over-weighting the first number you see (entry price, analyst target)	Start analysis from base rates and current data, not old prices
Recency Bias	Over-weighting recent events (last week's crash, last month's rally)	Look at longer timeframes — 6-month and 1-year charts minimum
Loss Aversion	Holding losers too long ("it'll come back"), cutting winners too fast	Use mechanical stop-losses and take-profit targets, set BEFORE entry
Overconfidence	Believing you are more right than you are	Track Brier scores, use Kelly fractions, never bet > 2% per trade
Narrative Bias	Compelling story = good trade (often false)	Focus on quantitative data, not stories. "Good company" != "good trade"
FOMO	Fear of missing out, chasing entries	Only enter at planned levels. The market is open 252 days a year
Sunk Cost	"I've lost so much, I can't sell now"	Each moment is a new decision. Ask: "Would I enter this trade NOW at current price?"
Hindsight Bias	"I knew that would happen"	Journal BEFORE trades with specific predictions, not after
Disposition Effect	Selling winners early to "lock in profits" but holding losers	Let winners run (trail stops), cut losers at planned stops
Gambler's Fallacy	"It's dropped 5 days in a row, it HAS to bounce"	Each day is independent. Trends persist more often than they reverse
Endowment Effect	Overvaluing positions you already own	Evaluate positions as if you were building from scratch today

Discipline Rules

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

8. Portfolio Construction

Asset Allocation Guidelines

Style	Equities	Crypto	Fixed Income / Cash	Max Single Position
Conservative	50-60%	0-5%	35-50%	5%
Moderate	60-75%	5-15%	10-35%	8%
Aggressive	70-85%	10-25%	5-20%	10%
Speculative	50-70%	20-40%	5-10%	15% (with strict stops)

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:

Pair	Typical Correlation	Risk
AAPL + MSFT + GOOGL	0.7-0.9	Concentrated large-cap tech
BTC + ETH + SOL	0.8-0.95	Concentrated crypto (moves together)
SPY + QQQ	0.9+	Nearly identical exposure
Stocks + Bonds	-0.2 to 0.3	Genuinely diversifying
Gold + Stocks	-0.1 to 0.2	Hedge in crisis
VIX + SPY	-0.8	Inverse — VIX as hedge

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.

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: Data · trading · finance · stocks
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 98 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @RightNow-AI · v1.0.0 · no license declared
Fluxly token estimate: Moderate
Fluxly setup estimate: Manual integration
External API key: Required · Vendor-specific
Detected OS requirements: macOS · Linux · Windows · WSL
Runtime requirements: Python
Detected file/system behavior: Read-only
Write / modify
Shell exec
Detected network behavior: External requests
Install commands: None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Output preview trader-hand-skill.preview

The current SKILL.md does not define a fixed output example.

SKILL.md SKILL.md

# Trading Expert Knowledge

## Reference Knowledge

## 1. Technical Analysis Indicators Reference

### RSI (Relative Strength Index)
```
Formula: RSI = 100 - (100 / (1 + RS))
Where:  RS = Average Gain / Average Loss over N periods (default N = 14)

Step-by-step calculation:
  1. For each period, compute change = Close(t) - Close(t-1)
  2. Gains = max(change, 0), Losses = abs(min(change, 0))
  3. First average: simple mean of first 14 gains/losses
  4. Subsequent: AvgGain = (PrevAvgGain * 13 + CurrentGain) / 14  (Wilder smoothing)
  5. RS = AvgGain / AvgLoss
  6. RSI = 100 - (100 / (1 + RS))

Worked example (14-period):
  Avg Gain over 14 periods = 1.02
  Avg Loss over 14 periods = 0.68
  RS = 1.02 / 0.68 = 1.50
  RSI = 100 - (100 / (1 + 1.50)) = 100 - 40 = 60.0
```

**Interpretation:**
- RSI < 30: Oversold territory (potential buy signal)
- RSI > 70: Overbought territory (potential sell signal)
- RSI = 50: Neutral — price momentum balanced

**Advanced RSI Signals:**
| Signal | Description | Strength |
|--------|-------------|----------|
| Bearish divergence | Price makes new high, RSI makes lower high | Strong reversal warning |
| Bullish divergence | Price makes new low, RSI makes higher low | Strong reversal warning |
| Bullish failure swing | RSI drops below 30, bounces, pulls back above 30, breaks prior RSI high | Very strong buy |
| Bearish failure swing | RSI rises above 70, drops, bounces below 70, breaks prior RSI low | Very strong sell |
| Range shift | RSI oscillates 40-80 in uptrend, 20-60 in downtrend | Trend confirmation |

**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.

---

…

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> Reference Knowledge → 1. Technical Analysis Indicators Reference → RSI (Relative Strength Index) → MACD (Moving Average Convergence Divergence) → Bollinger Bands → VWAP (Volume Weighted Average Price)
terms    -> Interpretation · Advanced RSI Signals · Best practices · Key Bollinger Band Signals · Usage · Institutional benchmark · Intraday S/R · Entry filter
files/cmd -> side · buy · sell · type · market · limit · stop · stoplimit
body sha256 -> 7d1e89b26ea4