trader-hand-skill — Автоматизация торговых решений на основе AI

--- name: trader-hand-skill version: "1.0.0" description: "Expert knowledge for autonomous market intelligence and trading — technical analysis, risk management, Alpaca API, financial data sources" author: OpenFang tags: [trading, finance, stocks, crypto, technical-analysis, risk-management] tools: [shell_exec, file_read, file_write, web_fetch, web_search, memory_store] runtime: prompt_only --- # 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 ```bash # 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: $ALPACA_SECRET_KEY'" ``` ### Account Information ```bash # Get account details curl -s "$BASE_URL/v2/account" $HEADERS # Key fields: id, status, equity, cash, buying_power, portfolio_value, # pattern_day_trader (bool), daytrade_count, last_equity ``` ### Get Current Positions ```bash # All positions curl -s "$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" $HEADERS ``` ### Place Orders ```bash # Market order (fills immediately at best available price) curl -s -X POST "$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" $HEADERS -H "Content-Type: application/json" -d '{ "symbol": "AAPL", "qty": "10", "side": "sell", "type": "limit", "time_in_force": "gtc", "limit_price": "165.00", "order_class": "oco", "stop_loss": {"stop_price": "145.00"} }' ``` **Order parameters reference:** | Parameter | Values | Notes | |-----------|--------|-------| | `side` | `buy`, `sell` | | | `type` | `market`, `limit`, `stop`, `stop_limit`, `trailing_stop` | | | `time_in_force` | `day`, `gtc`, `ioc`, `fok` | day = cancel at close, gtc = good til canceled | | `order_class` | `simple`, `bracket`, `oco`, `oto` | bracket = entry + stop + target | | `qty` | String number | Whole shares for stocks | | `notional` | String dollar amount | Alternative to qty (fractional shares) | ### Manage Orders ```bash # List open orders curl -s "$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" $HEADERS ``` ### Close Positions ```bash # Close entire position in a symbol curl -s -X DELETE "$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" $HEADERS ``` ### Market Data (free with Alpaca account) ```bash # Latest quote (bid/ask) curl -s "$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" $HEADERS ``` ### Market Clock & Calendar ```bash # Is market open right now? curl -s "$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 $100 worth ### Account Activity & History ```bash # Trade history curl -s "$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: 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 | 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 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 | 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) ```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: $ALPACA_SECRET_KEY" | python -c " import sys, json d = json.load(sys.stdin) print('OPEN' if d['is_open'] else 'CLOSED', '| Next:', d.get('next_open','') or d.get('next_close','')) " ``` ### macOS / Linux ```bash python3 -c "import json; ..." # curl, jq typically available by default # Use jq for JSON processing: curl -s URL | jq '.equity' ``` ### JSON Processing Without jq ```bash # Pretty-print JSON python3 -c "import sys,json; print(json.dumps(json.load(sys.stdin),indent=2))" < file.json # Extract specific field curl -s URL | python3 -c "import sys,json; d=json.load(sys.stdin); print(d['equity'])" # Parse Alpaca positions into readable table curl -s "$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 ```json { "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.