trader-hand-skill
Expert knowledge for autonomous market intelligence and trading — technical analysis, risk management, Alpaca API, financial data sources
What this skill does
# 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 do
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.