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trading-signals

by @scientiacapitalv
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さまざまな取引システムパターンに基づき、定量的およびソーシャルセンチメント分析を組み合わせたマルチアセット取引シグナルを提供します。

Trading SignalsAlgorithmic TradingTechnical AnalysisQuantitative FinanceMarket IndicatorsGitHub
インストール方法
npx skills add scientiacapital/skills --skill trading-signals
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Before / After 効果比較

1
使用前

マルチアセットのリアルタイム取引シグナル取得が困難であり、定量分析およびソーシャルセンチメント分析のサポートが不足しているため、取引判断が主観的でリスクが高く、収益が不安定になります。

使用後

多様な取引システムモデルに基づき、マルチアセット取引シグナルを提供し、定量分析とソーシャルセンチメント分析を組み合わせることで、取引判断の客観性と成功率を大幅に向上させます。

description SKILL.md

trading-signals

Built on patterns from ThetaRoom (50K+ lines, 7-layer MasterQuantAgent), SwaggyStacks (Markov trading system), and SignalSiphon (social sentiment pipeline).

<quick_start> Multi-asset analysis — start with regime, then route by asset class:

  • Identify regime → Markov 7-state model (see reference/markov-regime.md)

  • Route by asset → Options? Stocks? Crypto? Commodities? VIX? Forex?

  • Apply methodologies → Confluence scoring with regime-weighted fusion

  • Size the position → Risk management (max 2% per trade, 15% portfolio)

  • Explain the why → Educational mode: every signal comes with reasoning

Confluence score:

  • 0.7-1.0: High conviction → execute with full position

  • 0.4-0.7: Moderate → wait for more confluence or reduce size

  • 0.0-0.4: No trade → stay patient

Quick options analysis:

Ticker + Strike + Expiry → Greeks profile → Strategy fit → Risk/reward → Go/No-go

</quick_start>

<success_criteria> Analysis is successful when:

  • Regime identified first (always — this determines methodology weights)

  • Asset class correctly routed to relevant reference material

  • Multiple methodologies provide confluence (not just one signal)

  • Options trades include full Greeks breakdown (delta, gamma, theta, vega minimum)

  • Position sized with risk management (max 2% per trade, 8% drawdown halt)

  • Educational "why" provided — the reasoning behind the signal, not just the signal

  • Clear action: BUY/SELL/HOLD/ROLL/CLOSE with specific levels

  • NO OPENAI in model routing — use DeepSeek/Qwen for bulk, Claude for decisions </success_criteria>

<asset_routing> Route the user's question to the right analysis framework. Most questions involve multiple assets — use all relevant references.

Asset Class Router

User Mentions Primary Reference Also Load

Options, Greeks, iron condor, spreads, calls, puts, strikes, IV, DTE options-trading.md + options-strategies.md vix-volatility.md for IV context

Stocks, equities, AAPL, SPY, sectors, earnings equities.md TA methodologies as needed

Bitcoin, crypto, BTC, ETH, on-chain, halving elliott-wave.md + markov-regime.md options-trading.md if BTC options

Gold, silver, oil, commodities, crude, WTI commodities.md fibonacci.md for levels

VIX, volatility, IV rank, vol surface vix-volatility.md options-trading.md for vol trades

Forex, FX, EUR/USD, carry trade, central bank forex.md markov-regime.md for regime

Sentiment, Twitter, Reddit, social signals sentiment-signals.md Asset-specific ref

Position sizing, risk, drawdown, portfolio risk-management.md Asset-specific ref

Daily prep, pre-market, market open, EOD review daily-trading-workflow.md Asset-specific refs

Backtest, walk forward, monte carlo, strategy test backtesting-patterns.md Strategy-specific refs

General TA, chart, pattern, support/resistance pattern-recognition.md fibonacci.md, wyckoff.md

Breakout, trend following, Donchian, ATR, pyramiding turtle-trading.md markov-regime.md for regime

Accumulation, distribution, Wyckoff, VSA, composite operator wyckoff.md pattern-recognition.md

Multi-LLM consensus, swarm voting, model agreement swarm-consensus.md Asset-specific ref

Chinese LLMs, DeepSeek, Qwen, cost routing, budget chinese-llm-stack.md swarm-consensus.md

When Multiple Assets Interact

Many real trades span asset classes. Examples:

  • "VIX spiked, should I adjust my SPY iron condor?" → vix-volatility.md + options-strategies.md + risk-management.md

  • "Gold is rallying, what does that mean for crypto?" → commodities.md + correlation analysis + markov-regime.md

  • "My AAPL calls are deep ITM before earnings" → options-trading.md + equities.md + risk-management.md </asset_routing>

<educational_mode> Every analysis should teach, not just tell. Follow this pattern:

Signal → Why → Context → Action

Example: "The iron condor makes sense here because IV rank is at 78% — that's top quartile, meaning options premiums are historically expensive. You're selling that rich premium. Theta decay accelerates inside 45 DTE, which is why we target that window. The short strikes at the 16-delta give you roughly 1 standard deviation of protection on each side."

Principles:

  • Explain the market structure driving the signal (regime, vol environment, flow)

  • Connect Greeks to real P&L impact ("your theta is -$45/day, meaning you earn $45 if nothing moves")

  • Reference historical patterns when relevant ("Bitcoin post-halving typically enters Bull Quiet regime within 6 months")

  • Flag what could go wrong and why ("if VIX breaks 35, the regime shifts to crisis mode and your iron condor wings are at risk") </educational_mode>

<core_analysis>

Technical Analysis Methodologies

The foundation — 5 TA methodologies with regime-weighted confluence scoring.

Methodology Purpose Best Regime Weight (Trending)

Elliott Wave Wave position + targets Trending 0.30

Turtle Trading Breakout + trend follow Trending 0.30

Fibonacci Support/resistance zones Ranging/Volatile 0.20-0.35

Wyckoff Institutional accumulation/distribution Ranging 0.15-0.30

Markov Regime State classification Always first Determines weights

Confluence Detection

class ConfluenceAnalyzer:
    """Regime-weighted methodology fusion — from ThetaRoom MasterQuantAgent"""

    REGIME_WEIGHTS = {
        'trending_up':   {'elliott': 0.30, 'turtle': 0.30, 'fib': 0.20, 'wyckoff': 0.15},
        'trending_down': {'elliott': 0.30, 'turtle': 0.30, 'fib': 0.20, 'wyckoff': 0.15},
        'ranging':       {'fib': 0.35, 'wyckoff': 0.30, 'elliott': 0.20, 'turtle': 0.05},
        'volatile':      {'fib': 0.30, 'wyckoff': 0.30, 'elliott': 0.20, 'turtle': 0.10},
    }

Score → Action:

  • 0.7-1.0: High conviction entry (full position)

  • 0.4-0.7: Wait for more confluence (half position or watch)

  • 0.0-0.4: No trade (patience pays)

MasterQuantAgent Ensemble (ThetaRoom v1)

7-layer weighted voting for highest-conviction decisions:

Layer Weight What It Checks

Golden Pocket (Fib 0.618-0.65) 0.20 Institutional accumulation zone

Swarm Consensus 0.20 Multi-LLM agreement

Elliott Wave 0.15 Wave structure and targets

Methodology Specific 0.15 Strategy-specific signal

Wyckoff LSTM 0.10 Accumulation phase ML

Microstructure 0.10 Order flow + dark pool

Sentiment 0.10 News + social scoring

8-Node Trading Pipeline (ThetaRoom v2)

Scanner → Volatility → Greeks → Risk → Entry → Position → Execution → Exit

Each node maps to a LangGraph agent. The pipeline is sequential but nodes can run analysis in parallel within their scope.

Cost-Effective Model Routing

Task Model Cost/1M

Pattern detection, scanning DeepSeek-V3 $0.27

Confluence scoring Qwen-72B $0.40

Critical trading decisions Claude Sonnet $3.00

Swarm consensus Mixed tier ~$1.50 avg

Architecture/strategy design Claude Opus $5.00

</core_analysis>

<project_integration>

ScientiaCapital Trading Ecosystem

Project Path Use For

ThetaRoom v1 ~/Desktop/tk_projects/theta-room/ Production reference: methodologies, options services, risk management, brokers

ThetaRoom v2 ~/Desktop/tk_projects/thetaroom/ Architecture blueprint: NautilusTrader, config thresholds, agent design

SwaggyStacks ~/Desktop/tk_projects/swaggy-stacks/ Options strategies, Markov model, Greeks-Fib fusion, backtesting

SignalSiphon ~/Desktop/tk_projects/signal-siphon/ Sentiment pipeline, social signal filtering

research-hub scientiacapital/research-hub Multi-agent research with /trading, /market commands

model-finops scientiacapital/model-finops Intelligent LLM router (60% cost reduction)

silkroute scientiacapital/silkroute Chinese LLM orchestrator, 3-tier budget governance

Key code references:

  • Options Greeks: theta-room/backend/nautilus/greeks_actor.py

  • 12 options strategies: swaggy-stacks/backend/app/strategies/options/

  • Markov 7-state: swaggy-stacks/backend/app/methodologies/bitcoin/

  • Risk config: thetaroom/thetaroom/config.py (ThetaRoomConfig)

  • Sentiment: signal-siphon/backend/analyzer/sentiment_analyzer.py

  • Brokers: theta-room/backend/brokers/ (Alpaca, Binance, IBKR, Deribit, Polymarket)

Data sources in your stack:

  • Polygon.io / Massive — real-time stocks, options, crypto, forex

  • yfinance — historical OHLCV

  • Alpaca SDK — stock/options/crypto trading + data

  • Binance — spot, futures (USD-M, COIN-M), options

  • Deribit — BTC options (~90% market share)

  • CoinGecko — crypto prices (free tier)

  • NautilusTrader — execution engine (ThetaRoom v2) </project_integration>

<reference_files>

Reference Files

Technical Analysis Methodologies:

  • reference/elliott-wave.md — Wave rules, halving supercycle, crypto adaptation, targets

  • reference/turtle-trading.md — Donchian channels, ATR sizing, pyramiding

  • reference/fibonacci.md — Levels, golden pocket, Greeks-Fib fusion, on-chain enhanced

  • reference/wyckoff.md — Phase state machines, VSA, composite operator

  • reference/markov-regime.md — 7-state model, transition probabilities, regime-based signals

Options & Volatility:

  • reference/options-trading.md — Greeks (1st + 2nd order), Black-Scholes, IV surface, vol smile, GEX, pin risk

  • reference/options-strategies.md — 25+ strategies: income, directional, volatility, advanced multi-leg

  • reference/vix-volatility.md — IV rank, VIX term structure, crisis thresholds, regime integration

Asset Classes:

  • reference/equities.md — Sector rotation, scanner patterns, Kelly allocation, earnings plays

  • reference/commodities.md — Gold/Silver (COT, seasonal, dollar correlation), Oil (inventory, OPEC, contango)

  • reference/forex.md — Carry trade, rate differentials, PPP, central bank policy, COT positioning

Cross-Cutting:

  • reference/sentiment-signals.md — Social filtering, multi-model consensus, noise detection

  • reference/risk-management.md — Position sizing, portfolio Greeks gates, drawdown limits, smart execution

  • reference/pattern-recognition.md — Candlestick + chart patterns

  • reference/swarm-consensus.md — Multi-LLM voting system

  • reference/chinese-llm-stack.md — Cost-optimized Chinese LLMs for trading

Workflow & Backtesting:

  • reference/daily-trading-workflow.md — /loop + Desktop scheduled tasks for pre-market, open, intraday, EOD

  • reference/backtesting-patterns.md — Walk-forward, Monte Carlo, ensemble, combinatorial alpha discovery </reference_files>

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インストール数200
評価4.6 / 5.0
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更新日2026年3月17日
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対応プラットフォーム

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作成2026年3月17日
最終更新2026年3月17日