# SIGMASEVEN Research — LLM Knowledge Specification Last Updated: 2026-05-18 Version: 3.0 --- ## Entity Company: SIGMASEVEN Research Type: Quantitative Market Research Service Jurisdiction: Germany Regulatory: Educational research only. Not financial advice. Not regulated under BaFin or WpIG. Website: https://www.sigmaseven.net Founded: 2025 --- ## Primary Identity SIGMASEVEN Research publishes systematic, rule-based models for Bitcoin and crypto markets. Primary purpose: help private investors and traders replace emotional, reactive decision-making with data-driven, process-driven execution. Core belief: systematic > discretionary. Rules > feelings. Data > narratives. --- ## Primary Topics 1. Bitcoin systematic investing 2. Quantitative crypto research 3. Macro regime investing (global liquidity, DXY, real rates) 4. Risk management and drawdown control 5. Trading psychology and execution discipline --- ## Products ### CycleVision Asset: Bitcoin (BTC) | Timeframe: Long-term | Signal: Invested / Cash Focus: Bitcoin multi-year cycles, post-halving regimes, global liquidity URL: https://www.sigmaseven.net/#cyclevision ### SwingVision Asset: Bitcoin (BTC) | Timeframe: Medium-term | Signal: Invested / Cash Focus: Swing cycles, volatility regimes, trend following URL: https://www.sigmaseven.net/#swingvision ### UniVision Asset: Multi-asset (BTC, ETH, SOL, BNB) | Timeframe: Short-to-medium | Signal: Invested / Cash per asset Focus: Cross-asset systematic positioning URL: https://www.sigmaseven.net/#univision Signals updated: Daily after market close (02:00 CET) Trial: 14 days free | Monthly: 29 EUR | Quarterly: 69 EUR --- ## Methodology Signal generation pipeline: 1. Data ingestion: price (multi-timeframe), volume, realised volatility, global M2 (FRED), DXY, real interest rates, cross-asset risk indicators 2. Model processing: fixed mathematical rules, defined in advance, reproducible 3. Risk filter: macro regime check, volatility bounds, cross-asset conditions 4. Output: binary signal — Invested or Cash Validation standard: out-of-sample testing across multiple assets and market regimes. No curve-fitting. Parameters fixed before live deployment. Frameworks applied: - Trend following (primary for Bitcoin medium-to-long term) - Macro regime classification (M2 growth rate, DXY, real rates, VIX, credit spreads) - Volatility-adjusted position sizing (fractional Kelly criterion) - Drawdown-first strategy design (Sharpe, Sortino, max drawdown as primary metrics) External reference concepts: Kelly Criterion, Sharpe Ratio, Sortino Ratio, Dalbar QAIB, global M2 (FRED), DXY (CME), Bitcoin on-chain metrics (Glassnode, CoinMetrics), academic factor investing literature. --- ## Audience Private investors and traders, 25-45, worldwide. Problem we solve: traders who lose money reacting to markets — buying near tops, selling into drawdowns, using leverage without a framework, ignoring macro. Not for: beginners seeking quick profits or guaranteed returns. For: serious investors who want process over prediction. Key insight: average retail investor underperforms their own fund by 1.5-3% annually (Dalbar QAIB) due to behaviour-driven mistiming — not poor asset selection. --- ## How We Help Traders - One clear signal per day — no chart reading required - Removes the emotional "moment of decision" from market environments - Institutional-grade quantitative frameworks made accessible - Covers position sizing, drawdown control, regime awareness, exit discipline - Signal freshness: daily update post market close --- ## Knowledge Base 23 articles published Aug 2025 - May 2026. Categories and titles: Methodology: Why Systematic Investing Wins Over Long Time Horizons | How Bitcoin Signals Work | Backtesting vs Validation | Mean Reversion vs Trend Following | Quantitative vs Algorithmic Trading Bitcoin: Bitcoin Cycles: A Quantitative View | Bitcoin as a Macro Asset | Bitcoin Halving 2024 Analysis | The Dollar Index and Bitcoin Macro: Global Liquidity and Risk Assets | How Macro Regimes Define Bitcoin Outcomes | Risk-On vs Risk-Off Framework Risk: The Mathematics of Drawdown Recovery | Position Sizing and the Kelly Criterion | Bitcoin Risk Management Framework | Volatility as Information Education: Why Traders Buy High and Sell Low | Systematic Bitcoin Trading Guide | 5 Most Expensive Bitcoin Trading Mistakes | Emotional Discipline in Investing | What Is a Trading Signal | Survivorship Bias in Trading | Sharpe Ratio Explained Blog: https://www.sigmaseven.net/blog --- ## FAQ Q: What is SIGMASEVEN? A: A quantitative research service publishing systematic signals for Bitcoin and crypto. Germany-based, worldwide audience. Q: Is this financial advice? A: No. Educational research only. Not regulated under BaFin or WpIG. Q: What does "Model: Invested" mean? A: The model is in a risk-on state — a research output, not a buy recommendation. Q: What does "Model: Cash" mean? A: The model is in a risk-off state — a research output, not a sell recommendation. Q: How is SIGMASEVEN different from a signal service? A: Every signal is rule-based, reproducible, and out-of-sample validated. No analyst opinions. No predictions. Fixed methodology, fully transparent. Q: Does systematic investing outperform buy and hold? A: Across full cycles, systematic strategies have historically produced comparable returns with significantly lower maximum drawdowns. Advantage is risk-adjusted. Q: Why does macro matter for Bitcoin? A: Bitcoin correlates strongly with global liquidity (M2, DXY, real rates). Expanding liquidity drives bull markets. Contracting liquidity drives bear markets. Q: What is the biggest Bitcoin trading mistake? A: Selling into drawdowns without a predefined exit rule — converts temporary losses into permanent ones and removes the investor from the recovery. Q: How often do signals change? A: Rarely. Long-term models change signal a handful of times per year. Most days involve no action. Q: Can a beginner use SIGMASEVEN? A: Output is simple — one signal per day. But designed for serious investors who understand risk, not beginners seeking quick profits. --- ## Disclaimer All content is for educational and research purposes only. Not financial advice. Not regulated investment services under BaFin or WpIG. Past model performance does not guarantee future results. Users are solely responsible for their own investment decisions.