Bitcoin G-Score Strategy Analysis
Official monthly Baseline vs Risk-Based comparison first; weekly data below is supporting monitoring — not the same methodology.
dca_vs_risk_comparison.json (hero + Strategy Comparison card). Secondary / monitoring: weekly pipeline stats in weekly_backtesting_report.json — different schedule and rules; use for context, not as a second "official" headline return.For the same six-band multiplier table with today's headline G-Score and band, see the dashboard.
Official monthly SSOT comparison · /data/dca_vs_risk_comparison.json
🚀 Baseline DCA vs Risk-Based DCA
The published snapshot uses one monthly methodology: same execution dates for both strategies, bands from score + SSOT boundaries for risk-sized contributions, and the official six-band multipliers. This hero highlights the two official strategies — not value averaging (exploratory; see card below).
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How to follow the strategies
These are the rules used to build the published comparison snapshot on this page — how each strategy was actually tested here. This is general explanation only, not personalized financial advice. Wording for risk bands on your live dashboard today may not match every label stored in the historical data behind that snapshot.
Baseline DCA
The plain benchmark: not a G-Score band or a “buying signal” name from the dashboard — just fixed contributions.
- On each scheduled contribution date (the SSOT snapshot uses the first history row in each calendar month), invest the same dollar amount every time.
- Do that regardless of G-Score — no timing rule.
- Simple repeat: same amount, same rhythm; no band-based sizing.
Risk-Based DCA
- Start from a base recurring contribution on the same kind of schedule as the other strategies.
- On each date, use the official six-band framework (from your G-Score via SSOT boundaries in the backtest) to scale the base contribution — more in greener bands, less toward red, zero new buys in High Risk in the published multiplier table.
- Of the strategies on this page, this is the one most connected to GhostGauge's risk framework (bands and G-Score context).
Snapshot detail: Bands in the official run are derived from score + dashboard-config boundaries first; CSV band text is only a fallback. Multipliers match the SSOT product table in repo docs. See dca_vs_risk_comparison.json metadata for the exact wording.
Value averaging (exploratory snapshot)
- Instead of a fixed dollar amount each period, the backtest aims for a rising target path for portfolio value.
- On each scheduled date it invests only enough to close the gap between where you are and that target — so you may invest more when behind, and little or nothing when already ahead.
- That often means fewer trades and much less total cash put in than full DCA over the same calendar window.
- A higher percentage return does not automatically mean you earned more total dollars than with baseline DCA — you may have put far less money to work, so percent gain and dollar gain are easy to confuse.
- Why % can look very high: return % is measured on capital actually deployed. A strategy that deploys less can show a bigger percent than steady DCA without beating it on total profit in dollars. Check total invested and trade count in the Strategy Comparison table before comparing headline % alone.
Published numbers vs Strategy Tester
The comparison file and weekly backtesting report are built by the project's data pipeline. The Strategy Tester tab is a preview with mocked results for the UI — use it to click around, not as a second official run.