How to read your portfolio report — from 25 cells to decision

You have pressed 'Run deep analysis' in My portfolio. Seconds later a report is lying there: a color-coded 25-cell matrix, five wave profile bars, risk measures and a bias verdict. This is the guide to what everything means — and what you do with it.

Step 1: Read the bias verdict first

At the top: BULLISH, BEARISH or NEUTRAL BIAS. It summarizes 25 cells (5 theories × 5 horizons) weighted by how large each holding is. A real example from the system: a portfolio of Precise Biometrics and Volvo Cars gave BEARISH BIAS, 24/25 cells ▼ — the engine saw a correction on all five horizons for both companies. The same day, Saab B gave 21/25 ▲. Two portfolios, two completely different verdicts — from the same method, the same data sources.

Step 2: Which horizon is driving?

The bias can look different per row. A strong picture on long, weak on short = a matter of patience, not alarm. Split on medium = wait. Your SAVING HORIZON decides which row is yours. The course From stock to portfolio goes through all five windows in depth.

Step 3: The risk measures — fix them before you trade

• Weighted σ — the portfolio's aggregated volatility in %/year (the example above: 78 % — considerably riskier than OMX30's typical 15-20 %)
• Concentration — the largest holding's share; above 30 % one company steers your fortune
• Coverage — how large a share of the value has calculated analysis; uncontrolled capital is one of the risks in itself

Errors in the risk structure you fix before acting on signals — otherwise you trade on the right signal with the wrong structure.

Step 4: The per-stock view

Each holding is shown with price, σ, 52-week position and its own cell count. This is where you find the causes of the portfolio picture: a ▼-heavy portfolio can consist of one correcting large holding and three healthy small ones. Change one holding → run again → see the difference. That is systems thinking in practice.

Step 5: Write down what would change your picture

The routine that separates analysts from jumpers: note the thesis (three sentences max), which signals confirm it, and exactly what invalidates it. Review quarterly. Our calculator and annual report guide complement with a fundamental foundation.

The honesty in the numbers

Signals are calculated by the Python engine from price and volume data from independent sources (Yahoo Finance primarily, MarketStack secondarily). The theories — Elliott, Fibonacci, GANN, Lucas, volume — are heuristic structuring tools, reported as proxy measures, not proofs. Educational financial analysis, not investment advice. But every step is reviewable — and that is the whole point.

FAQ

What does the bias verdict in the portfolio report mean?

The bias verdict — BULLISH, BEARISH or NEUTRAL — summarizes the matrix's 25 cells (five theories × five time horizons) weighted by the size of each holding. BEARISH BIAS 24/25 ▼ means the engine sees correction signals in practically every combination across the holdings. The verdict is a summary of signals, not a trading order: it describes where the portfolio stands, not what you should do with it.

How do I interpret differences between the time horizons?

The bias can look different per row of the matrix. A strong picture on the long horizon with a weak one on the short is a question of patience rather than an alarm, while a split picture on the medium horizon argues for waiting. Your savings horizon determines which row is yours — which is why the report shows all five windows separately. The course From stock to portfolio covers the horizons in depth.

What is weighted volatility, and why is it shown?

Weighted σ is the portfolio's combined volatility in percent per year, calculated with regard to the size of the holdings. A portfolio at 78 percent swings considerably harder than the OMX30's typical 15–20 percent — that sets the frame for how large movements the portfolio is exposed to. Together with concentration (the largest holding's share) and coverage (the share of value with a calculated analysis), the measure describes the risk structure, which is worth addressing before acting on signals.

Can I trust the report's signals?

The signals are calculated by the Python engine from price and volume data at independent sources, but the theories — Elliott, Fibonacci, GANN, Lucas and volume — are heuristic structuring tools presented as proxy measures, not proof. Every step is auditable, and the routine in step 5 (write down the thesis, which signals confirm it and exactly what would invalidate it) lets you test the method against reality each quarter. That is how educational financial analysis works — structured grounding instead of ready-made answers.

This is educational financial analysis, not investment advice.