Cos'è Montbon Analytics?

Montbon Analytics è una piattaforma di analisi tecnica che combina onde di Elliott, Metodo Montbon e intelligenza artificiale per generare segnali di trading con score 0-100 e target di prezzo automatici.

Come funziona l'analisi con le onde di Elliott?

Il sistema identifica automaticamente i cicli di Elliott Wave su tutti i timeframe, calcola i livelli di Fibonacci, rileva divergenze e proietta i target di prezzo per ogni grado d'onda.

Cos'è il Metodo Montbon?

Il Metodo Montbon è un sistema proprietario di analisi tecnica che combina 4 pilastri: analisi tecnica classica, Elliott Wave, medie mobili multi-timeframe (MTF) e analisi fondamentale. Il risultato è uno score integrato 0-100 con livelli operativi automatici.

Sì, esiste un piano Free con 1 analisi gratuita senza login e 5 con account. I piani a pagamento partono da 1,99 EUR/mese (Basic, 30 analisi) fino a 24,99 EUR/mese (Elite, analisi illimitate).

I segnali di trading sono affidabili?

Montbon pubblica un track record verificabile di tutti i segnali generati, con win rate e rendimento medio storico consultabile nella sezione Track Record. I rendimenti passati non costituiscono un indicatore affidabile dei risultati futuri. Le analisi non costituiscono consulenza finanziaria.

Stock Scoring Model Example That Makes Sense

Most investors do not struggle because they lack data. They struggle because they have too much of it, and none of it agrees. A good stock scoring model example solves that problem by forcing different signals into one consistent framework you can actually use.

That matters more than it sounds. One chart can look strong while valuation looks stretched. Fundamentals may improve while momentum fades. News flow might be bullish, but risk-reward is poor at the current price. Without a scoring model, those inputs stay scattered. With one, you can rank, compare, and decide with less emotion.

What a stock scoring model example should actually do

A useful model is not built to predict the future with certainty. It is built to organize evidence. That means assigning weights to a small set of factors, translating those factors into numeric scores, and combining them into a final number that reflects current setup quality.

For most self-directed investors, the simplest structure is also the most practical. You want a score that answers one question fast: is this asset worth attention right now, not in theory, but at this price and under current conditions?

That is why the strongest models usually balance four areas: trend, momentum, fundamentals, and risk. Trend tells you whether price is aligned or fighting gravity. Momentum shows whether participation is improving or fading. Fundamentals help you avoid paying any price for a good story. Risk keeps the model honest, because even a strong asset can be a bad trade if downside is poorly defined.

A practical stock scoring model example

Let’s use a 0 to 100 framework. This keeps the output intuitive. Anything above 70 can be treated as a stronger candidate, 50 to 69 as watchlist territory, and below 50 as weak or high-risk. The exact thresholds depend on your style, but the logic stays the same.

Step 1: Choose the factor weights

A balanced starting point looks like this:

Trend: 30 points
Momentum: 25 points
Fundamentals: 25 points
Risk/position quality: 20 points

This weighting works well for swing traders and position traders because it does not let one pillar dominate the entire decision. If you are a shorter-term trader, you might give trend and momentum more weight. If you are a longer-term investor, you might shift more weight toward fundamentals.

Step 2: Define how each factor is scored

Trend can be measured with moving averages and market structure. For example, you might assign full points if the stock is above its 50-day and 200-day moving averages, the 50-day is above the 200-day, and price is making higher highs and higher lows. A mixed structure gets partial points. A broken trend gets few or none.

Momentum can be based on relative strength, volume confirmation, and recent rate of change. If the stock is outperforming its sector and benchmark while volume expands on up days, momentum deserves a higher score. If price rises on weak participation or momentum diverges, the score should drop.

Fundamentals should stay simple enough to compare across names. Revenue growth, earnings trend, margin quality, debt load, and valuation relative to growth are often enough. You are not trying to build a full investment bank model. You are trying to avoid obvious imbalance between price and business quality.

Risk/position quality is where many DIY models fail. This section should ask whether the setup offers a clear entry zone, logical stop loss, and attractive reward relative to risk. A stock with a strong chart but poor trade location should not get a top score.

Step 3: Score a real scenario

Imagine a stock with the following profile. Price is above the 50-day and 200-day moving averages. The 50-day is rising above the 200-day. The stock recently broke resistance and is holding above it. That would justify a strong trend score, say 26 out of 30.

Momentum is good but not perfect. Relative strength versus the S&P 500 has improved over the past month, and up days show better volume than down days. However, the last two sessions have slowed and short-term RSI is near overbought. That might earn 19 out of 25.

Fundamentally, revenue growth is solid, margins are improving, and debt is manageable. Valuation is not cheap, but it is not extreme given the growth rate. That could score 18 out of 25.

Now risk. Entry is slightly extended from the latest breakout zone, which means the stop would need to be wider unless price pulls back. If the nearest logical stop is 8 percent below current price and realistic upside to the next target is 12 percent, the trade still works, but it is not ideal. Give it 13 out of 20.

The total score is 76 out of 100.

That number tells you something useful immediately. This is not a random bullish opinion. It is a setup with strong alignment, decent quality, and some caution around timing. In a decision framework, that likely falls into an opportunity category, but not an automatic entry at any price.

Why this model works better than isolated indicators

Single indicators create false confidence. A moving average crossover may look clean while earnings quality weakens. A low valuation may look attractive while the trend remains clearly down. A stock scoring model example becomes valuable when it forces these conflicts into one visible result.

It also improves ranking. If you are tracking 30 stocks, you do not need 30 separate stories. You need a repeatable way to compare them. A stock scoring model turns comparison into process instead of intuition.

There is another benefit that matters in live markets: consistency. When your rules are written in advance, you are less likely to upgrade a weak setup just because the narrative is exciting. The model becomes a second opinion, not a replacement for judgment, but a disciplined filter against impulse.

Where most stock scoring models go wrong

The first mistake is using too many factors. If your model has 25 variables, you will spend more time maintaining it than using it. Worse, overlapping factors can double-count the same idea. Trend and momentum often overlap. So do valuation and quality. Keep each section distinct.

The second mistake is pretending every factor should matter equally in every market. That is rarely true. In risk-on phases, momentum and trend often deserve more influence. In uncertain or late-cycle environments, balance sheet quality and downside control may matter more. A static model is clean, but a rigid model can become blind.

The third mistake is treating the final score as a buy signal by itself. A score is a map, not a command. Two stocks with the same 74 can have very different profiles. One may be a strong business with weak timing. Another may be a fast technical setup with shallow fundamentals. Similar totals do not always mean similar trades.

How to adapt the model to your style

If you are a swing trader, shorten the lookback period and give more weight to trend acceleration, breakout structure, and risk-reward from current price. Fundamentals still matter, but mostly as a filter against low-quality names.

If you are a position trader, keep technicals in the model but increase the role of earnings consistency, margin stability, and valuation discipline. You do not need a perfect entry, but you do need a setup that can survive normal volatility.

If you trade ETFs or crypto, the fundamental block may need to change. For ETFs, composition, sector strength, and macro sensitivity matter more than corporate earnings. For crypto, market structure, trend persistence, volume, and volatility management often dominate. The framework stays useful, but the inputs must match the asset.

This is exactly why transparent scoring matters. A 0 to 100 score is only helpful when you can see what is driving it. Black-box outputs may look efficient, but they make it harder to trust the result when markets get messy.

From score to action

The best use of a scoring model is not to predict which stock will go up the most. It is to decide what deserves capital, what deserves monitoring, and what should be ignored for now.

A practical workflow is simple. Screen your universe, review the highest scores first, then confirm whether the setup still offers acceptable entry, stop, and target conditions. If the score is strong but price is too extended, move it to watchlist instead of forcing the trade. If the score is average but improving across multiple pillars, that may be more interesting than a higher score that is already fading.

This is where platforms like Montbon are useful when they combine multiple methods into one readable output rather than asking users to manually reconcile charts, moving averages, fundamentals, and risk levels across separate tools. The goal is not more complexity. The goal is faster clarity.

A stock scoring model does not remove uncertainty. Nothing does. What it can do is replace scattered signals with a structure you can test, refine, and trust under pressure. That alone makes better decisions more likely.