Price, time and volume: the only three primary inputs

Open any charting platform and you are offered several hundred indicators. It is easy to mistake that abundance for an abundance of information. It is not. For a cash equity or a spot currency pair, every one of those indicators is computed from three raw series: the price at which trades occurred, the moment each trade occurred, and the quantity that changed hands. Everything else — moving averages, RSI, Bollinger bands, Ichimoku clouds — is a function applied to those three. A function cannot add information that was not in its inputs.

This matters practically. If you know that an indicator is just an arithmetic transformation, you stop asking "what is the indicator telling me?" and start asking the better question: "what arrangement of price, time and volume produces this reading, and is that arrangement meaningful?" That question is answerable. The first one is not.

What a price actually is

A price is not a property of an instrument. It is the record of a completed transaction: one buyer, one seller, one quantity, one moment. Between transactions there is no price — there is a bid (the highest price someone is publicly willing to pay) and an ask (the lowest price someone is publicly willing to accept), and the gap between them is the spread.

This has three consequences that show up on every chart.

First, price is discrete. Exchanges enforce a minimum tick size, so a stock quoted with a five-rappen tick cannot print at 42.03. Apparent precision beyond the tick is an artifact of your software, not of the market.

Second, the "price" you see depends on which price you mean. Last traded, mid, bid and ask are four different numbers. For a liquid SMI constituent the difference is a rounding error. For a thinly traded Swiss small cap, or for an ETF outside its market maker's active hours, the spread can be wide enough that a chart of last-traded prices oscillates purely because trades alternate between hitting the bid and lifting the ask. That oscillation looks like volatility. It is mostly spread.

Third, price exists only where trades happened. When a Swiss company reports before the open and the stock closed at 48.00 and opens at 44.50, no one transacted at 46.20. The 3.50 move is real, but it is not tradeable territory — it is a hole in the record. Stop-loss orders placed inside that hole do not execute at the level you chose; they execute at the first available price on the other side.

Time is a sampling choice, not a market fact

The market does not produce bars. It produces a stream of trades. A bar is what you get when you slice that stream on a grid and record four numbers per slice: open, high, low, close. The grid is your decision, and it changes what you see.

A daily bar for a SIX-listed share covers continuous trading plus the opening and closing auctions. Continuous trading runs the Swiss business day and ends with a closing auction whose price becomes the official close — the number used for index calculation, fund valuation and margin. That is why the close is not just another tick: it is the price at which the largest concentration of size actually cleared, and it carries settlement consequences the intraday prints do not.

Two structural facts follow. Overnight and weekend, the instrument still has economic exposure but produces no bars, so gaps concentrate information into a single boundary between two candles. And equal-time bars sample activity very unevenly: the first and last thirty minutes of a Swiss trading day typically carry a large share of the day's volume, while the middle hours are quiet. A five-minute bar at 09:05 and one at 13:05 are labelled the same and represent very different amounts of trading.

Some practitioners respond by abandoning the clock: tick bars close after a fixed number of trades, volume bars after a fixed number of shares. These sample activity rather than time, which stabilises the statistical properties of the resulting series. The cost is that the bars no longer line up with anything else — news, sessions, other instruments — so comparison becomes harder.

Volume, and what it does not mean

Volume is the quantity traded in the interval. It is the most abused of the three inputs, so be precise about what it can and cannot say.

Every trade has a buyer and a seller of the same size. A day with 200,000 shares traded had 200,000 shares bought and 200,000 shares sold. "Buying volume exceeded selling volume" is arithmetically impossible as stated. What people usually mean is aggressor imbalance: more volume executed against the ask (buyer-initiated) than against the bid (seller-initiated). That is a real, measurable quantity — but it requires trade-and-quote data, not the volume bars in a standard chart package. If your platform shows one volume number per bar, you cannot infer aggressor direction from it.

What volume does measure is participation: how much capital had to change hands to produce the price path in that interval. A 3% move on ten times normal volume and a 3% move on a quarter of normal volume are different events, because they imply different amounts of inventory transferred. The volume lesson later in this course develops this properly.

Two data caveats for Swiss and European instruments. Volume is venue-specific: a stock that trades on several venues has several volume series, and your feed may show only one. Since the lapse of EU recognition of Swiss trading venues in 2019, trading in Swiss blue chips has been concentrated on SIX by Swiss regulation, which makes SIX volume unusually representative for Swiss shares — a convenience European equity traders do not enjoy. And for ETFs, on-exchange volume understates true activity, because creation and redemption in the primary market never prints on the tape at all. A low-volume ETF is not necessarily an illiquid one.

Everything else is a transformation — and you can prove it to yourself

Take a ten-day simple moving average. On day ten the closes are 100, 102, 101, 104, 106, 105, 108, 110, 109 and 112. They sum to 1,057, so the average is 105.7. On day eleven the close is 115 and the 100 drops out of the window: the new sum is 1,057 − 100 + 115 = 1,072, and the average is 107.2.

The average rose by exactly 1.5 while price rose by 3. That is not a coincidence and not a mystery. The change in an n-period simple moving average is always (new value − dropped value) ÷ n. Here (115 − 100) ÷ 10 = 1.5. The "lag" that everyone complains about is this formula. It also means the average can fall on a day price rises, if the value dropping out of the window is large enough — which is worth knowing before you interpret a downward-sloping average as selling pressure.

Volume-weighted average price behaves the same way. Suppose three trades: 10,000 at 30.00, 5,000 at 30.20 and 15,000 at 29.90. Traded value is 300,000 + 151,000 + 448,500 = 899,500 across 30,000 shares, so VWAP is 29.98. The simple average of the three prices is 30.03. The difference is entirely the weighting: the largest block traded at the lowest price. VWAP is not a signal, it is a size-weighted mean, and institutions reference it because their execution is measured against it.

What the three inputs exclude

Being clear about the boundary is as useful as knowing what is inside it.

The order book — resting bids and offers at each level — is a genuinely separate input, not derivable from trades. It is available on SIX and most futures venues, and it underpins depth-based analysis. Open interest is a fourth primary input for futures and options: it counts contracts outstanding, which distinguishes volume that opened new risk from volume that closed existing risk. Cash equities have no analogue.

And of course fundamentals, positioning surveys, flow data and policy are all outside the three. Technical analysis is not a claim that these do not matter. It is a claim that their effects arrive in price, time and volume, and that reading those three is sometimes faster than waiting for the underlying cause to be published.

Data hygiene, which quietly determines your results

Price series come adjusted or unadjusted. An unadjusted series shows a mechanical drop on the ex-dividend date; an adjusted series scales history back so returns are continuous. Both are correct for different purposes and mixing them corrupts backtests.

This is sharper in Switzerland than in most markets, for two reasons. Swiss dividends carry 35% federal withholding tax, refundable to Swiss residents through the tax return but not automatically — so the gross-dividend adjustment in a data feed does not match a private investor's cash experience. And the SMI itself is a price index: it excludes dividends by construction. Comparing a portfolio's total return against the SMI's price return is a systematic self-flattery of roughly the dividend yield per year. Use the total-return variant if you want an honest benchmark.

Finally, currency. A chart of a US-listed instrument in dollars is a chart of two things at once for a Swiss-resident holder: the asset and the USD/CHF rate. Deciding which of those you are analysing, before you draw anything, saves a great deal of confusion later.

What to take away

• Every indicator on a cash-equity chart is an arithmetic function of price, time and volume, so no indicator can contain information those three do not already carry.
• A bar is a sampling artifact: the open, high, low and close depend on where you chose to cut the stream, and equal-time bars sample very unequal amounts of trading activity.
• Volume measures participation, not direction — buyers and sellers are equal by construction, and aggressor imbalance requires trade-and-quote data your chart probably does not have.
• The change in an n-period simple moving average equals the new value minus the dropped value divided by n, which is the entire mechanism behind moving-average lag.
• For Swiss-resident readers, the SMI is a price index and Swiss dividends carry 35% withholding tax, so benchmark and adjustment choices materially change what your chart appears to say.

Sources

• SIX Swiss Exchange, trading rules and market model documentation (opening and closing auction mechanics, tick size regime).
• SIX Swiss Exchange Indices, SMI index rulebook (price index versus total-return variants).
• Swiss Federal Tax Administration, guidance on federal withholding tax (Verrechnungssteuer) on Swiss dividends.
• Larry Harris, Trading and Exchanges: Market Microstructure for Practitioners (Oxford University Press, 2003).
• Joel Hasbrouck, Empirical Market Microstructure (Oxford University Press, 2007).
• Marcos López de Prado, Advances in Financial Machine Learning (Wiley, 2018) — chapter on alternative bar sampling (tick, volume and dollar bars).
• Bank for International Settlements, Markets Committee reports on electronic trading and market liquidity.
• Andrew Lo and Jasmina Hasanhodzic, The Evolution of Technical Analysis (Bloomberg Press, 2010).