There's already a piece on this site about valuation — reading a company's financial statements to judge what it's actually worth. That's the right toolkit for an investor thinking in years. Technical analysis is a different discipline built for a different timeframe: reading price and volume data to judge what's likely to happen next, over minutes, hours, or days rather than years. Quantitative and prop trading firms lean heavily on systematic, price-based signals for exactly this reason — though it's worth being clear that their actual edge is usually built on infrastructure, order flow data, and execution speed retail traders don't have access to, not simply reading the same indicators available on a free charting platform. The indicators below are a real, useful starting point regardless — just not a claim that they'll put you on equal footing with a trading desk.
Most of technical analysis is math applied to past price data, and platforms like TradingView compute it for you. The three tools below are a reasonable place to start, roughly in order of how much they depend on your own judgment to use well.
EMA: the trend, with a catch
The exponential moving average tracks an asset's average price over a chosen period, weighting recent prices more heavily than older ones — which is what separates it from a simple moving average, and why it reacts to new information a bit faster. Its most common use is trend identification: price consistently trading above its EMA is read as an uptrend, below as a downtrend.
Here's the catch, and it's a real one: the EMA is a lagging indicator. It's built entirely from past price, so it can tell you what the trend has been, never what it's about to do. Seeing that a stock has traded above its 20-day EMA for the past several sessions doesn't tell you anything about tomorrow — it tells you about yesterday. Treating "price is above the EMA" as a standalone buy signal is a common beginner mistake, because it says nothing about whether that trend is exhausted or just getting started.
Used well, the EMA is better as a filter than a trigger. A common approach pairs a fast and slow EMA — the 9 and 21, for instance — and treats a crossover as a trend-shift signal, ideally confirmed by volume rather than acted on alone. Another approach is EMA retracement: waiting for price to pull back to the EMA within an established trend and treating that pullback as a lower-risk entry in the trend's direction, on the logic that the EMA represents a level the market has recently treated as fair. Neither guarantees anything on its own — nothing does — but pairing the EMA with a second, independent signal (volume, RSI, VWAP) filters out a meaningful share of the false signals a single moving average throws off.
VWAP: the market's running scorecard for the day
VWAP — volume-weighted average price — is the average price an asset has traded at during the session so far, weighted by how much volume traded at each price. Unlike the EMA, which is usually read over days or weeks, VWAP resets every session and gives a real-time read on where the balance of the day's trading has actually occurred.
VWAP is generally treated as a strong estimate of intraday fair value, which is exactly why institutional execution algorithms are built around it: a fund working a large order will often specifically target VWAP over the session, on the logic that trading at or near it means neither overpaying nor underselling relative to everyone else's activity that day. That institutional behavior is part of why price so often gravitates back toward VWAP after moving away from it — a real mechanical reason, not just chart pattern folklore.
Standard deviation bands plotted around VWAP mark statistically typical ranges for the session. Price trading inside those bands on a given day suggests a session likely to stay range-bound, which is the basis for fading moves toward the outer bands and expecting a reversion back toward VWAP itself. It's a genuinely useful framework for a first intraday strategy, though it leans more on discretion — reading whether a given day looks range-bound or trending — than EMA crossovers do.
Volume Profile: where the volume actually happened
Volume Profile plots the session's traded volume along the price axis instead of the time axis, producing a horizontal histogram that shows exactly which prices did the most business. The single price with the most volume is the point of control (POC) — a level price tends to return to repeatedly through the session, since it represents where the most participants agreed on value.
The value area is the price range containing roughly 70% of the session's total volume — a standard definition worth knowing, since it's the range most platforms highlight by default. Within the profile, high-volume nodes (HVN) mark prices with heavy participation, and low-volume nodes (LVN) mark prices the market moved through quickly with little trading. HVNs tend to act as support and resistance because a lot of participants have a position and an opinion there; LVNs tend to see price move through them fast in either direction, since comparatively few orders sit there to slow it down.
The practical edge Volume Profile has over a simple swing high or low is that its levels come from actual traded volume, not just the shape of a candlestick chart. A swing high is just the highest price reached — it says nothing about how much size actually traded there. A high-volume node says exactly that.
Part 2 of this series covers RSI, session-based liquidity concepts sometimes called "killzones," and support and resistance — tools that lean more on pattern recognition and less on a hard statistical formula, which makes them more powerful in skilled hands and easier to misuse in inexperienced ones.