--- title: "1.1.1 Market participants and trading venues" description: "Who trades in a market and where an order is executed: exchanges, MTFs, dark pools, internalizers, CCPs and CSDs, measured on one session of data." url: "https://pythonparatrading.com/en/python-trading-course/level-1/market-participants-and-trading-venues/" lang: "en" image: "https://pythonparatrading.com/og/en/python-trading-course/level-1/market-participants-and-trading-venues.png" --- # 1.1.1 · Market participants and trading venues > Behind every price there is a chain of intermediaries and a dozen places where an order can be matched. This lesson puts them in order and measures them on a full session of a fictitious stock, trade by trade. - Course: [Level 1: Financial data, markets and analysis](https://pythonparatrading.com/en/python-trading-course/level-1/) - Module: 1.1 · How markets work - Duration: 45 min - Prerequisites: [0.5.3 · Selection, filtering, groupby, merge, pivot, resample and moving windows](https://pythonparatrading.com/en/python-trading-course/level-0/pandas-groupby-merge-resample-rolling/) ## By the end of this lesson - Place each market participant (retail, institutional, market maker, HFT, broker, clearing house) by horizon, size and the way they make money. - Tell an exchange, an MTF, a dark pool, an internalizer and OTC apart by their pre-trade and post-trade transparency. - Measure with pandas the split of volume by venue and the typical trade size of each one in a real data session. - Align trades with the prevailing quote without using later information (`merge_asof` with `allow_exact_matches=False`). ## An order passes through many hands When a retail investor taps “buy” in their broker's app, the order does not go straight “to the exchange”. It travels through a chain of intermediaries, and each one adds a function, a risk and a cost. Knowing the chain explains many of the odd things that show up in the data: trades at half-cent prices, volume that is not in the book, prices that arrive out of order. ```mermaid flowchart TB I[Investor] --> B[Broker] B --> S[SOR router] B --> SI[Internalizer] S --> R[Exchange] S --> M[MTF] S --> O[Dark pool] R --> C[CCP clearing house] M --> C O --> C C --> D[CSD depository] ``` - **Investor.** Decides what to buy or sell. It can be a person, a fund or a program. - **Broker.** The authorized firm that receives the order, checks that there is enough cash or collateral and sends it for execution. In the EU it is an investment firm or a bank; it is obliged to seek **best execution** for its client (price, cost, speed, likelihood of execution). - **Router (SOR, *smart order router*).** The broker's program that decides which venue to send the order to, or how to split it among several, depending on where the best price and liquidity are at that moment. - **Execution venues.** The places where the order is matched with someone else's: exchanges, MTFs, dark pools (all multilateral) or an internalizer (bilateral). The next section tells them apart. - **Clearing house (CCP, central counterparty).** After the trade it steps in between buyer and seller: it becomes the buyer to every seller and the seller to every buyer. If one party defaults, the clearing house performs. To cover that risk it requires collateral (margins) from its members. - **Central securities depository (CSD).** Keeps the record of who holds each security and delivers against payment: **settlement**, one or two business days later (lesson 1.1.5). ## Who is on the other side Every purchase has a sale on the other side. Knowing who is usually on the other side helps you understand why a market behaves the way it does. The figure at the top places the participants on two logarithmic axes: how long they hold a position and how large their orders are. These are orders of magnitude, not measurements, but the distances are real: between a market maker and a pension fund there are twelve orders of magnitude of horizon. | Participant | What they seek | Horizon | How they make (or lose) money | |---|---|---|---| | Retail investor | Return on their own money | Minutes to years | From the direction of the price; pays the spread and commissions | | Asset manager, pension fund | Track an index or beat it with a lot of capital | Months to years | From direction and selection; their problem is moving a lot of volume without moving the price | | Quant fund, CTA | Exploit statistical regularities | Days to months | From the signal net of costs; suffers when the regularity disappears | | Market maker | Quote bid and ask continuously | Seconds | From the spread; loses when trading against someone better informed (adverse selection) | | HFT and arbitrageur | Exploit price differences between venues or products | Microseconds to minutes | From speed and scale; a tiny margin multiplied by millions of trades | | Broker | Execute on behalf of the client | — | From commissions, interest and, sometimes, order flow | | Clearing house and depository | Guarantee and settle | — | From fees; their business is that nothing fails | Two consequences for a trading system. First: almost all immediate liquidity is provided by market makers who do **not** want to hold positions; if their risk rises (volatility, news), they pull their orders and the spread widens (lesson 1.1.3). Second: a low-frequency system competes on its horizon with asset managers and CTAs, not with HFT. Speed is neither its edge nor its problem; costs and look-ahead bias are. ## Where trading happens: exchanges, MTFs, dark pools and internalizers European regulation (MiFID II and MiFIR) recognizes three types of multilateral **trading venue**, where many buyers and sellers are matched with each other under non-discretionary rules, and one bilateral figure, the internalizer. Outside all of them lies OTC. | Type | Who is matched | Pre-trade transparency | Examples of use | |---|---|---|---| | Regulated market (exchange) | Third parties with each other, fixed rules | Lit book | Listed stocks, opening and closing auctions | | MTF | Third parties with each other, fixed rules | Lit book (or blind with a waiver) | Alternative platforms for European stocks | | OTF | Third parties, with **discretionary** execution | Depends on the instrument | Only bonds, derivatives, structured products and emission allowances | | Dark pool | Third parties with each other, no lit book | None, with a waiver | Large orders matched at the midpoint | | Systematic internalizer | The client against the intermediary's own account | Firm quotes published | Client orders of a large bank or broker | | OTC | Two parties, bespoke | None | Bespoke derivatives, negotiated blocks | In Europe a “dark pool” is not a separate legal type: it is a regulated market or an MTF that operates with a **pre-trade transparency waiver**, for example matching at the reference price (the midpoint of another venue) or matching large-in-scale orders. Since those trades do not contribute to price formation, regulation limits how much volume can be traded that way. In the US the names change (registered exchanges, ATSs for dark pools, *wholesalers* that internalize retail orders), but the structure is the same: lit books fragmented across many exchanges and a large share of the volume outside them. ## A NOVA session seen by venue The data for this lesson is a full session of **NOVA**, a fictitious stock listed in the US, on June 2, 2025: 50,946 trades with their price, size, aggressor side and execution venue. It comes from a book simulated order by order, so trades, quotes and book are consistent with each other. The data is synthetic, but it follows real conventions: a one-cent tick, a session from 9:30 to 16:00 New York time (13:30 to 20:00 UTC) and opening and closing auctions. ```python import pandas as pd op = pd.read_parquet("nova_operaciones.parquet") print(len(op), "trades") op.head(4) ``` ```output 50946 trades ts precio tamano agresor centro condicion 0 2025-06-02 13:30:00+00:00 53.35 33900 NaN PRINCIPAL subasta_apertura 1 2025-06-02 13:30:00.301051+00:00 53.34 100 venta PRINCIPAL regular 2 2025-06-02 13:30:00.301052+00:00 53.34 100 venta PRINCIPAL regular 3 2025-06-02 13:30:00.407366+00:00 53.34 159 venta PRINCIPAL regular ``` Each row is a trade. `agresor` says which side took the initiative: the order that arrived and was executed against one already waiting in the book. In the opening auction it is empty, because an auction has no aggressor (lesson 1.1.2). Grouping by venue: ```python por_centro = op.groupby("centro").agg( operaciones=("tamano", "size"), volumen=("tamano", "sum"), tamano_medio=("tamano", "mean"), ) total = por_centro["volumen"].sum() por_centro["cuota_pct"] = 100 * por_centro["volumen"] / total por_centro.sort_values("volumen", ascending=False).round(1) ``` ```output operaciones volumen tamano_medio cuota_pct centro PRINCIPAL 25776 3564476 138.3 40.1 OSCURO 4386 2358700 537.8 26.5 INTERNALIZADOR 3914 1243861 317.8 14.0 MTF_A 9640 986282 102.3 11.1 MTF_B 7230 735395 101.7 8.3 ``` *Fig. — NOVA volume by execution venue in the 2 June 2025 session.* Horizontal bars with the share of traded volume on each venue: primary 40.1%, dark 26.5% (highlighted), internaliser 14.0%, MTF A 11.1% and MTF B 8.3%. The primary venue, the one with the reference book and the auctions, gathers 40% of the volume. The two MTFs add almost another 20%. And a quarter of the volume is matched in a dark pool: nobody saw it in a book before it happened. NOVA's figures come from a simulation, but the pattern —no venue concentrates all the volume— is that of any liquid stock in the US or in Europe. The average size tells the other half of the story: *Fig. — Average NOVA trade size on each execution venue (2 June 2025).* Horizontal bars with the average trade size in shares: primary 138, dark 538 (highlighted, the largest), internaliser 318, MTF A 102 and MTF B 102. In the venues with a book, a typical trade is about 100 shares: orders executed against what sits at the first level, many of them slices of a larger order split by an algorithm (lesson 1.1.4). In the dark pool, the average size is five times larger. The reason is economic: a large order shown in a lit book moves the price before it is executed, because the others step aside or front-run it. Matching it blind at the midpoint avoids that. The `condicion` column separates the special trades: ```python op.pivot_table(index="centro", columns="condicion", values="tamano", aggfunc="sum", fill_value=0) ``` ```output condicion bloque regular subasta_apertura subasta_cierre centro INTERNALIZADOR 0 1243861 0 0 MTF_A 0 986282 0 0 MTF_B 0 735395 0 0 OSCURO 324900 2033800 0 0 PRINCIPAL 0 2645076 33900 885500 ``` Auctions only exist in the primary venue: the opening auction matches little volume and the closing one almost 900,000 shares at a single price, because index funds and many asset managers execute at the close, which is the price they are valued at (lesson 1.1.2). The blocks, all in the dark pool, are a few huge trades: ```python bloques = op[op["condicion"] == "bloque"] bloques[["ts", "precio", "tamano", "centro"]] ``` ```output ts precio tamano centro 5478 2025-06-02 13:50:31.017740+00:00 53.375 64100 OSCURO 24035 2025-06-02 16:22:35.166309+00:00 53.795 46100 OSCURO 33823 2025-06-02 17:54:43.496329+00:00 53.255 63000 OSCURO 37493 2025-06-02 18:28:54.725977+00:00 53.195 36800 OSCURO 41959 2025-06-02 19:05:40.906104+00:00 52.655 68200 OSCURO 47010 2025-06-02 19:40:27.200952+00:00 52.570 46700 OSCURO ``` Six trades that add up to 324,900 shares, 3.7% of the day's volume. A rule that filters “abnormal volume” without looking at the condition would take these blocks for signals; they are not: they are institutions moving positions through negotiated trades. ## What the dark pool does not show The dark pool has no visible book, but it does have a price rule: it matches at the **midpoint** of the best bid and the best ask of the lit book. Checking it requires joining each trade with the quote prevailing at that instant. The quotes (`nova_cotizaciones.parquet`) are the best bid and the best ask of the lit book, one row every time they change: ```python cot = pd.read_parquet("nova_cotizaciones.parquet") print(len(cot), "quote changes") # the prevailing quote is the last one BEFORE the trade con_cot = pd.merge_asof(op, cot, on="ts", allow_exact_matches=False) con_cot["medio"] = (con_cot["bid"] + con_cot["ask"]) / 2 con_cot.loc[1:3, ["ts", "precio", "agresor", "centro", "bid", "ask"]] ``` ```output 129247 quote changes ts precio agresor centro bid ask 1 2025-06-02 13:30:00.301051+00:00 53.34 venta PRINCIPAL 53.34 53.36 2 2025-06-02 13:30:00.301052+00:00 53.34 venta PRINCIPAL 53.34 53.36 3 2025-06-02 13:30:00.407366+00:00 53.34 venta PRINCIPAL 53.34 53.36 ``` `merge_asof` is a nearest-key *join*: for each trade it looks for the last quote with an earlier timestamp. The detail `allow_exact_matches=False` matters. When an aggressive order sweeps a level of the book, the quote changes **in the same microsecond** as the trade. If the exact match is allowed, that trade is joined with the quote it caused itself: information from the next instant. It is the look-ahead bias of lesson 0.1.1, now at microsecond scale: ```python def ejecuta_al_mejor_precio(df): con_libro = df[df["centro"].isin(["PRINCIPAL", "MTF_A", "MTF_B"]) & (df["condicion"] == "regular")] lado, precio = con_libro["agresor"], con_libro["precio"] compra = (lado == "compra") & (precio >= con_libro["ask"]) venta = (lado == "venta") & (precio <= con_libro["bid"]) return (compra | venta).mean() mal = pd.merge_asof(op, cot, on="ts") # allow_exact_matches=True print(f"With exact matches: {ejecuta_al_mejor_precio(mal):.2%}") print(f"Without them: {ejecuta_al_mejor_precio(con_cot):.2%}") ``` ```output With exact matches: 99.91% Without them: 100.00% ``` With the correct alignment, every aggressive buy pays the ask or more and every sell receives the bid or less, which is how a book works. With the wrong one, a few dozen trades seem to execute at a better price than was available. In an execution cost study (lesson 1.1.5) that error distorts the effective spread. With the quote properly aligned, the rule of the dark pool and that of the internalizer can be checked: ```python oscuro = con_cot[con_cot["centro"] == "OSCURO"] al_medio = (oscuro["precio"] - oscuro["medio"]).abs() < 1e-9 print(f"Dark crosses at the midpoint: {al_medio.mean():.1%}") inter = con_cot[con_cot["centro"] == "INTERNALIZADOR"] mejora = (inter["ask"] - inter["precio"]).where( inter["agresor"] == "compra", inter["precio"] - inter["bid"]) print(f"Internalizer with price improvement: {(mejora > 0).mean():.1%}") print(f"Average improvement: {100 * mejora.mean():.2f} cents per share") ``` ```output Dark crosses at the midpoint: 100.0% Internalizer with price improvement: 44.1% Average improvement: 0.24 cents per share ``` All the dark crosses are at the midpoint, as their rule says. The internalizer executes against its own account inside the spread and, in a little under half of the trades, improves on the price of the lit book: that is what it offers in exchange for keeping the order flow. For the client, half a cent of improvement on a $53 stock is little; for the internalizer, multiplied by millions of orders, it is a business. ```mermaid flowchart TB O[Client order] --> T{Is it large?} T -- yes --> OS[Dark pool or block] T -- no --> P{Price improvement?} P -- yes --> IN[Internalizer] P -- no --> SOR[Lit books] ``` The diagram simplifies the decision of the router and the broker. In practice it is made by algorithms with data from each venue, and the best execution obligation requires being able to justify it. ## Clearing and settlement The trade is only the beginning. Two steps follow that the investor barely sees and that explain some of the data: ```mermaid flowchart LR N[Trading] --> C[CCP clearing] --> L[CSD settlement] ``` - **Clearing.** The CCP computes each member's net positions (many buys and sells of the same security cancel out) and requires collateral for the outstanding risk. - **Settlement.** The CSD delivers the securities and the cash. In the US it happens on the next business day (T+1) since May 28, 2024; in the EU, two days later (T+2), with the move to T+1 scheduled for October 11, 2027. For a trading system, settlement matters in two places: the cash from a sale is not available until it settles (in a cash account), and the date that determines who receives a dividend is the settlement date, which is why the *ex* date exists (lessons 1.1.5 and 1.2.1). ## What this changes in your data - **Closing price ≠ last trade.** The official close is that of the primary venue's auction; later trades on other venues or after hours do not change it. - **The day's volume is split.** A provider that only gives the primary exchange's volume (some do) shows 40% of NOVA's real volume. Before comparing volumes, you need to know which tape each source uses (lesson 1.3.1). - **Not all trades are equal.** Auctions, blocks and after-hours trades carry condition codes. An indicator such as VWAP (lesson 1.7.1) gives different results with and without them. - **The book is not all the liquidity.** What you see at the first level is part of the real interest; the dark pool and the internalizers match without going through it. ## Exercises 1. Compute each venue's volume share using only the trades with condition `regular`, without auctions or blocks. Which venue gains weight and why? **Solution.** ```python regular = op[op["condicion"] == "regular"] por_centro_reg = regular.groupby("centro")["tamano"].sum() cuota = 100 * por_centro_reg / regular["tamano"].sum() cuota.sort_values(ascending=False).round(1) ``` ```output centro PRINCIPAL 34.6 OSCURO 26.6 INTERNALIZADOR 16.3 MTF_A 12.9 MTF_B 9.6 Name: tamano, dtype: float64 ``` Without the auctions, the primary venue loses more than five points of share: the closing auction, almost a million shares at a single price, only exists there. All the others gain weight, above all the internalizer. The share “of the primary venue” depends a lot on whether auctions are counted or not, so it is worth always stating which one it is. 2. Compare the mean and the median trade size of each venue. What does the difference between the two tell you in the dark pool? **Solution.** ```python op.groupby("centro")["tamano"].agg(["mean", "median"]).round(0) ``` ```output mean median centro INTERNALIZADOR 318.0 236.0 MTF_A 102.0 100.0 MTF_B 102.0 100.0 OSCURO 538.0 300.0 PRINCIPAL 138.0 100.0 ``` In the venues with a book, mean and median are similar: almost all trades are small. In the dark pool, the mean is much larger than the median: the distribution has a long tail of large trades (the blocks) that pull the mean up. To describe a venue's typical size, the median is more robust; to measure how much volume it moves, the sum. 3. A system buys 2,000 NOVA shares with a market order at 15:00 New York time. Explain on which venues it could be executed, who would probably be on the other side and which data from this lesson you would use to estimate its cost. **Solution.** The broker's router would send it to the lit books (primary venue and MTFs), where it would sweep one or several levels, or it would split it and look for liquidity in the dark pool and in an internalizer. On the other side there would mostly be market makers in the lit books, the internalizer itself if it takes the order, and perhaps an asset manager in the dark pool. To estimate the cost you would need the spread and the depth of the book at that time (`nova_libro_l2.parquet`, lesson 1.1.3) and the effective spread of similar trades (`nova_operaciones` with `nova_cotizaciones`, lesson 1.1.5). ## Self-assessment 1. A retail buy order is executed at $53.345 when the best bid is 53.34 and the best ask 53.35. Where was it most likely matched? - A. In the book of the primary exchange, against the ask - B. In the closing auction - C. In a dark pool, at the midpoint - D. It cannot have been executed at that price *Answer C: In a dark pool, at the midpoint.* A half-cent price, exactly at the midpoint of the spread, is the typical signature of a dark cross pegged to a reference price. In the lit book an aggressive buy pays the ask (53.35). 2. What distinguishes a systematic internalizer from an MTF? - A. The internalizer publishes nothing - B. The MTF only trades bonds - C. Nothing: they are two names for the same thing - D. The internalizer is an investment firm that executes client orders against its own account; the MTF is a multilateral system that matches third parties with each other *Answer D: The internalizer is an investment firm that executes client orders against its own account; the MTF is a multilateral system that matches third parties with each other.* The MTF (SMN in Spanish terminology) is a multilateral trading venue. The systematic internalizer is not a trading venue: it is the counterparty of its clients, with its own quoting and publication obligations. 3. What does the central counterparty (CCP) do after a trade? - A. It steps in between buyer and seller and guarantees settlement, requiring collateral from its members - B. It decides the price of the trade - C. It holds the end investor's securities - D. It sends the order to the venue with the best price *Answer A: It steps in between buyer and seller and guarantees settlement, requiring collateral from its members.* The CCP becomes the buyer to every seller and the seller to every buyer (novation) and manages the risk with margins. Custody and delivery of securities are handled by the central securities depository (CSD); routing, by the broker. 4. When joining each trade with the prevailing quote, why `allow_exact_matches=False`? - A. To make the code faster - B. Because a quote with the same timestamp as the trade may be the one that trade has just caused: using it would be information from the next instant - C. Because pandas does not support exact matches - D. To remove the auctions *Answer B: Because a quote with the same timestamp as the trade may be the one that trade has just caused: using it would be information from the next instant.* When an aggressive order sweeps a level, the book changes in the same microsecond. The “prevailing” quote for that trade is the previous one. In the NOVA session, with exact matches 0.1% of the trades against the book seem to execute outside the best price, and without them none do. 5. In the NOVA session the average volume per trade in the dark pool is about five times that of the MTFs. Why does that make sense? - A. Because only retail investors trade in the dark pool - B. Because large orders try not to show their size in a lit book, where they would move the price before being executed - C. Because the dark pool charges lower commissions per share - D. By chance *Answer B: Because large orders try not to show their size in a lit book, where they would move the price before being executed.* Whoever needs to move a large block loses money by announcing it: the others step aside or front-run it. That is why the dark pool concentrates large, institutional orders; in the NOVA session it holds the six blocks of more than 36,000 shares. ## Key points - An order goes through broker, router, execution venue, clearing house and depository. Each link has its function and its cost. - Participants differ by horizon and size: from the market maker that holds positions for seconds to the pension fund that holds them for years. - Trading is fragmented: in the NOVA session the primary venue only gathers 40% of the volume; a quarter is matched in a dark pool, at the midpoint and with no visible book. - The book you see is not all the liquidity: large orders, blocks and internalizers execute outside it. - To align trades and quotes you have to use the immediately preceding quote; the one at the same instant may be a consequence of the trade. ## Resources ### Research - [Limit Order Book - Aggregated Market Liquidity](https://pythonparatrading.com/en/limit-order-book-and-market-liquidity/) · Research-0008 · 11 min - [Quantitative Models and Algorithmic Execution](https://pythonparatrading.com/en/quantitative-models-algorithmic-execution/) · Research-0009 · 11 min ### Documentation - [CNMV (Spanish securities regulator): market structures under MiFID II (regulated markets, MTFs, OTFs and internalizers)](https://www.cnmv.es/portal/mifidii_mifir/mercados-estructuras) · cnmv.es - [ESMA: volume cap mechanism (from the double cap to the single cap since October 2025)](https://www.esma.europa.eu/node/200113) · esma.europa.eu - [pandas: merge_asof](https://pandas.pydata.org/docs/reference/api/pandas.merge_asof.html) · pandas.pydata.org ### Data - [Level 1 data: NOVA trades on June 2, 2025 (Parquet)](https://pythonparatrading.com/curso/datos/nivel-1/nova_operaciones.parquet) · Download · nova_operaciones.parquet Next lesson: [1.1.2 · Sessions, auctions, holidays and calendars](https://pythonparatrading.com/en/python-trading-course/level-1/trading-sessions-auctions-and-market-calendars/)