What is a bid price?
A bid price is the highest amount a buyer is prepared to pay for an asset at a given moment. In most markets you will see two quoted prices for a security: the bid (what buyers will pay) and the ask or offer (what sellers want). The gap between them is the bid-ask spread, which represents an immediate cost to traders who want to buy and then sell right away and is a primary source of income for intermediaries such as market makers.
Key definitions
– Bid price: the maximum price a buyer is willing to pay now.
– Ask (offer) price: the minimum price a seller is willing to accept now.
– Bid-ask spread: ask minus bid; the difference between the two quoted prices.
– Bid size: the quantity available to be sold to the bid at that bid price (e.g., number of shares).
– Market order: an instruction to buy or sell immediately at the best available price; a market buy executes at the ask, a market sell “hits the bid.”
– Limit order: an instruction to buy or sell at a specified price or better (e.g., a buy limit is placed at the bidder’s desired price).
– NBBO (National Best Bid and Offer): a consolidated display of the best (highest) bid and best (lowest) ask across all U.S. trading venues for a security.
How the bid functions in trading
– Quotes shown on tickers and trading platforms typically display the best bid (highest buyer price) and the best ask (lowest seller price).
– If you submit a market sell order, it will usually be filled at the current best bid; submitting a market buy will generally be filled at the current best ask.
– A limit buy order placed at a bid price will only execute if a seller agrees to that price or better. Traders use limit orders to try to get a better price than a market order would deliver.
– When many buyers increase their bids, prices can be driven up in a “bidding war,” which benefits the seller.
– Market makers and liquidity providers often post bids and asks continuously; their profit largely comes from capturing the spread.
Why bid size matters
Bid size shows how many units (shares, contracts, lots) are available at the bid price. Comparing bid size to ask size gives a quick read on near-term supply and demand and on how easily a trade of a given size can be executed without moving the price.
Practical checklist before placing an order
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– Check the current bid and ask prices and the spread. A wide spread increases likely transaction cost for a market order; a narrow spread favors immediate execution.
– Compare bid size to ask size (displayed size or total visible depth). If your intended trade size is larger than the visible size at the best bid/ask, expect partial fills or price movement as your order consumes multiple price levels.
– Decide order type: market order for immediacy (accepting the posted ask when buying or the posted bid when selling) or limit order to control execution price (may not fill).
– If using a limit order, set a limit price relative to the best bid (for sells) or best ask (for buys). Be explicit: a sell limit above the bid may not execute; a buy limit below the ask may not execute.
– Choose time-in-force (GTC = good‑til‑canceled, IOC = immediate-or-cancel, FOK = fill-or-kill). Match this to your trading objective: liquidity hunting vs. guaranteed fill size.
– Account for size and market impact. For orders larger than visible size, consider slicing the order, using pegged/iceberg orders (if available), or routing to dark pools to reduce visible market impact.
– Review recent trade prints and short-term price action (volume spikes, news). Fast-moving markets increase the risk of slippage.
– Check fees and rebates (per-share commissions, exchange fees, rebates for maker/taker). These affect net execution cost, especially for high-frequency or large-volume strategies.
– Consider use of limit with a small tolerance (e.g., limit relative to mid-price or a fixed number of ticks) rather than a pure market order when liquidity is thin.
– Confirm the order on the ticket before sending: symbol, buy/sell, quantity, order type, limit price (if any), time-in-force, and routing destination (if configurable).
Worked numeric examples
Example A — Small retail buy in liquid stock
– Best bid: $50.00 × 1,200 shares (bid size = 1,200).
– Best ask: $50.02 × 800 shares (ask size = 800).
– Spread = $0.02.
If you submit a market buy for 500 shares, you will likely execute at $50.02 (the best ask) and cost = 500 × $50.02 = $25,010. Market impact is minimal because ask size ≥ your order.
Example B — Market buy exceeding displayed liquidity
– Same book, but you submit a market buy for 1,500 shares.
– Execution: 800 shares at $50.02 (best ask), then remaining 700 shares hit the next available ask level (say $50.05 × 2,000).
– Weighted average price = [(800 × 50.02) + (700 × 50.05)] / 1,500
= (40,016 + 35,035) / 1,500 = 75,051 / 1,500 = $50.034.
– Slippage relative to best ask = $50.034 − $50.02 = $0.014 per share; total extra cost ≈ $21.
Example C — Sell order and “bidding war” risk
– Rapidly rising stock: best bid jumps from $49.90 to $50.10 within seconds because many buyers increased bids.
– If you placed a sell limit at $49.90, your order might execute earlier at that price (if contra liquidity existed) or get replaced by higher bids that lift the price later. Using a limit or watching the book helps you avoid selling before a bidding war completes.
Quick checklist (one-page)
1. Verify symbol, quantity, and order type.
2. Check best bid/ask and sizes; compare to your size.
3. Compute maximum acceptable slippage and set limit accordingly.
4. Choose time-in-force and routing.
5. Consider order-slicing for large size.
6. Confirm fees and clearing implications.
7. Send order and monitor fills; cancel/re-price if market moves against plan.
Common pitfalls
– Ignoring bid/ask size: assuming you’ll get the best price for a large order when visible size is small.
– Using market orders in illiquid stocks or around news/events — high slippage risk.
– Not accounting for hidden liquidity (iceberg orders or dark pools) which may reduce visible size but still supply liquidity.
– Confusing displayed size with total available liquidity — some venues show only part of the true depth.
How to practice (safe steps for students/retail traders)
– Use a simulator or paper-trading account with Level I and Level II data to observe how orders consume book levels.
– Start with small sizes relative to visible bid/ask sizes to learn execution behavior.
– Track realized execution price vs. mid-price and compute slippage statistics over multiple trades.
Summary
Check price and size on both sides of the book before acting. Match order type and time-in-force to your objective. For larger trades, plan how to access liquidity to
access available liquidity while limiting market impact: estimate how much of the displayed size you can reasonably take, break the order into smaller child orders, use liquidity-seeking algorithms or broker-assisted execution, and schedule the trade over time rather than hitting the market all at once.
Practical checklist for larger trades
– Estimate available liquidity: compare your order size to Average Daily Volume (ADV) and to visible sizes at the top levels of the book. As a rule of thumb, institutional traders often limit a single child order to a small percentage of ADV (e.g., 1–5%); retail traders should scale much smaller.
– Choose an execution method aligned with your goal:
– Minimize immediate market impact: use limit orders, time-sliced execution (TWAP/VWAP), or broker algos.
– Guarantee speed (at cost of possible worse price): use market orders but with small sizes.
– Time-in-force and order types: set Good-Til-Canceled (GTC), Immediate-or-Cancel (IOC), Fill-or-Kill (FOK), or price-limited orders consistent with your tolerance for partial fills and speed.
– Use venue options wisely: lit markets (visible book), dark pools, or crossing networks — understand differences in transparency, access, and potential adverse selection.
– Work with your broker or use smart-order routing if available; ask about fees, rebate structures, and execution algorithms.
– Monitor and control risk: set maximum acceptable slippage and use stop/limit rules to abort the strategy if costs exceed expectations.
Worked numeric example — slippage and implementation shortfall
Definitions:
– Mid-price = (Best Bid + Best Ask) / 2.
– Slippage (absolute) = Execution Price − Reference Price (often mid-price at order placement).
– Slippage (%) = Slippage / Reference Price × 100%.
– Implementation shortfall = Execution Price − Decision Price (plus explicit costs such as commissions).
Example:
– Best bid = $10.00, best ask = $10.10 → Mid-price = (10.00 + 10.10)/2 = $10.05.
– You place an order; execution fills at $10.12.
– Absolute slippage = 10.12 − 10.05 = $0.07.
– Slippage (%) = 0.07 / 10.05 × 100% ≈ 0.696% (about 0.70%).
– If your decision price (the price when you decided to trade) was $10.00, implementation shortfall = 10.12 − 10.00 = $0.12 per share (plus commissions and fees).
Post-trade analysis checklist
– Record: decision price, reference mid-price, execution price(s), sizes, timestamps, venue(s), fees.
– Compute: average execution price weighted by size, total slippage, slippage % per child order, and implementation shortfall including explicit costs.
– Compare to benchmarks: mid-price, VWAP (volume-weighted average price), and any target benchmark you set before the trade.
– Adjust future strategy: if slippage consistently exceeds limits, reduce child order size, extend execution horizon, or change algorithms/venues.
Common pitfalls to avoid
– Confusing displayed size with true available liquidity — iceberg orders and dark liquidity can hide depth.
– Using market orders for large size in thinly traded stocks — high risk of adverse price moves.
– Ignoring fees and rebate structures — they can flip the economics between venues.
– Failing to measure post-trade performance — without data you can’t improve execution.
Quick pre-trade checklist (1–2 minutes)
– Confirm security, ticker, and order size relative to ADV.
– Check top-of-book bid and ask and recent prints (Level I & II if possible).
– Choose order type and time-in-force; set max slippage threshold.
– Decide whether to use broker algos or manual slicing.
– Record decision price and benchmark.
Resources for deeper reading
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Resources for deeper reading –
– Investopedia — “Bid Price” (definition, examples, related terms): https://www.investopedia.com/terms/b/bidprice.asp
– U.S. Securities and Exchange Commission (Investor.gov) — “How Stocks Trade” (market structure primer, order routing): https://www.investor.gov/introduction-investing/investing-basics/how-stocks-trade
– Nasdaq — “Order Types” (limit vs. market, IOC/FOK, midpoint/pegged orders): https://www.nasdaq.com/solutions/order-types
– Cboe U.S. Equities — “Trading” (exchange mechanics, displayed/hidden liquidity, matching rules): https://www.cboe.com/us/equities/trading/
Quick post-trade checklist (5–15 minutes)
1. Gather fills and timestamps — export executed prices, sizes, and venue flags from your broker or OMS (order management system).
2. Compute execution benchmarks
– Arrival/decision price: price when you decided to trade (use midpoint or NBBO quote at that time).
– VWAP (volume-weighted average price) if comparing to intraday benchmark: VWAP = sum(price_i * volume_i) / sum(volume_i).
3. Calculate slippage metrics
– Implementation shortfall per share = Execution price − Decision (arrival) price.
– Total implementation shortfall = sum[(execution price_i − arrival price) * shares_i] + explicit costs (commissions, fees).
– Effective spread per share = 2 × (Execution price − prevailing midpoint at execution). (Positive for buys above midpoint; negative for sells below midpoint.)
4. Attribute causes
– Time of day, venue, visible depth, hidden orders, broker algo parameters.
– Note any market events, news, or irregular prints near fills.
5. Compare to tolerance
– Did total slippage exceed your pre-trade max slippage threshold? Record variance.
6. Log lessons and next steps
– Save settings used (order type, TIF, algos, broker), and recommend adjustments for future trades.
Worked numeric example
– Trade: buy 10,000 shares.
– Arrival midpoint (decision price) at 10:00 = $10.00.
– Executed average price = $10.05.
– Commission/fees = $0.005 per share ($0.005 × 10,000 = $50).
Calculations:
– Implementation shortfall per share = 10.05 − 10.00 = $0.05.
– Total implementation shortfall = $0.05 × 10,000 + $50 =
= Continued worked numeric example =
– Total implementation shortfall = $0.05 × 10,000 + $50 = $500 + $50 = $550.
Additional quick metrics from the example
– Implementation shortfall per share (excluding explicit costs) = $0.05.
– Commission per share = $50 / 10,000 = $0.005.
– Total cost per share (including commissions) = $0.05 + $0.005 = $0.055.
– Total cost as a percentage of arrival price = $0.055 / $10.00 = 0.0055 = 0.55% (55 basis points).
– Implementation shortfall as basis points excluding commissions = 0.05 / 10.00 = 0.005 = 0.5% = 50 bps.
Checklist: post-trade review (practical steps)
1. Record raw data
– Ticker, side (buy/sell), quantity, decision/arrival price, execution timestamps, executed prices and sizes, commissions/fees.
2. Compute core metrics
– Implementation shortfall per share = Avg execution price − Arrival price.
– Total IS = (Avg execution price − Arrival price) × Quantity + explicit costs.
– IS in bps = (Total cost per share / Arrival price) × 10,000 (basis points).
3. Attribute causes
– Market impact (did orders move the market?), delay cost (price moved before execution), missed liquidity (unfilled portion), venue effects (rebates/fees), hidden orders.
4. Compare to tolerance
– Was total slippage within your pre-trade max slippage threshold? Record variance.
5. Save settings and actions
– Order type, time-in-force, any algos used, broker, routing venue, and any special instructions.
6. Recommendations
– Note concrete changes to try next time (e.g., use limit vs market, slice order, change TIF, switch algo parameters, trade at a different time).
Common formulas (keep these handy)
– Implementation shortfall per share = Execution price − Arrival price.
– Total implementation shortfall = Σ[(Execution price − Arrival price) × Shares executed] + Explicit costs (commissions, fees).
– Cost per share (incl. fees) = Total implementation shortfall / Total shares.
– Basis points (bps) = (Cost per share / Arrival price) × 10,000.
Practical mitigation tactics (what to try next)
– Use limit orders to cap price risk; accept potential non-fill risk.
– Slice large orders (time-slicing or volume-weighted strategies) to reduce market impact.
– Use execution algorithms (VWAP, TWAP, POV) when appropriate; tune participation rate.
– Choose liquid trading hours and venues; avoid thinly traded periods.
– Monitor visible book depth and avoid executing aggressively through multiple price levels.
– Work with brokers who provide real-time fill analytics and venue routing transparency.
– Use post-trade analytics consistently to validate whether changes reduced cost.
Template for a simple trade-log entry (fields to capture)
– Date/time, ticker, buy/sell, quantity.
– Arrival/decision price and time.
– Average execution price, timestamp range, per-trade fills.
– Explicit costs (commissions/fees).
– IS per share, total IS, IS in bps.
– Order type, TIF, algo name and parameters, routing venue.
– Market notes (news, notable prints, unusual spreads).
– Action items for next trade.
Assumptions and caveats
– These calculations ignore taxes, funding/borrow costs, rebates, and opportunity costs from unexecuted portions.
– For partial fills, treat each fill separately if execution price varies significantly.
– Benchmarks matter: arrival price is one choice. Other useful benchmarks include VWAP (volume-weighted