Trade-up contracts are one of CS2's most-misunderstood mechanics. Players treat them as either a slot machine for knives (consistently losing money over time) or as a guaranteed profit strategy (also consistently losing money over time). The reality is more nuanced: trade-up contracts have specific math, and that math produces positive expected value in narrow conditions and negative expected value in the broader range of typical use cases. Understanding when the math works is the difference between a thoughtful trading technique and a slow leak of capital into Valve's coffers.
Quick answer
CS2 trade-up contracts take 10 input skins of the same rarity tier and produce one output skin of the next-higher rarity tier. The output's specific identity is randomly selected from the collections contributing inputs, weighted by collection representation among the 10 inputs. Output float is calculated as the average of input floats, mapped through the output skin's wear range. Most random trade-ups have negative expected value — average output value is lower than total input cost plus opportunity cost. Positive EV trade-ups exist when (1) you concentrate inputs from collections where the output's highest-value skins significantly exceed the average, (2) you control input floats to maximize output float quality, and (3) you account for sticker premiums and other variables. Profitable trade-ups require research and capital; random trade-ups consistently lose money.
How does a trade-up contract actually work?
The mechanics:
Input requirements. Exactly 10 skins of the same rarity tier. All inputs must be from rarity tiers that have a trade-up output (Mil-Spec, Restricted, Classified, Covert). Consumer Grade and Industrial Grade trade-ups also exist but produce relatively low-value outputs.
Collection weighting. Each input contributes to the output collection probability proportional to its representation. If 7 inputs are from Collection A and 3 from Collection B, the output has a 70% chance of being from Collection A's next-rarity-tier pool and 30% chance of being from Collection B's pool.
Output selection within collection. Once the output collection is determined, the specific skin is randomly selected from the collection's skins at the output rarity tier, with equal probability among available options.
Output float calculation. The average of all 10 input floats is calculated. This average is treated as a raw float (0.00 to 1.00 value) and mapped through the output skin's specific wear range to produce the actual output float.
Output StatTrak. StatTrak inputs require all 10 inputs to be StatTrak. The output is automatically StatTrak in this case.
Stickers and applied items. Applied stickers on input skins are destroyed during the trade-up. The output skin doesn't carry over input stickers.
Why are most random trade-ups negative expected value?
The math works against random trade-up users:
Steam's market fees. Each input skin must be purchased through Steam Community Market or imported through trade-up. The 15% Steam fee creates a baseline disadvantage. Even if all 10 input prices match their market median exactly, the buyer paid 15% premium to acquire them through Steam.
Output value distribution. Collections typically have several output skins at the higher rarity tier with varying values. The "average" output value reflects this mixed pool. A trade-up targeting a collection with one $200 skin and five $20 skins has an average expected output value of around $50 — significantly less than if you'd been guaranteed the $200 skin.
Float mapping disadvantage. Low-float inputs (which are valuable) map to low-float outputs. But the output skin's wear range often limits how much "low float" actually means — if the output skin's minimum float is 0.06, even a perfect 0.00 average input float maps to a 0.06 output, which isn't impressive in terms of value premium.
Sticker destruction. If your input skins have applied stickers (especially valuable ones), those stickers are destroyed during the trade-up. The output skin doesn't carry the sticker value.
Time and opportunity cost. Capital tied up in trade-up inputs could be deployed elsewhere. The trade-up's expected return must exceed alternative uses to be net positive — which requires the absolute EV to be significantly positive, not just marginally so.
For a random trade-up using market-purchased inputs in standard quality, the cumulative effect of these factors is typically a 10–30% expected loss per trade-up cycle.
When do trade-ups become positive expected value?
Specific conditions can flip the EV math:
Collection arbitrage
Some collections have output pools where the highest-value skin is significantly above the average. If the collection has one Restricted skin trading at $300, three at $50 each, and one at $20, the average is around $94. If you can buy Mil-Spec inputs from this collection at favorable pricing (e.g., $5 each, totaling $50 for 10 inputs), the trade-up has positive EV — you're paying $50 for an expected output value of $94.
Collection arbitrage requires:
Identifying collections with favorable output pool distributions
Buying inputs at prices that make the math work
Sometimes waiting for market conditions to shift favorably
Doing the math precisely for current pricing
Float arbitrage
Specific scenarios where low input floats produce low output floats on skins where clean floats command significant premium. The AK-47 Vulcan, AWP Asiimov, and similar float-sensitive skins can produce trade-ups with positive EV when the output's low-float pricing significantly exceeds standard-float pricing.
Float arbitrage requires:
Sourcing low-float Mil-Spec or Restricted inputs at reasonable prices
Targeting outputs where low float commands premium
Understanding the output skin's wear range to predict actual output float
Sticker-applied input arbitrage
If you have low-value input skins with high-value applied stickers (perhaps acquired in bundle deals or through clever sourcing), the stickers get destroyed in the trade-up — but the input skin's float value is preserved. This can produce favorable cost basis if the input acquisition was driven by sticker value rather than skin value.
This is a niche scenario but legitimate when sourcing aligns.
StatTrak trade-ups
StatTrak trade-ups (requiring all StatTrak inputs) produce StatTrak outputs. The StatTrak premium on the output can create positive EV if input acquisition costs are favorable. The catch is that StatTrak inputs are typically more expensive, which often cancels the StatTrak output premium.
What's a concrete positive-EV trade-up example?
Specific examples shift with market conditions, but the structural pattern looks like this:
Hypothetical collection X has the following Restricted-tier output pool (next rarity above Mil-Spec):
Skin A: average market price $400 (FT condition)
Skin B: $80
Skin C: $60
Skin D: $50
Skin E: $40
Average expected output value: ($400 + $80 + $60 + $50 + $40) / 5 = $126
If 10 Mil-Spec inputs from this collection can be acquired at $8 each (total $80), the trade-up has positive expected value before any float considerations:
Cost: $80
Expected output: $126
Expected profit: $46 (57% return)
This is the structural pattern. Real-world execution requires:
Finding actual collections with favorable distributions (most don't)
Sourcing inputs at favorable prices (Steam Market pricing often makes inputs too expensive)
Accounting for Steam Market fees on purchases (15% surcharge)
Understanding the specific output pool, not relying on average
Accepting variance — even positive-EV trade-ups can produce low-value outputs in individual instances
Profitable trade-up specialists do this research routinely. Casual traders typically don't, which is why random trade-ups have a deserved reputation for losing money.
How does output float actually get calculated?
The exact formula:
Step 1: Sum the float values of all 10 inputs. Divide by 10. This produces the average input float (a value between 0.00 and 1.00).
Step 2: The average is treated as a "raw float." This raw value is mapped through the output skin's specific wear range using linear interpolation.
Step 3: The mapping formula: Output float = (Output skin's minimum float) + (Average input float × (Output skin's maximum float - Output skin's minimum float))
Example: 10 inputs with floats averaging 0.10. Output skin has wear range 0.00 to 0.50. Output float = 0.00 + (0.10 × 0.50) = 0.05. Result: Factory New output at 0.05 float.
Different example: 10 inputs with floats averaging 0.10. Output skin has wear range 0.06 to 0.80. Output float = 0.06 + (0.10 × 0.74) = 0.134. Result: Minimal Wear output (not Factory New) at 0.134.
This is why low-float inputs don't guarantee low-float outputs. The output skin's wear range determines how much "low float" actually translates into the visible result. Many trade-up targets have restricted wear ranges that prevent extreme low floats regardless of input quality.
What trade-up mistakes do most traders make?
Random target selection. "Trade-up for a knife" without specifying which output collection. The math almost never works on random knife trade-ups because input costs from current knife-collection Mil-Specs exceed average output values.
Ignoring collection composition. Using inputs from multiple collections without accounting for the proportional collection weighting in output selection.
Buying Steam Market inputs at full price. The 15% Steam fee makes most trade-ups negative EV from input acquisition alone. Sourcing inputs through third-party platforms at lower prices is essential for trade-up profitability.
Not verifying output wear range. Assuming low-float inputs produce low-float outputs without checking the output skin's actual wear range. Output skins with restricted ranges don't translate input float quality into output value.
Trade-up gambling psychology. Continuing to trade-up after losses, hoping variance will reverse. Variance over many trade-ups converges to expected value — if expected value is negative, more attempts means more losses, not regression toward profit.
Ignoring sticker destruction. Trading up input skins with valuable applied stickers destroys those stickers without compensation. Always scrape or sell valuable stickers before using their host skins as trade-up inputs.
How do I identify positive-EV trade-up opportunities?
The research workflow:
Step 1: Browse collection output pools. Steam Community Market and third-party platforms display each collection's skins by rarity tier. Identify collections where the output rarity has uneven value distribution (one or two high-value skins among lower-value options).
Step 2: Calculate expected average output value. Sum the current prices of all skins in the output pool, divide by number of skins. This is the random expected output value.
Step 3: Compare to input acquisition cost. If 10 inputs from the target collection can be acquired at total cost significantly below the expected average output, there's potential positive EV.
Step 4: Account for fees and overhead. Add Steam Market fees (15% if Steam purchased), opportunity cost of capital tied up, and time investment.
Step 5: Consider float optimization. If the target collection has high-value low-float outputs, calculate whether low-float inputs would produce favorable output floats given the wear range mapping.
Step 6: Execute or refine. If the math works, source inputs and execute. If the math is marginal, look for similar collections with better distributions or wait for market conditions to shift.
Trade-up specialists maintain ongoing analysis of which collections currently have favorable EV. The opportunities shift over time as input and output prices move. Static "best trade-up" lists are outdated quickly.
Are there tools that help with trade-up math?
Several community-maintained tools and platforms calculate trade-up EV in real-time:
Trade-up calculators that pull current market pricing from Steam and third-party platforms, calculate expected output values, and identify positive-EV opportunities. These tools democratize what used to be manual research.
The catch: opportunities surfaced by widely-used tools tend to be priced away quickly. If many traders see the same positive-EV opportunity, input prices rise (more demand for those specific inputs) and output prices fall (more supply from trade-ups), narrowing or eliminating the edge.
Serious trade-up specialists often build their own analysis frameworks or develop edges around specific collections that aren't widely tracked. The general pattern: easy-to-find positive-EV trade-ups don't stay positive for long.
Should I do trade-up contracts in 2026?
Honest assessment based on common scenarios:
Random trade-up for entertainment: fine, as long as you understand it's a slot machine with negative EV. Cost it like entertainment, not investment.
Targeted positive-EV trade-up after research: can be profitable. Requires real analysis effort and capital deployment. Expect moderate per-trade margins (10–30% positive EV in good cases) with variance — individual trades may produce below-average outcomes.
Bulk trade-up speculation across many cycles: requires significant capital and the ability to absorb variance. Top trade-up specialists run dozens or hundreds of cycles per month, smoothing out individual variance.
"Try to get a knife" strategy: almost certainly negative EV. Knife outputs from random knife-collection trade-ups have been studied extensively and don't typically produce profitable returns for casual users.
For most CS2 traders, trade-up contracts shouldn't be a primary strategy. They're a niche technique with positive EV in specific situations and negative EV in most random use cases. The right time investment is research before any trade-up, not high-volume execution hoping variance favors you.
Frequently asked questions
Frequently asked questions
Can I lose money on a single trade-up contract?
Are knife trade-ups worth it?
How does Steam calculate trade-up output stickers?
Can I cancel a trade-up contract?
What's the minimum cost to attempt a trade-up?
Do third-party trade-up sites have better odds than Steam's?
How do I learn to spot good trade-up opportunities?
Sources
- Steam Community Market — Skin and Collection Reference
- Steam Support — Trade-Up Contract Documentation
- CSFloat — Float and Pattern Reference for Trade-Up Inputs
- Skinport — Marketplace for Trade-Up Input Sourcing
- BUFF Market — Global Pricing Reference
- SkinSwap — Counterparty Platform
- Counter-Strike Official Website
