Author: Chen Xiaomeng
STANDX's "Points is Points" moment has arrived. It is not the first, and it will not be the last.
Preface: More points, but not necessarily more money
I wonder if everyone has noticed that over the past two years, points programs in crypto have become increasingly complex.
The situation now is completely different. Many projects have begun allocating points based on deposit size, borrowing amount, trading volume, LP liquidity, and holding time. Some projects encourage users to engage in recursive borrowing, amplifying 100,000 U of principal into several hundred thousand U of notional deposits; some projects can obtain dozens of times the points exposure through Pendle YT; and some trading platforms require users to continuously contribute tens of millions or even hundreds of millions in trading volume before they have a chance to enter the reward leaderboard.
These strategies do bring more opportunities to earn points, and they have also allowed many users with larger amounts of capital to find new sources of yield. The problem is that as participation thresholds rise, the funding costs and risks behind points also continue to increase.
So—how much real cost does earning points actually require, and in the end, what is the probability of converting those points into disposable cash returns? Therefore, this article aims to systematically organize the common points models in crypto and establish an anti-farming framework that can be directly used for investment judgment from several aspects: points valuation, funding cost, risk exposure, and exit mechanism.
I. Points Economics: Why do project teams increasingly like points?
Let us first understand the points mechanism from the perspective of project teams.
For projects that have not yet issued a token, points are a very efficient user incentive tool. Project teams can use points to attract liquidity, increase trading volume, and boost product activity, while deferring the settlement time of actual rewards to the future. For early-stage protocols, this can reduce the pressure of immediate cash or token payments and also help accumulate a user base before the product has matured.
For example, if a lending protocol wants to obtain 50 million USD in TVL in the short term, directly raising deposit rates would require bearing clear funding subsidy costs. If it uses points incentives, the project team can first attract funds and later decide the reward scale and distribution method based on the token economic model. For the project, fund growth can be immediately reflected in business data; for users, the final reward amount, redemption time, and actual value usually remain uncertain.
Project teams want to obtain more funds and users at the lowest possible incentive cost, while participants want to obtain more rewards at the lowest possible funding cost.
The two sides have shared interests at certain stages, but these interests cannot always remain aligned over the long term. Especially when market competition is fierce and projects continuously increase points multipliers, a large amount of capital may gather into the same protocol for rewards, while the protocol's own real business demand does not grow in sync.
If the final reward pool remains unchanged, the potential airdrop value you can obtain has in fact already been substantially diluted. This is also the first principle that must be understood in points investing: the key to determining the final reward is usually the effective points share, the size of the reward pool, and the redemption price. Simply increasing the number of points does not guarantee that returns will grow in sync.
II. The 10 common points models in crypto, and their respective anti-farming traps
Although points projects on the market come in many forms, from the perspective of capital input and sources of return, they can roughly be divided into ten types. The cost structures among different types vary greatly, so it is necessary to discuss them separately.
1. Interaction task type: fees and time are the main costs
Interaction-based points were the earliest popular farming model. Users earn points through operations such as swaps, cross-chain transfers, lending, NFT minting, and voting, and projects determine rewards based on the number of interactions, active days, cumulative transaction amount, or other behaviors.
The advantage of this type of project is that capital occupation is usually relatively low, and many tasks can be completed with only a small amount of funds. The disadvantage is that costs tend to be fragmented, especially in activities that require long-term sustained activity.
Suppose a project requires completing 50 interactions, with each interaction consuming an average of 0.5 U in gas. On-chain fees alone would require 25 U. If multiple cross-chain transfers, trading pairs with poor liquidity, or additional service fees are involved, the actual cost may continue to increase. For projects that require daily check-ins and monitoring of task progress, time input should also be considered.
Another risk worth noting for interaction-based points is eligibility review. Many projects screen abnormal accounts through trading patterns, address associations, funding sources, and operational behavior, so having already paid fees does not mean that rewards will definitely be obtained in the end.
For this type of project, I think it is already dead. It is better to get some bn wu accounts and farm alpha.
2. TVL deposit type: the opportunity cost of funds is most easily underestimated
TVL-based points have become one of the most common incentive methods for DeFi projects. Users deposit stablecoins or other assets into a protocol and earn points based on deposit size, duration, and acceleration multipliers. For users with larger amounts of capital, this model has the characteristics of simple operation and stable points accumulation. But it also easily leads participants to ignore the yield that the funds could originally have earned.
For example, a project offers a 3% base annual yield while also rewarding points. Suppose you have 100,000 U, and an alternative strategy with similar risk characteristics can provide a 7% annual yield. Then participating in points means giving up about 4,000 U in potential annual yield. If the funds are occupied for three months, the opportunity cost is close to 1,000 U (here you can refer to my fair annualized comparison).
Therefore, when participating in TVL points, it is necessary to compare base interest, potential airdrops, and alternative investment returns together. In addition, smart contract security, asset depegging, redemption restrictions, and capital concentration should all be included in the decision.
3. Lending points type: recursive amplification of points also amplifies risk
Lending points are usually divided into deposit rewards, borrowing rewards, and models that reward both sides at the same time. When a protocol allows users to redeposit borrowed assets, Loop appears, which is a recursive borrowing strategy.
Suppose a user has 100,000 U, and in this round can borrow up to 80% of the value of the collateral assets. If all borrowed funds are redeposited each time, and limits and fees are ignored, in theory this can form about 500,000 U in total collateral assets and 400,000 U in borrowing. If the project calculates points based on total deposit size, the notional points exposure could increase to 5 times the original. But at the same time, the user needs to bear the interest on 400,000 U of debt, as well as higher liquidation sensitivity.
Both the collateral and the debt are stablecoins, and depegging, interest rate changes, and oracle anomalies can still lead to losses. Recursive borrowing requires simultaneously calculating the net interest spread, borrowing rate fluctuations, the price relationship between collateral and debt, and changes in the health factor. For positions close to the liquidation line, a small amount of additional points may be far from enough to compensate for the potential risk.
4. LP market-making type: earning fees and points may still underperform simply holding the assets
Those who deposited in Fable understand this best.
LP points usually encourage users to provide liquidity to trading pools. Sources of return may include trading fees, liquidity incentives, and additional points, so the interface often shows a very attractive combined APR.
But the final return of LP is affected by changes in asset prices. Taking an ordinary ETH/USDC liquidity pool as an example, when ETH continues to rise, the pool will gradually reduce the amount of ETH held by the user and increase the proportion of USDC. Even if the user receives fees and points, the final asset value may still be lower than directly holding the initial ETH and USDC.
Therefore, when evaluating LP points, the benchmark for comparison must be clearly defined. For strategies whose underlying assets are ETH and USDC, directly holding the same initial assets is usually a meaningful reference. Only by combining fees, incentives, points, and the differences generated by asset rebalancing can the actual performance of the market-making strategy be judged.
5. Pendle YT type: the purchase cost of points needs to be calculated precisely
Pendle YT is a relatively representative points strategy tool in recent years.
In Pendle, yield-bearing assets can be split into PT, which represents principal rights, and YT, which represents rights to yield before maturity. After users purchase YT, they can obtain the yield rights for the corresponding period, as well as points and reward rights that comply with the relevant rules.
Because the price of YT is usually significantly lower than the notional value of the underlying asset it corresponds to, users can obtain larger points exposure with relatively less capital. This is also the reason the market often discusses points efficiency of more than ten times or even dozens of times. But YT has a very clear time-decay characteristic. As the maturity date approaches, the remaining yield period becomes shorter and shorter. After maturity, YT no longer has future yield rights, and its own value goes to zero. Previously qualified yield and rewards are handled according to specific rules.Therefore, when buying YT, the most important thing is to calculate the purchase price, expected claimable yield, points valuation, and remaining term. If you invest 2,000 U to buy YT, and before maturity you can only receive 120 U in underlying yield, then the remaining cost needs to be covered by points rewards.
For YT, I now only do free money and swing trading. Make me pay to buy points? Don't even think about it.
6. Perp volume type: the larger the scale, the more trading friction cannot be ignored
Perp platforms usually distribute points based on trading volume, holding time, order book liquidity, or trading activity. Some platforms also set up seasonal leaderboards to encourage users to continuously increase transaction scale.
This type of campaign can easily create huge numbers. An account may only have 10,000 U in margin, but through high-frequency trading and leverage it can cumulatively generate millions of U in trading volume. But trading volume cannot be directly converted into profit. Every trade may incur fees, slippage, and funding rates, and during the holding period you also have to bear market volatility and liquidation risk.
Let me put it this way: after var, perp can at most only be used for wealth management.
7. Spot trading type: costs are often hidden in spreads and price fluctuations
Spot points are similar to Perp points. Common rules include cumulative trading volume, volume for specified trading pairs, consecutive trading days, and token holding requirements. Spot trading does not have perpetual contract funding rates, but you still need to pay fees, bid-ask spreads, and execution slippage. For trading pairs with poor liquidity, the costs generated by repeated buying and selling can be quite substantial.
If the campaign requires long-term holding of a certain new token, then price fluctuations also need to be included in the final result. Even if you obtain a large number of points, the decline in the token itself may completely offset the potential airdrop.
For this type of project, the most efficient approach is usually to combine spot trades you originally planned to execute with points tasks, minimizing additional transactions generated purely to increase trading volume.
8. Social and content type: time and attention should also be priced
Social points include posting, interaction, content creation, community contribution, and various leaderboard activities. For users who are good at creation, research, and community operations, this type of project may have relatively high capital efficiency.
This is what we often call "mouth-farming."
Of course, content creation may also bring follower growth, research accumulation, network resources, and business cooperation opportunities, and these returns also have economic value. But when evaluating, one should distinguish between the long-term value generated by the content itself and the direct returns brought by project points.
For KOLs, I prefer to prioritize researching projects I already follow, and then treat points as an additional incentive. In this way, even without an airdrop, the research time and content invested may continue to generate value.
9. Referral and community type: points boosts and real cash income need to be calculated separately
Referral commissions, community partnerships, and points acceleration have become important growth methods for many projects. For participants who can organize users and capital, these mechanisms may bring additional returns.
For community operators, credit risk also needs to be considered additionally. If, in order to obtain higher commissions, you recommend projects with opaque risks to the community, then once problems arise in the underlying assets, the impact may exceed short-term partnership income.
Therefore, this type of activity should focus on partnership terms, reward attribution, payment timing, actual fulfillment conditions, and benefit disclosure to users.
10. Staking and lock-up type: high multipliers are often accompanied by liquidity restrictions
Staking, lock-up, and node-type points generally encourage users to hold assets for the long term by increasing the time weight. For example, locking for 30 days earns base points, locking for 90 days earns 2x points, and locking for 180 days earns 4x points.
This mechanism helps the project stabilize the scale of funds, but for users it means giving up part of their liquidity. If the underlying token falls sharply during the lock-up period, or if clearly better investment opportunities appear in the market, users may be unable to adjust their positions in time. If early exit also requires paying a penalty, the actual cost will further increase.
Especially for highly volatile tokens, lock-up points require evaluating token price risk, points value, and time cost at the same time. For users with larger principal, liquidity itself has important value, and the ability to exit an asset cannot be ignored just because the reward multiplier increases.
III. How much are points actually worth? Build a unified valuation model
After understanding the different types of points, the most critical issue to solve is valuation. Many people like to guess airdrop value through project financing amount, investors, and market hype. For example, if a project has a relatively large financing scale and the market expects FDV after TGE to reach 500 million USD, participants then believe the points have high value.
But FDV itself cannot directly determine an individual's airdrop amount. You also need to know how many tokens the project allocates to reward the corresponding points pool, what proportion your effective points account for in the entire points pool, and at what price the final tokens can be cashed out.
For projects that distribute linearly according to points share, the following basic model can be used:
Theoretical points value = FDV × points pool token ratio × individual effective points ÷ network-wide effective points
Here, the points pool token ratio is the proportion relative to the total token supply. If a project allocates a total of 10% of tokens for community incentives, but only 2% of that is used for the points pool you participate in, then 2% should be used in the calculation.
Assume a project ultimately has an FDV of 500 million USD, of which 2% of the token supply is allocated to a certain points pool, and you own 0.06% of the effective points in that pool. Then the theoretical reward value is:
500,000,000 × 2% × 0.06% = 6,000 USD.
This number already has reference significance, but unlock arrangements and actual trading prices still need to be considered. If the reward is subject to lock-up, phased release, or market sell pressure, the amount that can ultimately be cashed out may be lower than the theoretical valuation. A comprehensive realization coefficient can further be established to estimate the realizable value of rewards under different unlock and price scenarios. Assuming a comprehensive realization ratio of 50%, then the expected realized value of this points reward drops from 6,000 USD to 3,000 USD.
Next, consider points inflation. If the project continues to open points activities, and the total network-wide effective points grow to 3 times the original while your points remain unchanged, your individual share will drop to one third of the original. Even if the final FDV does not change, your theoretical reward will be only 2,000 USD; using the same 50% realization coefficient, the expected realized value further drops to 1,000 USD.
This shows that points valuation involves at least four core variables: final FDV, reward pool ratio, individual effective points share, and actual realization conditions.
If any one of these variables changes significantly, it may change the expected return of the entire strategy.
Why do I recommend building four scenarios at the same time?
Because future FDV and total points are both highly uncertain, a single valuation can easily lead to overly optimistic judgments. In actual evaluation, I recommend building at least four scenarios: optimistic, neutral, pessimistic, and zero airdrop.
For each scenario, calculate the final airdrop amount, and then compare it with the actual capital cost. If a strategy can only break even under the optimistic scenario, then it requires stricter position control.
IV. The real cost of getting farmed is often hidden outside the earnings dashboard
When analyzing points projects, one of the easiest mistakes is to count only explicit fees and ignore other capital costs.
A complete points investment needs to consider at least five parts: direct costs, opportunity costs, strategy gains and losses, liquidity costs, and tail risks.
Direct costs are relatively easy to understand, including gas, cross-chain fees, trading fees, slippage, and borrowing interest. Opportunity cost is the alternative return forgone by the capital during participation in points. Strategy gains and losses include factors such as LP rebalancing, YT price decay, and trading profit and loss. Liquidity costs mainly come from lock-ups, redemption queues, and early exit discounts, while tail risks involve contract vulnerabilities, asset depegging, liquidation, and protocol failures.
Among these, the first few categories can usually be quantified through transaction records and market data. Tail risks are relatively difficult to predict, but still need to be managed through position limits, risk scenarios, and diversified allocation.
To uniformly evaluate different projects, I recommend calculating two indicators at the same time:
Absolute net return = final net asset value + realized rewards − initial net investment − additional costs.
Relative excess return = actual net return of the points strategy − net return of a risk-comparable alternative strategy.
Note that borrowing strategies must first deduct outstanding liabilities, and gains and losses already included in the final asset value cannot be counted twice. For strategies involving LP or YT, calculation also needs to be based on the final realizable asset value, rather than directly using the initial principal.Here's a simple example: a points investment earned 1,000 U in three months, but an alternative strategy with a similar risk level over the same period could have earned 2,000 U. From the perspective of absolute return, the account is still profitable; from the perspective of capital allocation efficiency, this investment earned 1,000 U less in potential gains.
This is also why I have always recommended tracking the expected value of points separately from realized cash returns.
In addition, APR and APY also need to be distinguished. APR usually represents a simple annualized rate of return, while APY reflects the annualized result under specific compounding conditions. Different platforms may use different display conventions, and if base APY, token incentive APR, and unconfirmed points expectations are directly added together, it is very easy to overestimate actual returns.
V. 5 real-world cases: How exactly do points projects lead to reverse farming?
The previous discussion leaned toward mechanisms. Next, let's look at several specific cases to examine the break-even conditions of different strategies.
Case 1: 100,000 U deposited for three months, 500 U airdrop
Assume a certain points project offers a 3% base annualized rate, and the user deposits 100,000 U and holds it for 90 days. At the same time, there is an alternative strategy in the market with similar risk characteristics and a 7% annualized rate.
Calculated on a simple annualized basis and 365-day interest accrual, the base interest from the points project is about 740 U, while the return from the alternative strategy is about 1,726 U, a difference of about 986 U.
If participating in the points project also requires paying 100 U in operating fees, and the final airdrop value obtained is 500 U, then the results of the two approaches are as follows:
Although the points project ultimately earned 1,140 U, compared with the alternative strategy, it still earned about 586 U less.
Under this set of assumptions, the airdrop would need to reach about 1,086 U to cover the interest difference and operating fees. This is the opportunity cost break-even line for this strategy.
Case 2: Spending 2,000 U to buy YT, how much airdrop is needed to break even?
Assume the user spends 2,000 U to buy a certain YT with 90 days remaining until maturity, accumulates 120 U in underlying yield before maturity, and pays 30 U in fees during the period. If the YT is held all the way to maturity, its own residual value is zero, then the direct break-even condition of the strategy is:
2,000 − 120 + 30 = 1,910 U.
In other words, the final airdrop needs to reach 1,910 U to be enough to cover the purchase cost and fees. If the airdrop is only 1,000 U, the strategy will incur an actual loss of about 910 U. If we further consider other investment returns that this 2,000 U could have earned within 90 days, the economic break-even line will be even higher.
This type of calculation can help us judge the pricing of YT in the market. Even if the points multiplier is very high, if the purchase price already includes overly optimistic airdrop expectations, it may still fail to break even in the end.
Case 3: 100,000 U looped into 500,000 U, can points cover the negative spread?
Assume the user has 100,000 U and can, through an 80% looping borrowing ratio, theoretically form 500,000 U in collateral assets and 400,000 U in borrowings. The collateral assets have a 4% annualized rate, the borrowings have a 6% annualized rate, and it is assumed that interest rates remain unchanged over 90 days.
Then the interest generated by 500,000 U in collateral assets is about 4,932 U, and the interest expense corresponding to 400,000 U in borrowings is about 5,918 U. The final base spread return of the looped position is −986 U. In reality, there may also be constraints such as limits, the interest rate curve, and anti-looping points rules, so the theoretical 5x exposure may not necessarily be fully achievable.
The key point of this case is that after looping, the notional scale is very large, but the net principal equity is still only 100,000 U. While points efficiency increases, the balance sheet and liquidation risk also change significantly.
Case 4: Perp generated 10 million U in trading volume, what is the cost?
Assume a certain Perp platform distributes points based on trading volume. The user completes a cumulative 10 million U in trading volume within one season, with an average actual fee rate of 0.04%, an average execution slippage of 0.02%, and additionally pays 10,000 U net in funding rates.
Then fees are 4,000 U, slippage and other execution costs are 2,000 U, and adding funding rates, the total cost is 7,000 U.
If directional trading profit and loss are temporarily ignored, then the final points airdrop value needs to reach 7,000 U to cover these expenses. Assuming the actual airdrop is only 3,000 U, then the net loss is 4,000 U. If directional trading losses also occur during the volume farming process, the loss may continue to expand.
Therefore, for volume-based points, I believe it is necessary to establish an independent trading cost ledger, compare the cost per 1 million U in trading volume with the estimated airdrop value, and continuously update it based on fee tiers, maker ratio, and rebate conditions.
Case 5: LP shows 20% annualized, why can it still underperform simply holding?
Assume the user initially has 50,000 U of ETH and 50,000 U of USDC, with a total value of 100,000 U, and deposits both into an ordinary 50:50 constant product liquidity pool.
Over the next 90 days, the price of ETH doubles. If the initial assets were simply held, the final portfolio value would be 150,000 U. But for a constant product LP, ignoring trading fees and incentives, because the asset ratio automatically adjusts, the final value is about 141,421 U, about 8,579 U less than simply holding.
Now assume the LP additionally earns 20% simple annualized fees and incentive returns, calculated on the initial 100,000 U, which is about 4,932 U over 90 days. After adding this return, the final LP value is about 146,353 U, still lower than the 150,000 U from simply holding, a difference of about 3,647 U.
This case shows that LP annualized return and LP excess return relative to holding the coin need to be calculated separately. For highly volatile assets, the price path and range settings may have a significant impact on the result.
VI. Which points projects are more prone to reverse farming? Focus on seven signals
After analyzing the strategy itself, it is also necessary to judge whether the project has a reasonable economic foundation. The following signals deserve special attention.
First, long-term promotion of high-multiple points without a clear reward pool size. The points multiplier itself cannot explain dollar value. If the project has not published reward distribution, effective points rules, and redemption methods, it is difficult to judge how much reasonable cost is required to obtain these points.
Second, the points season is repeatedly extended, and total points continue to inflate. When a fixed reward pool faces continuously increasing capital, the potential value per unit of points may continue to decline. This is especially true when the project long delays its TGE, as early participants also need to bear additional capital occupation costs.
Third, points rules are frequently modified, and users lack clear expectations. This includes suddenly changing points weights, adding claim restrictions, adjusting snapshot dates, and adding lock-up requirements. Rule changes may have reasonable reasons, but participants must assess the impact of this uncertainty on final returns.
Fourth, business growth is highly dependent on subsidies. If the protocol mainly relies on high rewards to attract capital while underlying actual revenue is low, then it is necessary to focus on the subsidy budget, duration, and capital retention after it ends. After subsidies end, declining yields and liquidity outflow may occur at the same time.
Fifth, there is an obvious incentive disparity between ordinary users and special participants. For example, different user groups have different points weights, partnership rewards, and capital costs. These differences may be commercially reasonable, but they should be evaluated through public rules as much as possible. Ordinary users need to calculate returns based on the conditions they can actually obtain.
Sixth, valuation expectations rely excessively on market hype. When a points strategy must rely on an extremely high TGE FDV to break even, its expected return becomes extremely sensitive to market pricing. Project financing status and investment institutions can serve as references, but they cannot directly guarantee the price after the token is listed.
Seventh, the exit mechanism is complex, and capital liquidity is insufficient. This includes long redemption cooling-off periods, withdrawal queues, quota limits, lock-ups, and secondary market discounts. For users who need to adjust positions at any time, exit capability itself is an important investment condition.
VII. Build your own anti-reverse-farming system: From project screening to capital management
For people who participate in points projects over the long term, I think the most important thing is to establish a fixed evaluation process, to avoid relying on market sentiment anew every time they see a new project.
First, classify points projects by risk level
I am used to dividing projects into three categories based on the relationship between base yield and points expectations.The first category is more suitable for long-term capital allocation. The second category requires continuous tracking of the points economy and changes in alternative yields. The third category requires particularly strict position limits, especially in YT, Perp volume farming, and high-cost lock-up strategies.
For users with larger capital, this classification is especially meaningful. As capital size increases, the absolute amount of stable and reasonable yield also increases. Sacrificing a large amount of base yield to compete for uncertain points may lead to a decline in overall portfolio performance.
Second, all points strategies must have exit conditions determined in advance.
Many points campaigns go through season adjustments, rule changes, and extensions. If participants keep putting in capital simply because they have already accumulated a large number of points, they are very likely to be affected by sunk costs.
Therefore, before entering a project, you should clearly define how long you plan to participate, the maximum amount of capital you will commit, how much cost you can bear, and what circumstances would require reducing positions or exiting. For example, when the rate of points growth drops significantly, total points continue to inflate, borrowing rates rise, project subsidies end, or redemption conditions change, you need to recalculate the expected net return.
Costs already paid in the past cannot be recovered. The next decision should be based on the future returns that the remaining capital can generate. Even if you have already participated for several months, there is no need to keep paying unreasonable additional costs just to preserve existing points.
Finally, evaluate points within the entire investment portfolio.
Participating in ten projects at the same time does not necessarily mean you have achieved sufficient diversification. If all ten projects depend on the same stablecoin, lending asset, or cross-chain bridge, then the underlying risk may still be highly concentrated.
Particular attention should be paid to multi-layered DeFi structures. For example, depositing funds into one protocol to obtain an interest-bearing token, then using that token as collateral to borrow, and finally depositing the borrowed funds into another points project. Such a combination may indeed stack multiple sources of yield, but it also increases smart contract dependencies, asset conversion paths, and liquidation risk.
Therefore, a capital management table should at least record the principal invested in each project, current net equity, borrowing balance, base yield, points valuation, redemption period, and underlying protocol dependencies.
For points that have not yet been realized, it is best to set up a separate account and use conservative valuation. This makes it possible to more clearly distinguish between yield already earned and rewards that may be obtained in the future.
8. Use a single scorecard to quickly decide whether a points project is worth participating in.
To improve screening efficiency, the previous analysis can be converted into a simple scoring system.
Each dimension is rated from 0 to 100, and then a composite score is calculated according to weights. For the time being, a score above 80 can be regarded as a priority research range, 60 to 80 as a range requiring cautious participation, and below 60 as a reason to temporarily hold off on investing.
The purpose of this scoring system is to help establish a unified decision-making standard. The specific weights and scores are still subjective. For projects with major smart contract vulnerabilities, opaque custody, or obviously abnormal redemption mechanisms, independent risk veto conditions should also be set to prevent high expected returns from masking serious principal risk.
In addition to scoring, the expected return of points can also be estimated through scenario probabilities. Suppose that after participating in a points project, there are four possible outcomes:
If participating in this project requires a certain payment of 500 U in fees and there is no other yield, then the expected net return is −20 U.
These probabilities are entirely hypothetical and cannot represent the project's true future distribution. But this model has one obvious advantage: it requires participants to consider both low-return and zero-airdrop scenarios at the same time, avoiding the mistake of deciding capital allocation based only on the most optimistic outcome.
For higher-risk strategies, in addition to expected return, it is also necessary to separately examine the maximum loss under extreme scenarios. Because even if expected return is positive, a single large principal loss can still damage the long-term compounding of the entire portfolio.
9. From points speculation to long-term cash flow: the larger the capital, the more attention should be paid to underlying yield.
As I have spent more and more time participating in DeFi, I have noticed a very interesting phenomenon: many people are willing to spend a great deal of time studying points multipliers and potential airdrops, yet rarely seriously calculate how much cash flow their existing principal can generate each year.
For participants with smaller principal, low-cost interactions and small-scale points speculation can indeed be attractive. Because the absolute yield that the capital itself can generate is relatively limited, spending time to pursue high-multiple returns can sometimes significantly improve the overall outcome. But once capital reaches 100k, 300k, or even 500k U, the situation changes somewhat.
Suppose you have 300k U and can obtain 8% annualized return under acceptable risk conditions. Then in theory it can generate 24,000 U of yield in a year, or about 2,000 U per month on average. Of course, this needs to deduct actual fees, taxes, and potential losses, and the yield itself will also change with the market. A hypothetical annualized rate cannot be treated as a long-term guarantee. Even so, at this scale of capital, the base yield already has considerable economic value.
If, in order to participate in a certain points project, a large amount of capital is moved away from the original yield strategy, causing the base annualized rate to drop by 4 percentage points, then the annual opportunity cost corresponding to 300k U is 12,000 U. Even if the capital is occupied for only three months, it will still create a yield difference of about 3,000 U. This means that even if a points project ultimately distributes several thousand dollars in airdrops, the actual increase in net return may still be very limited.
In my view, a relatively ideal state is to first allocate most of the capital to base strategies that you fully understand and can continuously track in terms of risk, and then use a limited risk budget to participate in points investing. For protocols that themselves provide reasonable yield, points can be regarded as additional potential income; for strategies that are highly dependent on airdrops, position size should be determined according to actual costs and worst-case losses.
In addition, special attention should be paid to the situation where multiple points campaigns are stacked on the same capital. Some people like to deposit funds into one protocol to earn points, then put the receipt token into other protocols to continue earning rewards, and finally further amplify exposure through borrowing. Such strategies may have high capital efficiency when the market is stable, but once the underlying asset or a certain protocol runs into problems, risk may propagate along the entire capital chain.
For people who hope to build long-term cash flow through DeFi, the clearer the sources of yield, the easier the costs are to calculate, and the more controllable the exit conditions, the easier the entire portfolio is to manage.
Points are still worth studying, but they should be evaluated within a complete capital management framework.
10. Final anti-farming checklist.
When encountering a new points project in the future, I suggest answering at least the following ten questions before deciding whether to commit capital.
Are the points rules clear? Has the project clearly explained the use of points, the reward method, and eligibility conditions?
Can the reward scale be estimated? Do you know the allocation ratio of the corresponding points pool and the approximate scale of total network points?
Is the points share reasonable? Can you infer the potential airdrop based on your own effective points, rather than simply referring to the points multiplier?
What is the direct cost? Have gas, fees, slippage, borrowing interest, and other expenses been fully calculated?
What is the opportunity cost? How much yield could the same capital obtain in an alternative strategy with comparable risk?
What are the risks of the underlying asset? Is there depegging, price volatility, LP rebalancing, or YT value decay?
Is leverage used? If there is recursive borrowing, are the health factor, liquidation threshold, and interest rate changes within a controllable range?
Is exit convenient? Is there lock-up, redemption queuing, insufficient liquidity, or an early exit penalty?
Can you bear the worst outcome? If the airdrop ultimately has zero value, how much loss could the entire strategy generate?
When should you exit? Have you set in advance the maximum cost, target duration, and conditions for reassessment?
Conclusion: A mature points player should learn to give up some opportunities.
Looking back at the development of points mechanisms in crypto, an obvious change can be seen. Early participants mainly competed on time, information asymmetry, and interaction efficiency. Now more and more projects are beginning to compete on real capital, cost of capital, and risk-bearing capacity.
In the future, crypto will most likely see even more complex points gameplay, possibly involving new yield-bearing certificates, trading mechanisms, capital efficiency tools, and multi-protocol incentive combinations. The ways to earn points can keep changing, but the basic principles for evaluating capital returns always apply: focus on actual input, calculate all costs, estimate rewards that can actually be realized, and leave enough room for unfavorable outcomes.
I have always believed that once you have a certain amount of principal, the most worthwhile ability to cultivate is making reasonable trade-offs among various opportunities that all look attractive. Many times, participating in one less high-cost points project and keeping capital in yield strategies that are easier to understand and manage is itself an effective investment decision.
Points can improve the return of an investment portfolio, but every additional unit of yield needs to be evaluated together with the cost and risk taken to obtain it.
Guess what the next Point is Point project will be?