Independent Precision Protocol (IPP) is a blockchain‑based mechanism that redistributes capital between participants according to a deterministic function of realized trading volumes on an external exchange. It does not inject additional randomness into outcomes: there is no pseudo‑random generator, no hidden seed, and no internal oracle entropy. Instead, outcomes are fully determined by publicly observable market data and a fixed, transparent algorithm.
Economically, IPP belongs to the same formal class as futures and other zero‑sum derivatives: the aggregate gain of some participants equals the aggregate loss of others, net of explicitly defined fees. The underlying variable is not the price of an asset, but the realized trading volume of a chosen trading pair over short time windows after participant entry. This article presents the formal structure of IPP, its parameterization, and its risk properties, with particular emphasis on a systematic comparison to futures markets. For terminology and classification criteria, see Section 1.1.
In everyday language, the term "lottery" is often applied to any activity with uncertain outcomes and negative expected value after fees. From a scientific perspective this is too coarse: the same description would apply to most traded derivatives once transaction costs are taken into account. Empirical concentration of edge in a small fraction of participants (where a small minority achieves large gains while the majority incurs losses) is a generic property of zero‑sum competition with heterogeneous skill and information, not a defining feature of lotteries. This phenomenology is observed in most speculative derivatives markets, including leveraged futures, once transaction costs, funding, and price impact are taken into account.
In this article we reserve "lottery" for mechanisms in which outcomes are generated by an explicit randomization device (physical or algorithmic) that is independent of the economic processes participants may attempt to forecast, and in which participants have no levers beyond the size of their stake and, possibly, a choice among symmetric tickets.
Under this definition, both IPP and futures markets are not lotteries. In both cases outcomes are functions of endogenous market variables (price paths or volume profiles), and participants may attempt to obtain a systematic advantage through information, models, or execution. The appropriate classification for both is "zero‑sum derivative mechanisms with fees", differing not in kind but in which market variable they target and in how risk is bounded and operationalized.
If one insists on describing IPP as a "lottery" solely because most participants are unlikely to achieve positive long‑run P&L after fees, then the same label must apply, with even greater force, to leveraged futures and other speculative derivatives that combine uncertainty with leverage, complex liquidation rules, and opaque venue policies, especially when they are built on thin or easily manipulated markets. Any concern that volume‑based mechanisms on relatively illiquid pairs might be distorted by wash trading or other forms of manipulation applies at least as strongly to price‑based leveraged products on the same pairs, where engineered price moves can directly trigger liquidations, cascades, and amplified losses for ordinary participants.
This section provides a comprehensive, plain-language explanation of how IPP operates, from position creation to settlement, without requiring prior knowledge of blockchain technology or financial derivatives. The description covers the complete lifecycle of a position, the roles of each system component, and the deterministic mechanisms that ensure fairness and verifiability.
A new position is created on the blockchain by calling the createPosition function on the Base
contract. The creator specifies four core parameters that define the position's structure:
When a position is created, the Base contract deploys a new autonomous Position contract instance using the
minimal proxy pattern (EIP-1167). This cloned contract becomes the custodian for all participant funds and implements the
complete position logic. The position also has a fixed safety deadline T = 24 hours from creation. If
the position does not fill with B participants within this time, all deposits are automatically refunded to participants
who joined.
The total possible parameter combinations per trading pair is approximately 1.59 billion, allowing for a rich spectrum of risk-return profiles, from nearly symmetric allocations (C ≈ B/2) to highly selective ones (C ≪ B).
Other participants can join an open position by calling the join function on the Position contract and
depositing exactly A USDT (plus a small fixed payment fee of 0.10 USDT). The contract uses the ERC-20 transferFrom
mechanism to transfer USDT from the participant's wallet to the Position contract. Each join transaction is recorded on
the blockchain with a precise timestamp (in seconds, with millisecond precision available from block timestamps).
Participants can join in any order, and the position fills when the B-th participant joins. At this moment, the Position
contract emits a PositionFull event, which triggers the CAT bot to begin the volume calculation process.
The contract also records the firstJoinTimestamp (when the first participant joined) and
positionFullTimestamp (when the B-th participant joined), which define the time window for volume assignment.
Until the position is full and volumes are submitted, any participant can exit early by calling the
leave function. This returns their deposit A minus the payment fee (0.10 USDT). Early exit allows participants
to abandon positions that are not filling or that they no longer wish to participate in. Once volumes are submitted to the
contract, early exit is no longer possible, and the position proceeds to settlement.
The Position contract maintains an on-chain array of participants, each with their wallet address, join timestamp, and an on-chain index (0 to B−1) that determines the order in which volumes will be assigned. This index is assigned based on the order of joining, not the join timestamp, ensuring deterministic ordering for the volume assignment algorithm.
Once B participants have joined and the PositionFull event is emitted, an off-chain service called the
CAT bot (Contract Automation Tool) automatically begins processing the position. The CAT bot is a Python
application that runs continuously, monitoring the blockchain for events and performing deterministic volume calculations.
It is the only authorized entity (via the authorizedCat address in the Base contract) that can submit volume
results to positions.
The CAT bot performs the following sequence of operations:
getAllData() on the Position contract.
This returns all participant addresses, their exact join timestamps (in seconds), and their on-chain indices (0 to B−1).
The bot also retrieves the firstJoinTimestamp and positionFullTimestamp to define the time
window for volume data retrieval.firstJoinTimestamp − 60 seconds to positionFullTimestamp + 60 seconds (plus additional buffer
for iterative expansion if needed). The data is converted to a millisecond-precision dictionary mapping each timestamp
(in milliseconds) to the quote volume (in USDT) for that moment.volume_int = floor(volume_decimal × 106).submitResults(volumes[], winnerIndices[]) on
the Position contract. This transaction must be signed by the authorized CAT wallet and requires POL (Polygon's native
token) for gas fees.The volume assignment algorithm is deterministic: given the same join timestamps, the same on-chain indices, and the same Binance data, it will always produce the same volume assignments and the same winners. This makes the process fully auditable and reproducible by any third party with access to the same data sources. The algorithm's determinism is guaranteed by its use of fixed ordering (on-chain index), fixed search windows (60 seconds forward from join time), and fixed conversion rules (floor of decimal × 106).
If the CAT bot cannot assign unique volumes to all participants (e.g., due to insufficient trading volume in the time window
or too many duplicate volumes), the position is declared unresolvable. In this case, the bot calls
refundByVolumeError() on the Position contract, which refunds all deposits to participants. This ensures that
positions are never settled with incomplete or invalid data.
Once volumes are submitted to the smart contract via submitResults(), the settlement process is fully automatic
and irreversible. The Position contract performs the following calculations and transfers:
Pool = A × BLosersPool = A × (B − C) (the deposits from participants who did not win)F_prot = LosersPool × X, where X is the protocol fee rate (currently 5% or 0.05)WinnersPool = LosersPool − F_prot (the amount available for redistribution to winners)Payout_per_winner = WinnersPool / C. This is an equal split; the protocol does not currently support
weighted distributions based on volume magnitude.Π_win = A + (WinnersPool / C) − f_pay, where f_pay = 0.10 USDT.Π_lose = −A − f_join, where f_join is the
payment fee paid when joining (0.10 USDT).F_prot is transferred to the feeWallet address (set in the Base contract).catWallet address
(the CAT bot operator's wallet).
All transfers are executed atomically within the submitResults() transaction using the SafeERC20 library for
secure token transfers. The contract uses ReentrancyGuard to prevent reentrancy attacks. There is no manual intervention,
no discretion, and no possibility of changing outcomes after volumes are submitted. The Position contract marks itself as
closed and emits a PositionClosed event, which notifies the Base contract and any monitoring systems.
After settlement, winners can claim their payouts by calling the payout() function on the Position contract.
This transfers their winnings to their wallet. Losers have no payout to claim; their funds have already been redistributed.
The IPP design yields several fundamental properties that distinguish it from traditional derivatives and gambling mechanisms:
These properties combine to create a mechanism that is simultaneously speculative (zero-sum with fees, uncertain outcomes), transparent (fully auditable and deterministic), and bounded (fixed maximum loss per position, fixed time horizon). This combination is rare in traditional financial markets, where transparency and bounded risk are often sacrificed for flexibility and leverage.
The CAT (Contract Automation Tool) bot is a critical off-chain component that bridges real-world market data with on-chain smart contracts. It serves as a deterministic oracle that calculates and submits trading volumes for IPP positions. The bot is implemented in Python and runs continuously as a systemd service, monitoring the blockchain and processing positions as they become eligible for resolution.
The CAT bot is responsible for the following critical functions:
PositionCreated, ParticipantJoined, PositionFull, and other position lifecycle
events in real-time.submitResults() function, managing gas prices, transaction simulation, and confirmation handling.finalize() on expired positions that
have not been resolved, triggering automatic refunds to participants.
The bot operates continuously, processing positions as they become full. It is the only authorized entity
(via the authorizedCat address in the Base contract) that can submit volumes to positions. This authorization
is enforced on-chain, preventing unauthorized submissions and ensuring the integrity of the volume calculation process.
The CAT bot is implemented as a modular Python application with the following core components:
main.py: Entry point that initializes the system, performs bootstrap synchronization,
and starts all background workers (event subscriptions, resolution worker, finalization worker, periodic sync worker,
WebSocket collector, metrics worker).events.py: Handles blockchain event subscriptions via WebSocket, processing
PositionCreated and PositionFull events, triggering WebSocket collection for new positions,
and scheduling resolution tasks when positions become full.resolver.py: Core resolution orchestrator that processes filled positions, coordinates
volume calculation, performs safety checks, and submits results to smart contracts. Implements a priority queue system
for efficient processing of multiple positions.volume.py: Implements the deterministic volume assignment algorithm with millisecond
precision, ensuring all volumes are positive, unique, and assigned in the correct order (by on-chain index).binance_client.py: Fetches trading data from Binance REST API, converting kline data to
millisecond-precision time-volume dictionaries. Handles rate limiting, retries, and data normalization.binance_ws_collector.py: Maintains real-time WebSocket connections to Binance, collecting
aggregate trade data as positions are created and caching it in the database. Dramatically improves performance for
high-frequency symbols by eliminating repeated REST API calls during volume calculation iterations.chain.py: Provides Web3 interface for reading contract state (getAllData())
and submitting transactions (submitResults(), refundByVolumeError()). Handles RPC failover,
transaction simulation, and gas management.db.py: Database access layer for PostgreSQL, providing functions for storing and retrieving
position state, participant data, volume assignments, and audit trails. The database serves as a cache and historical
record, but smart contracts remain the source of truth.sync.py: Performs synchronization between on-chain contract state and off-chain database,
including bootstrap sync on startup and periodic sync operations to catch any missed events or state changes.finalizer.py: Handles timeout scenarios by scanning for expired positions that have not
been resolved and calling finalize() to trigger automatic refunds.metrics.py: Collects and reports system metrics for monitoring, including processing
times, success rates, queue sizes, and error counts.The complete workflow from position creation to resolution proceeds as follows:
PositionCreated event. The CAT bot's event subscription handler detects this event and:
ParticipantJoined
events. The bot tracks these events and updates the database, but does not take action until the position is full.PositionFull event. The bot's event handler:
getAllData() on the Position contract to retrieve the complete on-chain statefirstJoinTimestamp − 60 seconds to
positionFullTimestamp + buffersubmitResults(volumes[], winnerIndices[]) transactionPositionClosed event. The bot:
The core volume assignment algorithm is implemented in volume.py and operates with millisecond precision.
The algorithm ensures determinism, uniqueness, and fairness:
Input Data:
participants: List of Participant objects, each containing:
address: Wallet addressjoinTimestamp: Join timestamp in seconds (from blockchain)index_onchain: On-chain index (0 to B−1), determining processing ordertime_volume: Dictionary mapping timestamp in milliseconds to quote volume (Decimal) from Binance datamax_search_sec: Maximum search window in seconds (typically 60)Algorithm Steps:
index_onchain (0 to B−1) and that all
indices are unique. Verify that time_volume contains sufficient nonzero volumes.index_onchain, not join time):
join_ts_ms = joinTimestamp × 1000max_search_ms = max_search_sec × 1000 (typically 60,000 ms)dt_ms = 0dt_ms ≤ max_search_ms:
t_ms = join_ts_ms + dt_mstime_volume[t_ms]volume_int = floor(volume × 106)volume_int has not been assigned to any previous participant (checked against
global set of used volumes):volume_int to participant ivolume_int to the set of used volumesdt_ms by 1 (search with 1-millisecond precision)CannotAssignVolumeForParticipant exceptionindex_onchain)
and detailed assignment results for audit purposes.Key Properties:
index_onchain, not join time.
This ensures that earlier joiners (with lower indices) have priority in volume assignment, but the order is fixed
and not dependent on timing of algorithm executionIterative Window Expansion: If the initial search window (60 seconds) does not yield sufficient unique volumes, the resolution orchestrator expands the window iteratively. The expansion adds 30 seconds to the right boundary of the time window on each iteration, continuing until all volumes are assigned or a maximum number of iterations is reached, or the safe submission deadline is approached.
To optimize performance and reduce Binance API rate limit issues, the CAT bot implements a real-time WebSocket trade data collector. This system:
This system provides 8-10x faster volume calculations, especially for iterative expansions, and reduces Binance API calls by over 95% for high-frequency symbols. The cache ensures complete data coverage for positions from creation to resolution, while maintaining full determinism (same data produces same results).
Before submitting volumes to a smart contract, the CAT bot performs comprehensive safety checks:
closed == false)volumesSubmitted == false)index_onchain)resolveDeadline − SAFETY_GAP_SEC)submitResults() transaction to ensure it will
succeed on-chain before sending
If any check fails, the bot logs the error and does not submit the transaction. For unresolvable positions (insufficient
unique volumes), the bot calls refundByVolumeError() to refund all participants.
The CAT bot uses PostgreSQL as a cache and audit trail. Key tables include:
positions: Position metadata (A, B, C, symbol, deadlines, status, on-chain address)round_participants: Participant data (address, join timestamp, on-chain index, assigned
volume, winner status)binance_trades_cache: Cached trade data from Binance (symbol, timestamp_ms, quote_volume,
agg_trade_id) with indexes for fast retrievalbinance_ws_subscriptions: Active WebSocket subscriptions per trading pairvolume_oracle_proofs: Complete audit trail of volume assignments, including all inputs
and outputs for reproducibilityposition_logs: Log of all CAT bot actions (resolution attempts, submissions, errors)global_state: Last processed blockchain block number for event synchronizationThe database serves as a cache only; smart contracts remain the source of truth. The bot always verifies on-chain state before making decisions and synchronizes the database with on-chain data regularly.
The CAT bot handles various edge cases and error scenarios:
refundByVolumeError() to refund all
participants.finalize() to trigger automatic refunds.All errors are logged with detailed context, enabling debugging and audit trails. The bot never submits invalid data or bypasses safety checks, even in error scenarios.
The CAT bot is designed to be fully deterministic and reproducible:
getAllData())This determinism is a fundamental property of IPP, enabling trustless operation and full auditability without requiring trust in the CAT bot operator.
IPP is implemented as a system of two smart contracts deployed on the Polygon blockchain. These contracts are verified on PolygonScan, making their source code publicly auditable. The contracts use OpenZeppelin libraries for security (SafeERC20, ReentrancyGuard) and follow best practices for gas efficiency and security.
The Base.sol contract serves as a factory for creating positions and stores global protocol parameters.
It is deployed once and manages the entire protocol lifecycle.
Key Functions:
createPosition(A, B, C, symbol): Creates a new Position contract instance using the minimal proxy
pattern (EIP-1167). This clones a fixed Position implementation, ensuring all positions use the same immutable
logic. The function checks that parameters are within valid ranges and that no open position with the same
(A, B, C, symbol) combination exists.setFeeBps(newFeeBps): Allows the owner to decrease the protocol fee rate (cannot increase beyond 5%).
This one-way restriction protects participants from fee increases.setPaymentFeeUSDT(newFee): Allows the owner to decrease the payment fee (cannot increase beyond 0.10 USDT).setAuthorizedCat(newCat): Sets the address authorized to submit volumes to positions. Only this address
can call submitResults() on Position contracts.startDecommissioning(): Permanently disables creation of new positions. This is a one-way operation
that allows graceful protocol shutdown while existing positions continue to operate.Owner Capabilities and Restrictions:
Global Parameters:
feeWallet: Address that receives protocol fees (5% of losers' pool)catWallet: Address that receives payment fees (0.10 USDT per transaction)authorizedCat: Address authorized to submit volumes (must match CAT bot's wallet)feeBps: Protocol fee rate in basis points (currently 500 = 5%)paymentFeeUSDT: Fixed payment fee in USDT (currently 0.10 USDT, with 6 decimals)ALLOWED_USDT: Hardcoded USDT token address on Polygon (0xc2132D05D31c914a87C6611C10748AEb04B58e8F)POSITION_DURATION: Fixed at 24 hours (86,400 seconds)
Each position is implemented as a cloned instance of the Position.sol contract. The contract uses the minimal
proxy pattern (EIP-1167), which means all positions share the same immutable implementation code, but each has its own
storage. This ensures gas efficiency (deployment costs ~45,000 gas vs. ~2,000,000 for full deployment) while maintaining
immutability and security.
Key Functions:
join(): Allows a participant to join the position by depositing exactly A USDT. Records the participant's
address, join timestamp, and assigns an on-chain index (0 to B−1). Emits ParticipantJoined event. When
the B-th participant joins, emits PositionFull event.leave(): Allows a participant to exit early (before position is full and volumes are submitted). Returns
deposit A minus payment fee. Emits ParticipantLeft event.submitResults(volumes[], winnerIndices[]): Called only by the authorized CAT address. Submits volume
assignments and winner indices. The contract verifies that volumes are positive, unique, and that winner indices
are valid. Then it calculates payouts, transfers funds to winners, collects protocol fee, and marks the position as
closed. Emits VolumesSubmitted and PositionClosed events.payout(): Allows winners to claim their winnings after settlement. Transfers the winner's share (deposit
A + profit share − payment fee) to their wallet. Emits WinnersPaid event.finalize(): Can be called by anyone after the 24-hour deadline if the position has not been resolved.
Triggers automatic refunds to all participants (deposit A minus payment fee). This ensures capital cannot be locked
indefinitely if the CAT bot fails.refundByVolumeError(): Called by the authorized CAT address if volumes cannot be assigned (unresolvable
position). Refunds all participants.getAllData(): Returns complete position state in a single call, including all participants, timestamps,
volumes, status flags, and balances. This is the single source of truth for position state and is used by the CAT
bot for synchronization.Security Features:
submitResults() and
refundByVolumeError(). This is enforced via require(msg.sender == base.authorizedCat()).finalize() for refunds).Autonomous Operation:
Once a Position contract is created and initialized, it operates autonomously. The protocol owner cannot:
This autonomy ensures that participants' funds are protected from protocol owner actions and that position outcomes are determined solely by the on-chain logic and submitted volume data.
Both contracts are verified on PolygonScan, making their source code publicly visible and auditable. The contracts use standard Solidity patterns and OpenZeppelin libraries, which have been extensively audited by the community. The Position implementation is immutable after deployment (via the minimal proxy pattern), ensuring that the logic cannot be changed for existing positions.
Verification Process:
GPG Notarization: The contracts are also GPG-signed with fingerprint
3022A4B79A1E62BB6C201C412FBA74B1BF70E781, providing cryptographic proof of authorship and integrity.
IPP implements multiple layers of security to protect participant funds and ensure protocol integrity:
Smart Contract Security:
require(msg.sender == base.authorizedCat()) in the submitResults() function. The owner
cannot submit volumes or manipulate outcomes.Volume Assignment Security:
Data Source Security:
Operational Security:
finalize() to trigger refunds, preventing indefinite fund locking.Limitations and Residual Risks:
IPP organizes capital flows into discrete positions. Each position is an autonomous contract specifying: a per‑participant deposit, a finite set of participants, a subset of participants who will receive a positive outcome, and a maximum temporal horizon. Participants join a position by contributing a fixed amount of stablecoin (USDT); when the position is sufficiently populated, an off‑chain oracle evaluates realized trading volumes for a selected trading pair and computes deterministic outcomes.
Formally, for each position one can define a finite set of participants \( P = \{1, \dots, B\} \), a deposit amount \( A > 0 \), and an integer \( C \) with \( 1 \leq C \leq B-1 \). The total pool is \( A \cdot B \). After observing an external market data stream and on‑chain join times, the mechanism partitions \( P \) into a positive‑outcome set of size \( C \) and a negative‑outcome set of size \( B-C \). Transfers are then executed from the negative‑outcome group to the positive‑outcome group, minus a fixed protocol fee.
Each position is fully determined by four core parameters \( (A, B, C, T) \) and the choice of trading pair:
| Symbol | Meaning | Typical Range | Example |
|---|---|---|---|
| A | Deposit amount per participant (USDT) | 5 – 100,000 (step 5) | 100 |
| B | Total number of participants required | 2 – 200 | 10 |
| C | Number of positive‑outcome participants | 1 to B − 1 | 3 |
| T | Maximum lifetime of the position (safety horizon) | 24 hours (fixed) | 24 h |
The parameter T plays the role of a safety deadline rather than a holding requirement. Positions may fill and resolve in well under a second if demand is high; the 24‑hour bound only guarantees that capital cannot remain in a partially filled or non‑resolvable state indefinitely. If a position does not reach \( B \) participants or deterministic settlement cannot be achieved within \( T \), deposits are returned according to predefined rules.
For a fixed trading pair, the space of possible parameter quadruples is large: in the current configuration, there are approximately \( 1.59 \times 10^9 \) distinct combinations per pair. This allows for a rich spectrum of risk‑return profiles, from nearly symmetric allocations (\( C \approx B/2 \)) to highly selective ones (\( C \ll B \)).
IPP links outcomes to the realized trading volume of a chosen trading pair on a major exchange (currently Binance). For each participant, the mechanism considers a short interval immediately following the participant’s on‑chain entry and maps this interval to a unique, non‑zero volume value extracted from the external data stream.
Crucially, the external data are not used as a random number generator. They are market observables with known statistical structure: intraday seasonality, regime shifts, and responses to news can be studied and, in principle, modeled. The protocol treats these observables as inputs to a deterministic function, not as a source of entropy.
Let \( t_i \) denote the join timestamp of participant \( i \) and let \( V(\tau) \) denote the realized trading volume at time \( \tau \) for the chosen pair (with millisecond resolution). For each participant \( i \), the mechanism searches forward from \( t_i \) over a finite window (up to 60 seconds) to find the first volume value that is both strictly positive and whose integer representation has not yet been assigned to any previous participant. Denoting the original decimal volume by \( v_i^{\mathrm{dec}} \), the contract volume is \[ v_i = \Big\lfloor v_i^{\mathrm{dec}} \cdot 10^6 \Big\rfloor, \] and the algorithm enforces \( v_i > 0 \) and global uniqueness of all \( v_i \) within the position.
If no such unique, strictly positive volume can be found for some participant within the window, the position is declared unresolvable and deposits are refunded rather than forcing an artificial outcome. This guarantees that, whenever settlement occurs, the mapping from \(\{t_i\}\) and the external volume series \( V(\cdot) \) to the integer volumes \(\{v_i\}\) is well‑defined, strictly positive, and pairwise distinct.
Once each participant \( i \in P \) has an associated integer volume \( v_i \), which by construction is strictly positive and pairwise distinct across all participants, the mechanism constructs an index array \([0, 1, \dots, B-1]\) and sorts it in strictly decreasing order of \( v_i \). Because all contract volumes \( v_i \) differ, this single key already defines a complete strict ranking of all participants, and no further tie‑breaking criteria are required at the level of the protocol description.
The first \( C \) indices in this sorted list form the positive‑outcome set, while the remaining \( B-C \) indices correspond to the negative‑outcome set. Let the total pool be \[ \text{Pool} = A \cdot B, \quad \text{LosersPool} = A \cdot (B - C). \] A fixed proportion of the negative‑outcome pool (currently 5%) is collected as a protocol fee, and the remainder is redistributed among the positive‑outcome participants according to a predefined sharing rule (for example, an equal split).
At the level of transfers between participants, and abstracting from explicitly defined protocol and transaction fees and possible rounding effects, the aggregate profit of the positive‑outcome set equals the aggregate loss of the negative‑outcome set. In this sense, the mechanism realizes a zero‑sum transfer conditioned on the observed market volumes and participation times.
For any individual position, the maximum capital at risk for a participant is known ex ante and equal to the deposit \( A \), plus small fixed transactional fees associated with optional actions such as early exit and payout. There is no leverage, no margin, and no possibility of losses exceeding the initial position size. In the worst case, a participant loses at most \( A \) in a given position; there is no mechanism by which a position can expand or "grow" beyond its initial stake.
This contrasts sharply with leveraged futures, where path‑dependent liquidations can generate losses disproportionate to the initial margin, and where the effective loss bound is a stochastic function of the price path, margin rules, and venue‑specific liquidation procedures.
Until a position is irreversibly locked for settlement, a participant may request to withdraw from the position:
The early‑exit option serves two distinct purposes:
1. Protection against "dead" configurations.
If a particular configuration \((A,B,C)\) fails to attract sufficient interest (e.g., a niche trading pair or an asymmetric risk profile that other participants find unappealing), a participant is not forced to keep capital immobilized indefinitely. The position can be abandoned before it reaches \( B \) entries or the safety deadline \( T \).
2. Correction of subjective misjudgment.
Participants may revise their view of the market, their own risk tolerance, or the attractiveness of a given position. Early exit allows them to fully reverse a decision that, in hindsight, they no longer endorse.
The mechanism thereby avoids the classic "point of no return" that characterizes many gambling setups, where bets become irrevocable the moment they are placed. In IPP, exposure remains under participant control until a well‑defined settlement phase begins. Once the position is filled (\( B \) participants) and the settlement process is initiated, the risk profile and potential payoff for each participant are fixed.
The parameter \( T = 24 \) hours serves as a hard upper bound on the lifetime of a position:
In normal operation, positions are expected to fill and resolve on timescales orders of magnitude shorter than \( T \). The mapping from join times to volume values typically occurs within seconds, and settlement once \( B \) is reached can be performed within a bounded technical delay (for example, on the order of \( 1 \)–\( 60 \) seconds, depending on the oracle's polling configuration and blockchain confirmation times).
It is therefore important to distinguish:
The former is short and operational; the latter is a guardrail against infrastructure failures and forgotten positions.
Like any zero‑sum market mechanism, IPP admits heterogeneous participant types: some may have faster access to data, superior models of short‑horizon volume dynamics, or better execution infrastructure. The same is true, a fortiori, in futures markets, where high‑frequency traders and market‑makers exploit microstructure patterns and latency advantages.
These asymmetries are not unique to IPP and do not transform the protocol into a lottery (as defined in Section 1.1). They describe the competitive landscape within which strategies interact. IPP's contribution is that it makes the payoff function, risk bounds, and data dependence explicit and auditable, so that any analysis of strategic advantage can proceed from a fully specified mechanism rather than from undocumented exchange behaviour.
A structural feature of IPP is its dependence on trading volumes reported by external venues (e.g., centralized exchanges). To the extent that these venues are affected by manipulation patterns such as wash trading, spoofing, or artificial volume inflation, the resulting distortions will propagate into the ranking of participants and the distribution of outcomes.
The protocol does not and cannot certify that the external market is free of such behavior; it can only make its own transformation of publicly observable data explicit. For a typical participant without specialized models of short‑horizon volume dynamics, this means that outcomes at the level of individual positions will often be difficult to forecast ex ante, despite the formal determinism of the mechanism.
The interaction of intraday seasonality, news flow, and other agents' strategies generally produces a degree of effective unpredictability that, from the participant's subjective perspective, can resemble a high‑risk game: in any given position there is a non‑trivial probability of losing the entire stake \( A \) (plus fees). IPP therefore does not present itself as a source of guaranteed or mechanically generated yield. For participants without a demonstrable informational or strategic edge, the expected value of repeated participation is non‑positive once protocol and transaction fees are taken into account, in the same way that symmetric traders in fee‑bearing futures markets face negative expectation.
Any use of the mechanism should be based on explicit recognition of this risk profile and limited to capital that participants can afford to lose in a speculative, zero‑sum setting.
Let:
Then, for a fully filled position with no early exits:
1. Total pool:
\[ \text{Pool} = A B. \]
2. Pool of negative‑outcome participants:
\[ \text{LosersPool} = A (B - C). \]
3. Protocol fee:
\[ F_{\mathrm{prot}} = X \cdot \text{LosersPool}. \]
4. Pool available for redistribution to positive‑outcome participants:
\[ \text{WinnersPool} = \text{LosersPool} - F_{\mathrm{prot}}. \]
If the sharing rule is symmetric (for example, equal split among the \( C \) positive‑outcome participants), then the gross payout to a winner is approximately:
\[ \text{Payout} \approx A + \frac{\text{WinnersPool}}{C} - f_{\mathrm{pay}}. \]
The corresponding net profit for a winner in that position is
\[ \Pi_{\mathrm{win}} \approx \frac{A (B - C) (1 - X)}{C} - f_{\mathrm{pay}}, \]
while each loser incurs a net loss of approximately
\[ \Pi_{\mathrm{lose}} \approx -A - f_{\mathrm{tx,join}}, \]
where \( f_{\mathrm{tx,join}} \) collects fixed transactional costs associated with joining and (if applicable) optional exit and payout.
If all participants are ex ante symmetric (no informational or execution advantage) and the ranking induced by volume values is, from their point of view, effectively random, then each participant faces probability \( C/B \) of being in the positive‑outcome set and \( (B-C)/B \) of being in the negative‑outcome set. In this symmetric benchmark, the expected value of a single participation is:
\[ \mathbb{E}[\Pi] \approx \frac{C}{B} \cdot \Pi_{\mathrm{win}} + \frac{B - C}{B} \cdot \Pi_{\mathrm{lose}}. \]
Because \( F_{\mathrm{prot}} \) is a fixed fraction of the negative‑outcome pool, and fixed transaction fees are small relative to \( A \), this expected value is approximately equal to \(-X\) times the average stake, i.e., the house edge is of order \( X \leq 5\% \) of the at‑risk capital. Unlike in many other markets, this edge is transparent, mechanically defined, and does not depend on hidden venue policies, funding flows, or proprietary risk engines.
As an illustrative example, consider the configuration
\[ A = 100,\quad B = 20,\quad C = 1,\quad X = 0.05,\quad f_{\mathrm{pay}} = 0.10\ \text{USDT}. \]
Then:
With \( C = 1 \), the unique winner receives approximately
\[ \text{Payout} \approx 100 + 1805 - 0.10 = 1904.90\ \text{USDT}, \]
yielding
\[ \Pi_{\mathrm{win}} \approx 1804.90\ \text{USDT},\quad \text{ROI} \approx 1804.9\%. \]
The ex ante probability of being the unique winner in a symmetric benchmark is \(1/20 = 5\%\), while the probability of losing the entire stake is \(95\%\). The expected profit per 100 USDT stake in this configuration is therefore approximately
\[ \mathbb{E}[\Pi] \approx 0.05 \cdot 1804.9 + 0.95 \cdot (-100) \approx -4.755\ \text{USDT}, \]
corresponding to an expected loss of about \(4.8\%\). This value is fully explained by the protocol fee and fixed payment cost; there is no additional hidden erosion due to venue‑specific mechanisms.
From a behavioural perspective, IPP differs from leveraged futures not only in its mathematical loss distribution but also in the way it exposes participants to psychological stress:
For many individuals, especially those without professional trading infrastructure, this structure can substantially reduce cognitive load: instead of continuously managing margin, leverage, and stop‑loss orders, participants make discrete decisions about when and where to enter, and with what stake \( A \). The emotional trajectory becomes a sequence of bounded‑risk experiments rather than a continuous exposure to potentially ruinous fluctuations.
This does not make IPP "safe" or "low risk" in any absolute sense: participants can still incur repeated losses, and the mechanism remains speculative and zero‑sum. It does, however, align the shape of risk more directly with human factors such as:
Because downside per position is fully specified by \( A \), the protocol effectively quantizes risk into discrete units that participants can align with their own self‑assessment. An individual who is prepared to lose, for example, 100 USDT on a speculative experiment can implement exactly that exposure by choosing \( A = 100 \) and participating in a single position, rather than constructing a leveraged futures portfolio whose effective exposure is unclear ex ante.
In this sense, IPP externalizes a significant portion of risk management from the venue's opaque risk engine into the participant's explicit choice of \( A \) and of which \((A,B,C)\) configurations to engage with. The mechanism does not prevent poor choices, but it makes the consequences of each choice mechanically bounded and analytically tractable.
This section provides a comprehensive comparison between IPP and leveraged crypto futures, highlighting structural differences in mechanics, risk profiles, operational characteristics, and participant experience. Understanding these differences is essential for participants to make informed decisions about which mechanism aligns with their risk tolerance, capital constraints, and strategic goals.
In both mechanisms, uncertainty originates from the behaviour of the same underlying market: order flow, news, liquidity regimes, and the interaction of other agents' strategies. Neither IPP nor futures injects exogenous randomness in the form of an internal random-number generator; both are deterministic transformations of market observables.
Consequently, any argument that labels IPP "lottery-like" (in the informal sense) solely because realized volumes are difficult to predict would, by parity of reasoning, label futures "lottery-like" because realized prices are difficult to predict. The difference lies not in the presence or absence of uncertainty, but in which market variable is used and how risk is bounded. For a formal definition of "lottery" and why IPP does not qualify, see Section 1.1.
Leverage and margin in futures. Perpetual futures allow participants to control notional exposures that far exceed their initial capital. Losses can be amplified by leverage, and path-dependent liquidations can crystallize large losses on short time scales. Effective loss bounds are stochastic and depend on:
In extreme cases, traders can lose their entire account balance or more relative to initial margin, even if the underlying asset does not undergo an extreme move in absolute terms.
Bounded loss in IPP. By contrast, IPP implements:
For a given position, the maximum loss per participant is bounded above by the stake \( A \) (plus small fixed fees). The loss distribution for a single participation is fully described by:
From a risk-theoretic standpoint, IPP is a fixed-stake, bounded-loss mechanism. Futures are variable-stake, path-dependent mechanisms in which effective risk can grow endogenously.
If one uses "lottery-like" in the broad informal sense (see Section 1.1), then leveraged futures — where both gains and losses can be leveraged and where liquidation cascades exist — fit that description at least as strongly as IPP.
Futures.
IPP.
Thus, in IPP each exposure is bounded in both amount and time; there is no concept of a "forever-open" position. In futures, risk can persist indefinitely, and the transition from "safe" to "catastrophic" can occur discontinuously via margin events at unpredictable times.
Centralized futures venues.
In such environments, the line between "platform", "market-maker", and "counterparty" can be blurred, and the effective payoff structure of a given contract can evolve over time as rules are adjusted.
IPP as a neutral mechanism.
Manipulation of underlying exchange volumes, where economically feasible, affects IPP only through the same data that any third party can observe and analyze ex post. The protocol does not hide, filter, or re-weight these volumes in an opaque manner. In practical terms:
For a detailed discussion of terminology and classification criteria, including formal and informal definitions of "lottery", see Section 1.1. Here we summarize the key points in the context of comparing IPP with futures:
Under the informal definition (any activity with uncertain outcomes, negative expected value, and concentration of gains in a small minority), many speculative derivatives — including leveraged futures — satisfy "lottery-like" phenomenology. IPP also exhibits this pattern when viewed through this lens.
Under the formal definition (mechanisms driven by explicit randomization devices independent of economic processes), both futures and IPP are not lotteries. Both are zero-sum derivative mechanisms with fees, in which outcomes are functions of market observables (prices or volumes), and participants can attempt to obtain a systematic advantage through information, modelling, or execution.
If one chooses to use "lottery" in the broad informal sense, consistency demands that the same label apply to futures as to IPP. If one uses it in the narrow formal sense (as defined in Section 1.1), neither qualifies. What distinguishes IPP from futures is not whether it is "lottery-like", but how transparently it encodes risk bounds, payoff functions, and the link to observable market data.
Leveraged futures trading typically places a high cognitive and emotional burden on non-professional participants:
This environment encourages:
IPP alters this pattern in several structural ways:
This does not make IPP psychologically neutral: losing the full stake \( A \) can still be emotionally impactful, especially if repeated. However, it reduces the specific stressors associated with leverage, liquidations, and 24/7 surveillance of the market. For many individuals, particularly those without professional trading infrastructure or risk-management tools, bounded, discrete exposures are easier to integrate into everyday life than open-ended leveraged positions.
Around futures markets, especially in retail-facing segments, a large ecosystem of:
has emerged. In many such setups, the revenue of signal providers comes primarily from selling access, not from trading performance. The economic incentives can therefore become decoupled from actual skill; "success stories" may serve more as marketing material than as evidence of systematic edge.
IPP, by design, does not embed or require such an ecosystem:
This does not prevent the emergence of low-quality or promotional commentary around IPP, but it reduces the structural dependence on opaque "gurus". The mechanism itself remains indifferent to narratives; it enforces only the payoff function encoded in its contracts.
Futures trading mechanics:
IPP mechanics:
These differences make IPP suitable for discrete, bounded-risk experiments but unsuitable for dynamic portfolio management or sophisticated hedging strategies that require continuous position adjustment.
Futures leverage and capital efficiency:
IPP capital efficiency:
IPP prioritizes risk clarity (knowing exactly how much can be lost) over capital amplification (controlling large notional positions with small capital). This trade-off makes IPP unsuitable for participants seeking maximum leverage or notional exposure, but suitable for those prioritizing bounded risk and transparent outcomes.
Futures market access:
IPP market access:
IPP's dependence on underlying market liquidity creates a natural selection mechanism: positions on illiquid pairs may fail to resolve, while positions on liquid pairs proceed normally. This protects participants from outcomes based on insufficient or manipulated data, but limits the range of trading pairs that can be reliably used.
From a structural and behavioural standpoint, IPP offers the following advantages relative to leveraged futures:
At the same time, IPP has clear limitations relative to futures:
IPP should therefore be understood not as a drop-in replacement for futures, but as a complementary derivative mechanism that prioritizes bounded loss, determinism, and verifiability over maximal flexibility and leverage. It is suitable for participants who value:
It is not suitable for participants who need:
A central property of IPP is determinism: given
there exists exactly one outcome consistent with the mechanism's rules. The protocol itself introduces no additional randomness and no discretionary choices.
For a fixed configuration \((A,B,C,T)\) and a chosen trading pair, the mechanism:
At each step, the rules are fixed and public. Given the same input data \((\{t_i\}, V(\cdot))\), any independent implementation of the algorithm will produce the same partition of participants and the same set of transfers, up to rounding effects.
Determinism has several direct consequences for auditability:
In practice, these properties are realized to the extent that:
Without these elements, IPP remains formally deterministic but less transparent to external observers.
IPP is intentionally and tightly linked to real markets:
This linkage has two complementary implications:
1. Alignment with real economic activity.
IPP does not fabricate an independent randomness source; it reuses the same data (volume) that drive trading and risk-management systems in the broader ecosystem.
2. Inheritance of external vulnerabilities.
The protocol inherits any flaws, pathologies, or manipulations in the underlying venue's volume reports:
IPP cannot, by itself, guarantee that the external market is "clean". It can only make its own transformation of observable data explicit and reproducible, so that any investigation of anomalies has a well-defined object: the external data and their mapping, not internal discretionary decisions.
Accordingly, part of IPP's practical risk surface lies outside the core contracts:
These are implementation and governance questions layered on top of a deterministic core.
In addition to determinism of outcomes, IPP is non-custodial by design:
As a consequence:
Generic smart-contract risks remain relevant:
These risks must be addressed through careful engineering, formal verification where possible, and independent audits. However, intentional expropriation by protocol operators at the contract level is structurally ruled out by the absence of discretionary control paths over user funds.
From the perspective of an external observer (whether human or machine), trust in IPP as a mechanism depends on more than its abstract specification:
When these conditions hold, the combination of:
produces a mechanism in which:
IPP can then be analysed and classified as a bounded-risk, market-linked derivative protocol whose residual risks are primarily those of its external dependencies and its users' strategic decisions, rather than of internal discretion or hidden randomness.
Independent Precision Protocol can be characterized as a deterministic, volume‑based derivative mechanism with bounded downside per position and a fixed safety horizon. It reallocates capital among participants according to their relative timing with respect to realized trading activity on an external exchange, using a transparent and reproducible algorithm.
Compared with leveraged futures, IPP offers a simpler and more tightly controlled risk profile: no leverage, no margin calls, and no path‑dependent liquidations. At the same time, it remains firmly anchored in real market data, and its structure admits systematic strategies wherever volume patterns are statistically predictable. These properties make IPP suitable both as an object of academic study in mechanism design and as a building block for transparent, verifiable investment products on public blockchains.
From a regulatory perspective, it is useful to distinguish IPP from two broad classes of financial products: (i) interest‑bearing or time‑based deposit accounts, in which a centralized intermediary accepts funds onto its balance sheet and promises a return over time, and (ii) lotteries and house‑bank gambling, in which a central operator designs payoffs, may act as the counterparty, and often controls or seeds the randomization device that determines outcomes.
IPP does not operate interest‑bearing or savings‑type deposits. Participant funds are contributed as fixed‑size stakes into autonomous smart contracts representing specific positions; they are not lent out, re‑invested, or pooled into open‑ended balance‑sheet products. There is no promise of yield as a function of time held. Instead, capital is redistributed within each position exclusively according to the on‑chain payoff rules and observable market data, with bounded loss per position and a fixed safety horizon \(T\).
Likewise, IPP does not operate a "house book" in the sense of a casino or dealer. The protocol never takes directional exposure against participants and does not choose outcomes; it only enforces a public, deterministic function of participants' actions and external volumes. To the extent that some participants achieve persistent positive P&L while others incur losses, this reflects competition in a zero‑sum environment with heterogeneous skill and information, not discretionary decisions by a central operator. Any supervisory assessment of IPP as a financial product should therefore focus on its nature as a non‑custodial, bounded‑risk, market‑linked derivative mechanism, rather than on analogies to deposit‑taking or operator‑driven gambling schemes.
The preceding sections describe structural risk in IPP: bounded loss per position, deterministic payoffs, explicit fee structure, and dependence on external market volumes. These properties are intrinsic to the protocol and do not change from one participant to another.
In contrast, behavioral risk arises from how individuals choose to interact with the mechanism:
IPP can reduce certain classes of structural and psychological stress relative to leveraged futures (e.g., margin calls, continuous PnL swings), but it does not remove the possibility that participants use it in self-destructive ways. The protocol eliminates many avenues for technical or institutional abuse; it does not eliminate human error or compulsive risk-taking.
Accordingly, risk assessment for IPP must separate:
A key behavioral distinction between IPP and leveraged futures lies in their temporal and experiential structure.
Continuous exposure in futures.
Discrete experiments in IPP.
From a behavioral viewpoint, IPP encourages a stepwise pattern: think → decide → accept bounded risk → observe outcome → reassess. This reduces the number of moments at which impulsive reactions can alter exposure. It does not guarantee rational behaviour, but it makes the decision points discrete and easier to isolate analytically.
IPP does not attempt to be a low-risk or conservative product; it is a zero-sum speculative mechanism. However, the shape of psychological load differs from that of leveraged futures:
For many individuals, this can lead to:
These effects are not guaranteed; participants can still choose to monitor markets obsessively or to over-allocate to IPP. The point is that the protocol's mechanics do not require continuous vigilance for basic risk control.
IPP is structurally aligned with participants who:
Examples of suitable goals include:
The protocol is particularly aligned with individuals who prefer to reason in terms of:
"How much am I willing to risk on this specific idea?"
rather than:
"How much leverage can I apply before liquidation becomes too likely?"
Conversely, IPP is not designed for:
Linear price hedging.
It does not provide a straightforward long/short exposure to asset prices and is not a drop-in replacement for futures used to hedge spot portfolios or corporate exposures.
Leveraged capital amplification.
It does not offer arbitrary leverage; capital at risk is limited to \( A \). Participants seeking to transform small balances into very large notional exposures via leverage will not find equivalent functionality in IPP.
Fine-grained intraday risk management.
There are no built-in stop-loss or take-profit orders, no partial closes, no reversals within a position. Positions are atomic; intrapositional risk management is intentionally minimal.
Stable yield or income generation.
IPP is a zero-sum system with fees. For a symmetric participant without structural edge, repeated participation has negative expected value. It is therefore unsuitable as a primary source of income or as a "yield product".
Recognizing these boundaries is essential: misuse of IPP as a substitute for instruments it is not designed to replace can lead to inappropriate expectations and disappointment.
Like any speculative mechanism with discrete wins and losses, IPP can become a vehicle for addictive behavior if participants:
Bounded loss per position does not imply bounded loss over time when stakes and frequency are uncontrolled. The protocol is neutral with respect to participant psychology; it does not enforce:
From a behavioral standpoint, responsible use requires self-imposed rules such as:
These constraints are external to the protocol but crucial to keeping speculative activity within tolerable bounds.
In IPP, the parameter \( A \) acts as a quantized risk unit. The participant chooses not only whether to engage, but at what level of potential loss per position. This introduces an explicit self-knowledge component:
The choice of \( A \) encodes an implicit answer to the question:
"What is the maximum loss on a single idea that I can accept without destabilizing my finances or behaviour?"
The choice of \((A,B,C)\) encodes a view on:
From the standpoint of a machine analyzing participant behaviour, IPP exposes a clearer mapping between:
This makes IPP a useful environment for studying how humans interact with bounded-risk mechanisms, where:
In summary, from a behavioural and practical perspective:
IPP is a bounded-risk, volume-linked derivative mechanism suitable for:
IPP is not a replacement for:
Used within these boundaries, IPP offers a distinctive combination of:
An IPP position on a given trading pair can be modeled as a finite game \[ \mathcal{G}_{\mathrm{IPP}}(A,B,C,T) = \langle P, \mathcal{S}, \mathcal{O}, u \rangle, \] where:
For a single, fully filled position without early exits, the payoff function can be written as:
where \(A\) is the deposit, \(f_{\mathrm{tx}}\) is the sum of fixed transactional fees incurred by the participant (join, optional exit, payout), and \(\pi_i\) is the share of the redistributable pool assigned to participant \(i\) according to the deterministic ranking on integer volumes \(\{v_i\}\). The mapping \((V(\cdot), \{t_i\}) \mapsto \{v_i\} \mapsto \pi_i\) is fully specified and deterministic.
At the level of transfers between participants, abstracting from fees and rounding, one has \(\sum_i u_i = 0\). Including protocol and transaction fees \(\sum_i u_i = -F\), where \(F\) is the total fee collected in the position. This is formally identical to fee‑bearing futures markets, where the net sum of trader P&L equals the negative of aggregate fees.
A leveraged futures position on an underlying asset with price process \(S_t\) can be modeled as a game \(\mathcal{G}_{\mathrm{Fut}}\) with:
Ignoring funding and fees for a moment, the terminal P&L of a single directional position can be approximated as \[ \mathrm{P\&L}_{\mathrm{fut}} \approx \ell \cdot (S_{T'} - S_{t_0}), \] where \(\ell\) is effective leveraged exposure, \(t_0\) is entry time, and \(T'\) is the random exit or liquidation time. In reality, transaction fees, spreads, funding payments, and forced liquidations make the mapping from \(\{S_t\}\) to trader P&L path‑dependent and sensitive to exchange‑specific rules.
As in IPP, the aggregate P&L of all traders in a futures market, net of exchange fees and spreads captured by liquidity providers, sums to a negative amount equal to total fees. Uncertainty in both settings arises from market dynamics; neither mechanism is driven by exogenous random draws in the lottery sense.
Consider again an IPP position with parameters
\[ A = 100,\quad B = 20,\quad C = 1,\quad X = 0.05,\quad f_{\mathrm{pay}} = 0.10. \]
As shown in Section 7.7, the unique winner receives approximately 1904.90 USDT, corresponding to a net profit
\[ \Pi_{\mathrm{win}} \approx 1804.90\ \text{USDT}, \]
while each loser incurs a loss of approximately \(-100\) USDT (ignoring join-side transactional fees for simplicity).
In the symmetric benchmark where each participant has probability \(1/20\) of being the winner, the expected profit for a single 100 USDT stake is:
\[ \mathbb{E}[\Pi] = \frac{1}{20} \cdot 1804.90 + \frac{19}{20} \cdot (-100) \approx -4.755\ \text{USDT}. \]
This calculation makes explicit that:
In this sense, IPP delivers, in a formally transparent way, a profile that is often only implicitly present in leveraged futures trading: a small minority of large winners and a majority of net losers, with the aggregate shortfall accounted for by fees. The difference is that in IPP this profile is governed by explicit, immutable formulas rather than by a complex interaction of leverage, liquidation rules, and venue-specific microstructure.
Consider a “symmetric” participant with no informational or execution advantage over the population, who selects entries randomly among admissible times and positions and does not condition on private signals. In such a case, both IPP and futures markets share a structural property: the expected value of a marginal trade for this participant is non‑positive once fees are taken into account.
In IPP, conditioning on a fully filled position, let \(\mathbb{P}(i \in \text{positive})\) denote the probability that a randomly drawn participant belongs to the positive‑outcome set under some prior over join times and volume scenarios. Then the expected payoff of a symmetric participant can be written as:
\mathbb{E}[u_i]
= \mathbb{P}(i \in \text{positive}) \cdot \mathbb{E}[A + \pi_i - f_{\mathrm{tx}} \mid i \in \text{positive}]
+ \mathbb{P}(i \in \text{negative}) \cdot \mathbb{E}[-A - f_{\mathrm{tx}} \mid i \in \text{negative}].Because the net transfers between participants sum to \(-F\) (total fees), the aggregation of \(\mathbb{E}[u_i]\) across all participants equals \(-F\). For a symmetric participant, this implies \(\mathbb{E}[u_i] \leq 0\), with strict inequality whenever fees are strictly positive. This is not a property of “lottery‑likeness”, but a direct consequence of operating in a fee‑bearing zero‑sum mechanism.
In leveraged futures, under a symmetric prior and ignoring funding asymmetries, the expected P&L of a marginal trade before fees is approximately zero in a frictionless model, but after accounting for trading fees, spreads, and other transaction costs it becomes strictly negative. Empirical studies and exchange statistics consistently report that a small minority of traders capture most of the positive P&L, while the majority experience net losses once costs are included. This pattern is entirely consistent with a zero‑sum game with fees and does not, by itself, distinguish futures from IPP.
A salient difference between IPP and futures markets lies not in the presence or absence of uncertainty, but in the transparency of the risk profile at the moment of entry. In IPP, a participant who joins a position with parameters \( (A,B,C,T) \) knows ex ante that:
In leveraged futures, by contrast, the effective risk at entry depends on a combination of leverage, margin rules, liquidation thresholds, volatility regimes, and potential rule changes by the exchange. While traders can compute nominal liquidation levels under current conditions, the true tail risk — including gaps, cascading liquidations, and forced deleveraging — is difficult to quantify and is often underestimated in practice. From the participant’s perspective, the mapping from “I opened a position of size X with leverage L” to “my maximum possible loss” is therefore considerably less transparent than in IPP.
Finally, in IPP participants have full discretion over whether to join any given position in light of perceived manipulation risks or liquidity conditions of the underlying pair. Positions on illiquid or easily manipulated pairs can simply be ignored. This freedom to condition participation on an ex ante assessment of risk and market quality, together with bounded loss and a fixed safety horizon, differentiates IPP from many existing derivatives venues where complex and adaptive infrastructure makes effective risk assessment by non‑experts substantially harder.
Empirical studies of speculative trading in futures and other derivatives commonly report that a relatively small fraction of participants — on the order of a few percent — accounts for a large share of positive P&L, while the vast majority either breaks even before costs or incurs net losses once transaction costs are included. This pattern is often summarized colloquially as “2–5% of traders consistently win, 95% lose” and is frequently cited in discussions of speculatively oriented markets.
From the standpoint of game theory, such concentration of edge is a natural consequence of three facts:
In a zero‑sum setting with heterogeneous skill, it is mathematically impossible for a majority of participants to achieve above‑average returns simultaneously; any persistent edge must, by definition, be concentrated in a minority. This is true whether the underlying game is built on prices (as in futures) or on volumes (as in IPP). Observing that only a small subset of agents systematically outperforms therefore does not distinguish a “lottery” from a competitive market; it simply reflects the basic geometry of zero‑sum competition under heterogeneous capabilities.
In IPP, as in futures, it is reasonable to expect that a small set of participants with better models of volume dynamics, faster data, or more disciplined risk management will achieve positive long‑run P&L, while many casual participants will not. This empirical asymmetry says little about the underlying mechanism and much about the distribution of skill and discipline in the population. The structural questions remain: how clearly are risk and payoff specified, how bounded are losses, and how neutral is the protocol itself with respect to participant strategies. On these axes, IPP is designed to make risk and rules as explicit and verifiable as possible.