Documentation Index
Fetch the complete documentation index at: https://mintlify.com/0xW1re/solvedocs/llms.txt
Use this file to discover all available pages before exploring further.
The standard model receives a 68-feature vector assembled at inference time from wallet profiles, signal-level event data, creator intelligence, token state, social graph analysis, and market context. Every feature is documented below with its source and what it represents.
Features must be assembled in exactly the order defined by the FEATURE_ORDER constant in the codebase. The metadata file for each .onnx model contains the ordered feature list used during training. A mismatch in feature ordering silently corrupts every prediction.
Wallet point-in-time (4 features)
These are the wallet’s stats as they were when the signal fired — not current values. Using point-in-time snapshots is what prevents lookahead bias: the model sees only the information that was available at the moment of the trading decision.
| Feature | Description |
|---|
alpha_score | 0–100 composite quality score at time of signal |
wallet_graduation_rate | Share of tokens bought that graduated, at signal time |
wallet_avg_buy_rank | Typical buy rank at signal time (lower = earlier entry) |
wallet_trades_at | Total trades observed at signal time |
Wallet behavioural (15 features)
Computed from the wallet’s full trading history by FeatureComputer, rolled up on a 30-minute cadence.
| Feature | Description |
|---|
tokens_traded | Total unique tokens bought |
pct_top10_entries | Fraction of buys that were in the first 10 wallets |
avg_seconds_after_creation | Average delay between token creation and the wallet’s buy |
pct_buys_under_60s | Fraction of buys within 60 seconds of token creation |
pct_buys_under_300s | Fraction of buys within 300 seconds of token creation |
avg_sol_per_buy | Mean buy size in SOL |
median_sol_per_buy | Median buy size in SOL |
sol_size_stddev | Standard deviation of buy sizes (high = inconsistent sizing) |
max_single_buy | Largest single buy ever recorded |
avg_hold_time_seconds | Average time between buy and sell |
pct_quick_flips | Fraction of positions closed within 5 minutes |
pct_diamond_hands | Fraction of positions held over 24 hours |
unique_creators_traded | Number of distinct token creators engaged with |
pct_repeat_creator_buys | Fraction of buys on tokens from already-seen creators |
active_days_30d | Distinct active days in the last 30 days |
Wallet returns (11 features)
Computed from PnlCalculator and PeakTracker output across the wallet’s full trade history.
| Feature | Description |
|---|
graduation_rate | Share of bought tokens that graduated |
win_rate | Share of closed positions in profit |
sol_weighted_return | SOL-weighted average return across all positions |
avg_realized_multiple | Average exit multiple |
avg_peak_multiple | Average of peak prices seen during hold |
capture_efficiency | Ratio of realised multiple to peak multiple (0–1) |
profit_factor | Total profit divided by total loss |
return_stddev | Standard deviation of per-trade returns |
avg_loss_pct_on_losers | Average loss magnitude on losing trades |
avg_gain_pct_on_winners | Average gain magnitude on winning trades |
is_bot | 1 if the wallet has been classified as a bot |
Wallet context (1 feature)
| Feature | Description |
|---|
wallet_age_days | Days since the wallet was first observed by the pipeline |
Signal-level (10 features)
These describe the specific signal event — not the wallet’s history. They capture the conditions at the exact moment of the tracked buy.
| Feature | Description |
|---|
buy_rank | This wallet’s buy rank on this token |
sol_amount | Size of this specific buy in SOL |
curve_pct_at_buy | Bonding curve fill percentage at time of buy (0–1) |
curve_sol | Curve SOL reserves at time of buy |
velocity_buys_60s | Buy count on this token in the last 60 seconds |
velocity_buys_300s | Buy count on this token in the last 300 seconds |
sol_volume_60s | SOL volume on this token in the last 60 seconds |
buy_rank_percentile | Buy rank divided by unique buyer count (0–1) |
sol_vs_wallet_avg | This buy’s SOL amount vs the wallet’s average (1.0 = typical) |
token_age_at_signal | Token age in seconds at time of signal |
Temporal (3 features)
| Feature | Description |
|---|
buy_sell_ratio | Total buys divided by (total sells + 1) at time of signal |
hour_of_day | UTC hour of the signal (0–23) |
day_of_week | Day of week (0 = Sunday, 6 = Saturday) |
Creator intelligence (8 features)
Sourced from CreatorRiskScorer, which aggregates historical data across all tokens created by this address.
| Feature | Description |
|---|
creator_graduation_rate | This token creator’s historical graduation rate |
creator_rug_rate | Quick-death rate across this creator’s tokens |
creator_risk_score | 0–100 composite creator risk score |
creator_tokens_created | Total tokens ever created by this creator |
creator_serial_velocity | Tokens created per day over the last 30 days |
creator_avg_insider_pct | Average insider presence across the creator’s tokens |
creator_avg_bot_pct | Average bot buyer percentage across the creator’s tokens |
creator_is_serial | 1 if the creator has been classified as a serial creator |
Token state (7 features)
Current on-chain and risk state of the token at the moment of the signal.
| Feature | Description |
|---|
token_unique_buyers | Unique buyers on this token so far |
token_total_buys | Total buy transactions |
token_total_sells | Total sell transactions |
token_risk_score | Token-level risk score (0–100) |
token_bot_buyer_pct | Fraction of buyers classified as bots |
token_top10_concentration | Share of token supply held by the top 10 wallets |
token_bundle_confidence | Highest bundle confidence score detected for this token |
Lifecycle (1 feature)
| Feature | Description |
|---|
lifecycle_state_encoded | Token lifecycle state encoded as an integer (0 = launch … 7 = graduated) |
Social graph (5 features)
Sourced from the CoOccurrence and GraphBuilder services, which maintain a wallet co-buy frequency graph updated hourly.
| Feature | Description |
|---|
cluster_size | Number of wallets in this wallet’s co-occurrence cluster |
co_occurrence_max_score | Highest co-occurrence edge score for this wallet |
cluster_avg_grad_rate | Average graduation rate of wallets in the same cluster |
tracked_wallets_already_in | Count of tracked wallets already holding this token |
is_first_tracked_buy | 1 if this is the first tracked wallet to buy this token |
Market context (2 features)
Market-wide signals that capture the current environment at the time of the signal.
| Feature | Description |
|---|
tokens_created_last_hour | Market-wide token creation rate over the last hour |
rolling_graduation_rate_2h | Market-wide graduation rate over the last 2 hours |
Default values
Every feature has a carefully chosen default value applied when data is unavailable — for example, a wallet that has never been seen before, or a creator with no prior tokens.
Defaults represent a neutral, unknown entity in a neutral market — not a worst-case assumption, which would introduce systematic bias toward rejecting unknown wallets. The model was trained with these same defaults applied during training wherever data was missing, so it handles them correctly in production.
For example: wallet_avg_buy_rank defaults to 50 (median rank), avg_sol_per_buy defaults to 0.5 SOL (typical size), and lifecycle_state_encoded defaults to -1 (unknown state).