Nansen Token Screener

Token Discovery

Answers: "What tokens are trending and worth a deeper look?"

CHAIN=solana

# Screen top tokens by volume
nansen research token screener --chain $CHAIN --timeframe 24h --limit 20
# → token_symbol, price_usd, price_change, volume, buy_volume, market_cap_usd, fdv, liquidity, token_age_days

# Smart money only
nansen research token screener --chain $CHAIN --timeframe 24h --smart-money --limit 20

# Search within screener results (client-side filter)
nansen research token screener --chain $CHAIN --search "bonk"

# Smart money holdings — what SM wallets are holding
nansen research smart-money holdings --chain $CHAIN --labels "Smart Trader" --limit 20
# → token_symbol, value_usd, holders_count, balance_24h_percent_change, share_of_holdings_percent

# Nansen indicators for a specific token
TOKEN=<address>
nansen research token indicators --token $TOKEN --chain $CHAIN
# → risk_indicators, reward_indicators (each with score, signal, signal_percentile)

# Flow intelligence — only use for promising tokens from screener/indicators above
nansen research token flow-intelligence --token $TOKEN --chain $CHAIN
# → net_flow_usd per label: smart_trader, whale, exchange, fresh_wallets, public_figure

# Nansen Score Top Tokens — "what should I buy?" (public endpoint, any authenticated API key)
# Use this FIRST for discovery, then drill into individual tokens with `indicators` above
nansen research token top-tokens --limit 25
nansen research token top-tokens --market-cap largecap --limit 10
# → chain, token_address, token_symbol, performance_score, risk_score,
#   per-indicator contributions, market_cap_group, latest_date, last_trigger_on

Screener timeframes: 5m, 10m, 1h, 6h, 24h, 7d, 30d

Indicators: score is "bullish"/"bearish"/"neutral". signal_percentile > 70 = historically significant. Some tokens return empty indicators — not an error.

Top tokens — Nansen Score field reference

Results are pre-filtered to performance_score >= 15 server-side and returned sorted by: 1. performance_score DESC 2. market_cap_group priority (largecap → midcap → lowcap) 3. risk_score DESC 4. 24h volume DESC

So row 0 is always the strongest candidate for the filter you applied — no client-side ranking needed.

Market cap buckets (used in both the sort priority and the --market-cap filter):

• lowcap: market cap < $100M
• midcap: market cap $100M – $1B
• largecap: market cap > $1B

Every contribution is ternary — exactly one of {negative, 0, positive} per field. No partial values. Zero means "indicator didn't apply to this token" (out of scope), not "indicator was neutral".

Performance Score (Alpha — "likely to outperform BTC over 7–30d") Range: -60 to +75 (arithmetic bounds; live max is closer to +45 since no single token hits every positive indicator simultaneously). Buy threshold: >= 15. Sum of the five *_performance fields below.

Risk Score (Safety — "filters falling knives / dangerous setups") Range: -60 to +80 (arithmetic bounds). Safety threshold: > 0 (positive = safer, negative = riskier). Sum of the four *_risk fields below. For every risk field: upstream score low → positive contribution, high → negative contribution, medium/missing → 0.

Other response fields:

• market_cap_group: lowcap / midcap / largecap — see thresholds above.
• latest_date: ISO datetime of the most recent indicator refresh for this token.
• last_trigger_on: ISO datetime of the most recent trigger across contributing indicators (MAX aggregate — individual indicators may be days-to-months stale even when this looks fresh). Use indicators on a specific token to audit per-indicator ages.

Stablecoins rank high but aren't picks. USDC, USDT, DAI, FDUSD and similar score well on chain_fees + liquidity indicators but aren't what "what should I buy" means. Filter them out of the shortlist using the canonical whitelist at nansen-dbt-ch-tokens/seeds/stablecoins_for_indicator.csv before drilling into indicators.

Typical workflow: start with top-tokens for a shortlist → drop stablecoins → run indicators on the top 3–5 to inspect individual signals and their signal_percentile → flow-intelligence only on the finalists to confirm SM conviction.

Field meanings and contribution mappings above are sourced from nansen-dbt-ch-tokens/models/indicators/api_nansen_score_indicators_all_tokens_latest.sql and per-indicator model yml files. Sign conventions and live value ranges were validated against production ClickHouse data.

Flow intelligence is credit-heavy. Use it to confirm SM conviction on tokens that already look promising from screener + indicators, not as a first pass on every token.