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.mplconfig
.mplconfig
 
 
data
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fetch-ethers
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scripts
scripts
 
 
src
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tests
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README.md
README.md
 
 
requirements.txt
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Oscillon fee backtest

Compare static 3 bps vs Oscillon dynamic drain fee on Ethereum USDC/USDT minute swaps.

Setup

python3 -m pip install -r requirements.txt

Data

Minute CSVs in data/ named:

ethereum-0x3416cf6c708da44db2624d63ea0aaef7113527c6-YYYY-MM-DD.minute.csv

Fetch with ethers (no BigQuery):

cd fetch-ethers && npm install && npm run fetch -- --chain ethereum --hours 24

Set ETHEREUM_RPC_URL in fetch-ethers/.env if needed. Output lands in ../data/.

Backtest

python3 scripts/backtest.py --start 2023-03-10 --end 2023-03-15 \
  --mode oracle --oracle-csv data/chainlink_usdc_2023.csv --fee-model hybrid
python3 scripts/backtest.py --start 2023-03-10 --end 2023-03-15 --apr

Writes output/summary.json. Stress window (default): Chainlink USDC $0.997, pool lags 15 bps for 180 minutes.

APR / APY

Fee APR/APY = annualized swap-fee income (positive yield).
Net APR/APY = fee yield minus modeled LVR; negative under stress when toxic flow > fees.

python3 scripts/calc_apr.py --days 1 --capital 100000 --tvl 2500000

Fee model

Case Fee
Healthy / restore 3 bps base
Drain, depeg < 3 bps 3 bps base only
Drain, depeg ≥ 3 bps 3 bps base + hybrid surcharge (piecewise ∩ quadratic, anchor 1 bps)

Solidity parity: BASE_FEE_PIPS = 300 + OscillonFeePolicy.hybridFeeBps() surcharge.

Verified event replays

python3 scripts/run_verified_backtests.py   # March 2023 USDC (in-repo data)
python3 -m pytest tests/ -q

Results: output/verified_backtest_results.md

Event Prepared data Status
USDC Mar 2023 (SVB) data/prepared_swaps_2023-03.csv ✅ in repo
USDe Oct 2025 data/prepared_swaps_usde_2025-10.csv fetch BigQuery first
  • Data source: backtest_mainnet.py only (not depeg_analysis.py for headline numbers)
  • Base fee: 3 bps (BASE_FEE_BPS = 3.0)
  • LVR formula: max(0, drain_size × (dev_bps − fee_bps) / 10000) on drain swaps only
  • Volume loss: drain swaps where dynamic_fee > competitor_fee and dev_bps < 50
  • Swap size: drain leg only (netAmount0.clip(lower=0) / 1e6 for USDC/USDT pool)
  • APR: annualised period return × (365 / days) — label as stress period APR, not normal APR
  • Solidity parity: Python uses identical base + surcharge architecture as OscillonFeePolicy.sol

Publishing limitations (conservative direction)

These are documented, accepted biases — they understate surcharge/LP income, not overstate it:

Limitation Effect on headline numbers
Some swap replays from Infura RPC, not BigQuery +10% drain volume → ~0.71 → ~0.78 bps/year (immaterial)
Backtest uses hook integer fees, not float Slightly lower LP income (e.g. dev=7: 4.0 vs 4.33 bps)
Oracle merge_asof backward, no staleness cap Rare gaps understate dev_bps during fast depegs

USDC vs USDT oracle legs (separate — never mixed)

Leg Oracle feed Drain rule Use for
USDC (token0) Chainlink USDC/USD netAmount0 > 0 + peg below Deployed hook / auditor headlines
USDT (token1) Chainlink USDT/USD netAmount1 > 0 + peg below Counterfactual only — not deployed

Run both legs end-to-end (separate prepared CSVs, charts, timelines):

python3 scripts/run_oracle_leg_pipeline.py --start 2026-01-01 --end 2026-06-30

Outputs:

  • data/prepared_swaps_2026_h1_usdc_oracle.csv + output/backtest_2026_h1_usdc_oracle.png
  • data/prepared_swaps_2026_h1_usdt_oracle.csv + output/backtest_2026_h1_usdt_oracle.png
  • output/oracle_leg_comparison_2026_h1.md

Single leg only:

python3 scripts/run_oracle_leg_pipeline.py --leg usdc --start 2026-01-01 --end 2026-06-30
python3 scripts/run_oracle_leg_pipeline.py --leg usdt --start 2026-01-01 --end 2026-06-30

Fetch USDT oracle (once):

python3 scripts/fetch_data.py --dune-only --oracle-asset usdt \
  --start-date 2026-01-01 --end-date 2026-06-30 \
  --oracle-out data/chainlink_usdt_2026_h1.csv

Cross-asset research (new stables / RWAs)

Pool presets in src/pool_config.py:

Preset Pool Chain Use
usdc-usdt USDC/USDT Ethereum Deployed baseline
usde-usdt USDe/USDT v4 Ethereum Active ~$4.5M pool
usde-usdt-legacy USDe/USDT v3 Ethereum Oct 2025 minute files in repo
pyusd-usdc PYUSD/USDC v4 Ethereum Thin fiat stable
fdusd-usdc-bsc FDUSD/USDC BSC Apr 2025 sentiment depeg

Fetch oracles (Dune):

python3 scripts/fetch_data.py --dune-only --oracle-asset usde --start-date 2026-01-01 --end-date 2026-06-30
python3 scripts/fetch_data.py --dune-only --oracle-asset pyusd --start-date 2026-01-01 --end-date 2026-06-30
python3 scripts/fetch_data.py --dune-only --oracle-asset fdusd --start-date 2025-04-01 --end-date 2025-04-05 \
  --oracle-out data/chainlink_fdusd_2025-04.csv

NAV reference mode (RWAs — OUSG, USDY):

python3 scripts/prepare_data.py \
  --pool-preset pyusd-usdc \
  --oracle-source nav \
  --nav-csv data/nav_ousg_sample.csv \
  --reference-mode nav \
  --out data/prepared_swaps_ousg_nav_sample.csv

Cross-asset scorecard (runs backtests on prepared CSVs that exist):

python3 scripts/asset_scorecard.py
# → output/asset_scorecard.md + output/asset_scorecard.json

Tests

python3 -m pytest tests/ -q

Layout

src/oscillon_fee.py   # hook-aligned fee
src/depeg_analysis.py # static vs dynamic + LVR proxy
src/scenario.py       # inject stress depeg
src/data_loader.py    # load minute CSVs
src/apr.py            # APR/APY
scripts/backtest.py
scripts/calc_apr.py
fetch-ethers/         # optional data fetch

ETH Mainnet Pipeline (BigQuery)

Uses Chainlink USDC oracle from Dune + demeter-fetch with BigQuery for swaps (no RPC calls).

Full month (example: May 2026):

export BIGQUERY_AUTH_FILE=/absolute/path/to/gcp-bigquery.json
chmod +x scripts/run_month_pipeline.sh
./scripts/run_month_pipeline.sh 2026-05

Or step by step:

# 1) Fetch oracle + 31 daily *.minute.csv files (Dune key from .env if set)
python3 scripts/fetch_data.py \
  --start-date 2026-05-01 \
  --end-date 2026-05-31 \
  --oracle-out data/chainlink_usdc_2026-05.csv \
  --bigquery-auth-file "$BIGQUERY_AUTH_FILE"

# 2) Merge oracle, dev_bps, is_drain (peg_below & USDC sold into pool)
python3 scripts/prepare_data.py \
  --use-minute-files \
  --start 2026-05-01 --end 2026-05-31 \
  --oracle data/chainlink_usdc_2026-05.csv \
  --out data/prepared_swaps_2026-05.csv

# 3) Backtest + charts
python3 scripts/backtest_mainnet.py --prepared data/prepared_swaps_2026-05.csv
python3 scripts/compare_fee_models.py --prepared data/prepared_swaps_2026-05.csv

Minute files land in data/ as: ethereum-0x3416cf6c708da44db2624d63ea0aaef7113527c6-2026-05-DD.minute.csv

Equivalent generated demeter-fetch config:

cat data/demeter_fetch_uniswap_bigquery.toml

Optimize K (λ score)

python3 scripts/sweep_k.py --prepared data/prepared_swaps.csv --ks 20,30,45,60,80

Score: lp_revenue - λ1×LVR - λ2×volume_loss. Best K is printed and saved to output/k_sweep_score.png. Use that K in the hook / FeeContext(..., k_override=K).

If you already have oracle CSV and only want swaps:

python3 scripts/fetch_data.py --skip-dune \
  --start-date 2023-03-10 \
  --end-date 2023-03-15 \
  --bigquery-auth-file /absolute/path/to/gcp-bigquery.json

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