{
  "sources": [
    {
      "url": "https://www.linkedin.com/pulse/amm-fee-challenge-notes-from-our-run-optimization-arena-qe3ff",
      "quote": "Our submission reached 9th among 654 participants."
    },
    {
      "url": "https://www.benedict.dev/optimization-arena-learnings",
      "quote": "Fair price drops and all 8 strategies simultaneously flip protect side to ask"
    },
    {
      "url": "https://raw.githubusercontent.com/jiayaoqijia/amm-challenge-yq/3e9dc7d5a6192c88b562a0f8d9c172ca566ec94d/README.md",
      "quote": "Official leaderboard score: **523.21**"
    },
    {
      "url": "https://raw.githubusercontent.com/dipankar/amm-challenge/c588ae6c47fc9af2063c9bb1f2efbeb0dd276dab/ANALYSIS.md",
      "quote": "Research Methodology Post-Mortem"
    },
    {
      "url": "https://raw.githubusercontent.com/dipankar/amm-challenge/c588ae6c47fc9af2063c9bb1f2efbeb0dd276dab/README.md",
      "quote": "Current Best: SigmaAdaptive (526.54 Edge)"
    },
    {
      "url": "https://raw.githubusercontent.com/ChengHua926/amm-challenge/d34a5199c59a4c44d5336a25f1436b4ae1f21104/contracts/src/Strategy.sol",
      "quote": "uint256 constant PHAT_ALPHA_RETAIL = 60000000000000000;"
    },
    {
      "url": "https://raw.githubusercontent.com/jiayaoqijia/amm-challenge-yq/3e9dc7d5a6192c88b562a0f8d9c172ca566ec94d/contracts/src/Strategy.sol",
      "quote": "uint256 constant PHAT_ALPHA = 260000000000000000;"
    },
    {
      "url": "https://raw.githubusercontent.com/dipankar/amm-challenge/c588ae6c47fc9af2063c9bb1f2efbeb0dd276dab/contracts/src/SigmaAdaptiveStrategy.sol",
      "quote": "uint256 constant TOX_CUBIC_COEF  = 15000 * BPS;"
    },
    {
      "url": "https://api.imd.fun/artifacts/19f2ec857df536e8ab62ab1da89ec6623332521ce46849f15bccff402c63e1e5",
      "quote": "f24b6ca198b744c380842c3a17964a6dc21f311bb04984b26733afa3fee8eaf2"
    },
    {
      "url": "https://api.imd.fun/artifacts/33f54e81f3372088e9eced52c0174f310ac348af98ef5727b73785c088679194",
      "quote": "Do not use confirmation seeds to choose or change a strategy."
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_competition/competition/match.py",
      "quote": "rng = np.random.default_rng(seed=i)"
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_sim_rs/src/simulation/engine.rs",
      "quote": "Some(seed + 1)"
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_sim_rs/src/market/arbitrageur.rs",
      "quote": "Maximize profit = Δx * p - Y_paid"
    }
  ]
}