{
 "sources": [
  {
   "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/README.md",
   "quote": "3. **Arb or retail.** Worth 2.7. Tried, no gain: forbidding the quote zone where the two overlap (`DP_MASK`, costs 0.6 to 2.4 with perfect information and leaves the error at 13 to 15%), buying more pins with tighter quotes (kappa above 1e-5 costs more than it returns), a quote-aware fill likelihood."
  },
  {
   "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/README.md",
   "quote": "A table classifier on the filter's own features gets 18.8% against the lattice's 16.2%, so the error is near what those features allow. Not tried: choosing quotes against the whole lattice posterior instead of its mean and sd, per-run estimates of sigma and order rate inside the classifier, the router likelihood per price hypothesis."
  },
  {
   "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/README.md",
   "quote": "**What separates +4.26 from #1 is one call:** is the first trade of a step the arb or retail. The filter is wrong 16% of the time. With the true answer the same code scores +6.98."
  },
  {
   "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/README.md",
   "quote": "python3 -I real.py pol_mid.json plain:soft=1 qm:soft=1,qm=qm_k1e-5.json    # +2.828, +3.769"
  },
  {
   "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/README.md",
   "quote": "10000  448.261189  443.455598  443.522608  443.663520"
  },
  {
   "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/real.py",
   "quote": "SIG2 = 0.000945 ** 2"
  },
  {
   "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/real.py",
   "quote": "RHO = 0.26                       # P(a no-arb step's first own trade is retail on a given side)"
  },
  {
   "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/replica2.py",
   "quote": "lam = cr.uniform(0.6, 1.0)"
  },
  {
   "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/replica2.py",
   "quote": "sigma = cr.uniform(0.000882, 0.001008)"
  },
  {
   "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/replica2.py",
   "quote": "fb, fa = policy.on_trade(False, ax, ty, t, x1, y1, True, p, y2 / x2, 0)"
  },
  {
   "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/gates.py",
   "quote": "b = R.run(seed, real.RealPolicy(T, {'soft': 1.0}))['edge']"
  }
 ]
}