InformationTheory

InformationTheory.Shannon.Portfolio.StationaryWinftyAEP

source

Growing-memory W_∞ AEP for stationary markets (Cover–Thomas) #

The growing-memory log-wealth average growingMemoryLogAvg converges almost surely to the infinite-past optimal growth rate W_∞ = condOptGrowthInfty (growingMemory_logWealth_tendsto_condOptGrowthInfty). The proof is the Algoet–Cover sandwich: the eventual upper bound ≤ W_∞ + ε from the wealth-ratio supermartingale against the infinite-past optimal portfolio, and the eventual lower bound ≥ W_∞ − ε from the finite-memory Birkhoff rates rising to W_∞. Split out of StationaryWinfty.lean (measurable selection, monotone convergence, and the conditional Kuhn–Tucker gateway) to keep each file under the size budget.

noncomputable def

InformationTheory.Shannon.Portfolio.growingMemoryLogAvg

source
{Ω : Type u_1} {m : } (X : ΩFin m) (bstar : ΩFin m) (T : ΩΩ) (n : ) (ω : Ω) :

Growing-memory log-wealth average (Cover–Thomas): the time average up to horizon n of the per-epoch log returns of the stagewise conditional log-optimal portfolios bstar i along the shift orbit of T. At epoch i the causal strategy uses the i-past optimal portfolio bstar i, so growingMemoryLogAvg X bstar T n ω = (1/(n+1)) log S*_n where S*_n = ∏ᵢ (bstar i · Xᵢ) is the growing-memory wealth. This is the object whose limsup is bounded above by W_∞ (upper half of the Algoet–Cover sandwich) and whose liminf is bounded below by each finite-memory rate.

Equations
Instances For
    Used by
      theorem

      InformationTheory.Shannon.Portfolio.growingMemory_eventually_le_condOptGrowthInfty

      source
      {Ω : Type u_1} {m0 : MeasurableSpace Ω} {m : } [StandardBorelSpace Ω] [Nonempty Ω] (μ : MeasureTheory.Measure Ω) [MeasureTheory.IsProbabilityMeasure μ] {T : ΩΩ} (hT : MeasureTheory.MeasurePreserving T μ μ) (hT_erg : Ergodic T μ) ( : MeasureTheory.Filtration m0) (X : ΩFin m) [Nonempty (Fin m)] (hX : Measurable X) (hpos : ∀ (ω : Ω), bstdSimplex (Fin m), 0 < j : Fin m, b j * X ω j) (hint : ∀ (c : ΩFin m), Measurable c(∀ (ω : Ω), c ω stdSimplex (Fin m))MeasureTheory.Integrable (causalLogReturn X c) μ) (bstar : ΩFin m) (hbstar_meas : ∀ (k : ), MeasureTheory.StronglyMeasurable (bstar k)) (hbstar_simplex : ∀ (k : ) (ω : Ω), bstar k ω stdSimplex (Fin m)) (hbstar_dom : ∀ (k : ) (c : ΩFin m), MeasureTheory.StronglyMeasurable c(∀ (ω : Ω), c ω stdSimplex (Fin m))μ[causalLogReturn X c | k] ≤ᵐ[μ] μ[causalLogReturn X (bstar k) | k]) (bstarInf : ΩFin m) (hInf_meas : MeasureTheory.StronglyMeasurable bstarInf) (hInf_simplex : ∀ (ω : Ω), bstarInf ω stdSimplex (Fin m)) (hint_coord : ∀ (i : Fin m), MeasureTheory.Integrable (fun (ω : Ω) => X ω i / j : Fin m, bstarInf ω j * X ω j) μ) (hInf_dom : ∀ (c : ΩFin m), MeasureTheory.StronglyMeasurable c(∀ (ω : Ω), c ω stdSimplex (Fin m))μ[causalLogReturn X c | ⨆ (j : ), j] ≤ᵐ[μ] μ[causalLogReturn X bstarInf | ⨆ (j : ), j]) (hcoh : ∀ (k i : ), i k(MeasureTheory.StronglyMeasurable fun (ω : Ω) => X (T^[i] ω)) (MeasureTheory.StronglyMeasurable fun (ω : Ω) => bstar i (T^[i] ω)) MeasureTheory.StronglyMeasurable fun (ω : Ω) => bstarInf (T^[i] ω)) :
      ∀ᵐ (ω : Ω) μ, ∀ (ε : ), 0 < ε∀ᶠ (n : ) in Filter.atTop, growingMemoryLogAvg X bstar T n ω condOptGrowthInfty μ X bstar + ε

      Upper half of the growing-memory W_∞ AEP (Cover–Thomas): the growing-memory log-wealth average is eventually below W_∞ = condOptGrowthInfty up to any margin ε, almost surely. The proof decomposes growingMemoryLogAvg n = (1/n) log Mₙ + (1/n) ∑ᵢ log(bstarInf · Xᵢ): the first term is eventually below any positive threshold (wealthRatio_logAvg_eventually_le, from the supermartingale integral bound) and the second converges to ∫ log(bstarInf · X) = W_∞ by Birkhoff's ergodic theorem (birkhoff_ergodic_ae) and the gateway identity condOptGrowthInfty_eq_integral_infPast. The eventual-upper-bound form is the honest content of limsup ≤ W_∞; combined with the Birkhoff lower half it yields the almost-sure convergence (the -limsup value is junk on paths where the growing memory underperforms to −∞, which only the lower half rules out). bstar/bstarInf and their conditional-dominance properties (hbstar_dom/hInf_dom) are received as the stagewise/infinite-past conditional log-optimal selections (constructed separately, e.g. via exists_condLogOptimalSeq / exists_infPast_condLogOptimal); the remaining hypotheses are market-regularity/ergodicity preconditions. hcoh is the shift/past coherence (measurability only) letting the wealth-ratio supermartingale bound pull the growing history out of the epoch-k+1 conditional expectation; it holds for the concrete past-filtration/shift instantiation.

      Used by
        theorem

        InformationTheory.Shannon.Portfolio.growingMemory_eventually_ge_condOptGrowthInfty

        source
        {Ω : Type u_1} {m0 : MeasurableSpace Ω} {m : } [StandardBorelSpace Ω] [Nonempty Ω] (μ : MeasureTheory.Measure Ω) [MeasureTheory.IsProbabilityMeasure μ] {T : ΩΩ} (hT : MeasureTheory.MeasurePreserving T μ μ) (hT_erg : Ergodic T μ) ( : MeasureTheory.Filtration m0) (X : ΩFin m) [Nonempty (Fin m)] (hX : Measurable X) (hpos : ∀ (ω : Ω), bstdSimplex (Fin m), 0 < j : Fin m, b j * X ω j) (hint : ∀ (c : ΩFin m), Measurable c(∀ (ω : Ω), c ω stdSimplex (Fin m))MeasureTheory.Integrable (causalLogReturn X c) μ) (bstar : ΩFin m) (hbstar_meas : ∀ (k : ), MeasureTheory.StronglyMeasurable (bstar k)) (hbstar_simplex : ∀ (k : ) (ω : Ω), bstar k ω stdSimplex (Fin m)) (hbstar_dom : ∀ (k : ) (c : ΩFin m), MeasureTheory.StronglyMeasurable c(∀ (ω : Ω), c ω stdSimplex (Fin m))μ[causalLogReturn X c | k] ≤ᵐ[μ] μ[causalLogReturn X (bstar k) | k]) (hint_coord : ∀ (i : ) (coord : Fin m), MeasureTheory.Integrable (fun (ω : Ω) => X ω coord / j : Fin m, bstar i ω j * X ω j) μ) (hUB : ∃ (C : ), ∀ (c : ΩFin m), (∀ (ω : Ω), c ω stdSimplex (Fin m))MeasureTheory.Integrable (causalLogReturn X c) μ (ω : Ω), causalLogReturn X c ω μ C) (hcoh : ∀ (K k i : ), K ii k(MeasureTheory.StronglyMeasurable fun (ω : Ω) => X (T^[i] ω)) (MeasureTheory.StronglyMeasurable fun (ω : Ω) => bstar i (T^[i] ω)) MeasureTheory.StronglyMeasurable fun (ω : Ω) => bstar K (T^[i] ω)) :
        ∀ᵐ (ω : Ω) μ, ∀ (ε : ), 0 < ε∀ᶠ (n : ) in Filter.atTop, condOptGrowthInfty μ X bstar - ε growingMemoryLogAvg X bstar T n ω

        Lower half of the growing-memory W_∞ AEP (Cover–Thomas): the growing-memory log-wealth average is eventually above W_∞ = condOptGrowthInfty down to any margin ε, almost surely. For a fixed finite memory K, the growing-memory returns dominate the fixed-K strategy up to the lower wealth ratio: growingMemoryLogAvg n = (1/n) ∑ᵢ log(bstar K · Xᵢ) + (head)/n − (1/n) log Nₙ^{(K)}, where the first term converges to ∫ log(bstar K · X) = condOptGrowth K = W*_K by Birkhoff's ergodic theorem, the head (a finite ω-constant) vanishes, and (1/n) log Nₙ^{(K)} is eventually below any positive threshold (logAvg_eventually_le_of_lintegral_le_one, from the lower-ratio supermartingale bound lowerRatioProcess_lintegral_le_one). Since W*_K ↑ W_∞ (condOptGrowth_monotone + condOptGrowth_bddAbove, the monotone convergence for this bstar), choosing K with W*_K > W_∞ − ε/2 yields growingMemoryLogAvg n ≥ W_∞ − ε eventually. W_∞ is not received as a hypothesis: it is pinned constructively as the supremum of the W*_K, and the K for each ε is chosen deterministically (ω-independent) from the monotone limit, so the almost-sure set is the countable intersection over K ∈ ℕ of the Birkhoff and lower-ratio a.e. sets. hcoh is the shift/past coherence (measurability only). Combined with the upper half (growingMemory_eventually_le_condOptGrowthInfty) this gives almost-sure convergence to W_∞.

        Used by
          theorem

          InformationTheory.Shannon.Portfolio.growingMemory_logWealth_tendsto_condOptGrowthInfty

          source
          {Ω : Type u_1} {m0 : MeasurableSpace Ω} {m : } [StandardBorelSpace Ω] [Nonempty Ω] (μ : MeasureTheory.Measure Ω) [MeasureTheory.IsProbabilityMeasure μ] {T : ΩΩ} (hT : MeasureTheory.MeasurePreserving T μ μ) (hT_erg : Ergodic T μ) ( : MeasureTheory.Filtration m0) (X : ΩFin m) [Nonempty (Fin m)] (hX : Measurable X) (hpos : ∀ (ω : Ω), bstdSimplex (Fin m), 0 < j : Fin m, b j * X ω j) (hint : ∀ (c : ΩFin m), Measurable c(∀ (ω : Ω), c ω stdSimplex (Fin m))MeasureTheory.Integrable (causalLogReturn X c) μ) (bstar : ΩFin m) (hbstar_meas : ∀ (k : ), MeasureTheory.StronglyMeasurable (bstar k)) (hbstar_simplex : ∀ (k : ) (ω : Ω), bstar k ω stdSimplex (Fin m)) (hbstar_dom : ∀ (k : ) (c : ΩFin m), MeasureTheory.StronglyMeasurable c(∀ (ω : Ω), c ω stdSimplex (Fin m))μ[causalLogReturn X c | k] ≤ᵐ[μ] μ[causalLogReturn X (bstar k) | k]) (hint_coord : ∀ (i : ) (coord : Fin m), MeasureTheory.Integrable (fun (ω : Ω) => X ω coord / j : Fin m, bstar i ω j * X ω j) μ) (hUB : ∃ (C : ), ∀ (c : ΩFin m), (∀ (ω : Ω), c ω stdSimplex (Fin m))MeasureTheory.Integrable (causalLogReturn X c) μ (ω : Ω), causalLogReturn X c ω μ C) (hcoh : ∀ (K k i : ), K ii k(MeasureTheory.StronglyMeasurable fun (ω : Ω) => X (T^[i] ω)) (MeasureTheory.StronglyMeasurable fun (ω : Ω) => bstar i (T^[i] ω)) MeasureTheory.StronglyMeasurable fun (ω : Ω) => bstar K (T^[i] ω)) (bstarInf : ΩFin m) (hInf_meas : MeasureTheory.StronglyMeasurable bstarInf) (hInf_simplex : ∀ (ω : Ω), bstarInf ω stdSimplex (Fin m)) (hint_coord_inf : ∀ (i : Fin m), MeasureTheory.Integrable (fun (ω : Ω) => X ω i / j : Fin m, bstarInf ω j * X ω j) μ) (hInf_dom : ∀ (c : ΩFin m), MeasureTheory.StronglyMeasurable c(∀ (ω : Ω), c ω stdSimplex (Fin m))μ[causalLogReturn X c | ⨆ (j : ), j] ≤ᵐ[μ] μ[causalLogReturn X bstarInf | ⨆ (j : ), j]) (hcoh_inf : ∀ (k i : ), i k(MeasureTheory.StronglyMeasurable fun (ω : Ω) => X (T^[i] ω)) (MeasureTheory.StronglyMeasurable fun (ω : Ω) => bstar i (T^[i] ω)) MeasureTheory.StronglyMeasurable fun (ω : Ω) => bstarInf (T^[i] ω)) :
          ∀ᵐ (ω : Ω) μ, Filter.Tendsto (fun (n : ) => growingMemoryLogAvg X bstar T n ω) Filter.atTop (nhds (condOptGrowthInfty μ X bstar))

          Growing-memory W_∞ AEP (Cover–Thomas): the growing-memory log-wealth average converges almost surely to the infinite-past optimal growth rate W_∞ = condOptGrowthInfty. This is the Algoet–Cover sandwich: the eventual upper bound ≤ W_∞ + ε (growingMemory_eventually_le_condOptGrowthInfty, from the wealth-ratio supermartingale against the infinite-past optimal bstarInf) and the eventual lower bound ≥ W_∞ − ε (growingMemory_eventually_ge_condOptGrowthInfty, from the fixed-memory Birkhoff rates rising to W_∞) pinch the average to W_∞. bstar/bstarInf are the stagewise/infinite-past conditional log-optimal selections (constructed separately via exists_condLogOptimalSeq / exists_infPast_condLogOptimal); the remaining hypotheses are market-regularity/ergodicity preconditions and the two measurability-only shift/past coherences (hcoh_inf at ⨆ⱼℱⱼ for the upper half, hcoh at each ℱ (k+1) for the lower).

          Used by