import spread import math import random o = spread.declutter(spread.load('diff_and_price.csv')) diffs = [float(q[2]) for q in o][::-1] prices = [float(q[1]) for q in o][::-1] def simple_estimator(fac): o = [1] for i in range(1, len(diffs)): o.append(o[-1] * diffs[i] * 1.0 / diffs[i-1] / fac) return o def minimax_estimator(fac): o = [1] for i in range(1, len(diffs)): if diffs[i] * 1.0 / diffs[i-1] > fac: o.append(o[-1] * diffs[i] * 1.0 / diffs[i-1] / fac) elif diffs[i] > diffs[i-1]: o.append(o[-1]) else: o.append(o[-1] * diffs[i] * 1.0 / diffs[i-1]) return o def diff_estimator(fac, dw, mf): o = [1] derivs = [0] * 14 for i in range(14, len(diffs)): derivs.append(diffs[i] - diffs[i - 14]) for i in range(0, 14): derivs[i] = derivs[14] vals = [max(diffs[i] + derivs[i] * dw, diffs[i] * mf) for i in range(len(diffs))] for i in range(1, len(diffs)): if vals[i] * 1.0 / vals[i-1] > fac: o.append(o[-1] * vals[i] * 1.0 / vals[i-1] / fac) elif vals[i] > vals[i-1]: o.append(o[-1]) else: o.append(o[-1] * vals[i] * 1.0 / vals[i-1]) return o def ndiff_estimator(*args): fac, dws, mf = args[0], args[1:-1], args[-1] o = [1] ds = [diffs] for dw in dws: derivs = [0] * 14 for i in range(14, len(diffs)): derivs.append(ds[-1][i] - ds[-1][i - 14]) for i in range(0, 14): derivs[i] = derivs[14] ds.append(derivs) vals = [] for i in range(len(diffs)): q = ds[0][i] + sum([ds[j+1][i] * dws[j] for j in range(len(dws))]) vals.append(max(q, ds[0][i] * mf)) for i in range(1, len(diffs)): if vals[i] * 1.0 / vals[i-1] > fac: o.append(o[-1] * vals[i] * 1.0 / vals[i-1] / fac) elif vals[i] > vals[i-1]: o.append(o[-1]) else: o.append(o[-1] * vals[i] * 1.0 / vals[i-1]) return o def dual_threshold_estimator(fac1, fac2, dmul): o = [1] derivs = [0] * 14 for i in range(14, len(diffs)): derivs.append(diffs[i] - diffs[i - 14]) for i in range(0, 14): derivs[i] = derivs[14] for i in range(1, len(diffs)): if diffs[i] * 1.0 / diffs[i-1] > fac1 and derivs[i] * 1.0 / derivs[i-1] > fac2: o.append(o[-1] * diffs[i] * 1.0 / diffs[i-1] / fac1 * (1 + (derivs[i] / derivs[i-1] - fac2) * dmul)) elif diffs[i] > diffs[i-1]: o.append(o[-1]) else: o.append(o[-1] * diffs[i] * 1.0 / diffs[i-1]) return o def evaluate_estimates(estimates, crossvalidate=False): sz = len(prices) if crossvalidate else 780 sqdiffsum = 0 # compute average tot = 0 for i in range(sz): tot += math.log(prices[i] / estimates[i]) avg = 2.718281828459 ** (tot * 1.0 / sz) for i in range(1, sz): sqdiffsum += math.log(prices[i] / estimates[i] / avg) ** 2 return sqdiffsum # Simulated annealing optimizer def optimize(producer, floors, ceilings, rate=0.7): vals = [f*0.5+c*0.5 for f, c in zip(floors, ceilings)] y = evaluate_estimates(producer(*vals)) for i in range(1, 5000): stepsizes = [(f*0.5-c*0.5) / i**rate for f, c in zip(floors, ceilings)] steps = [(random.random() * 2 - 1) * s for s in stepsizes] newvals = [max(mi, min(ma, v+s)) for v, s, mi, ma in zip(vals, steps, floors, ceilings)] newy = evaluate_estimates(producer(*newvals)) if newy < y: vals = newvals y = newy if not i % 1000: print i, vals, y return vals def score(producer, *vals): return evaluate_estimates(producer(*vals), True)