tom-wagner

Full Order Pipeline Overview

This pipeline turns market data and probabilistic forecasts into executable orders by fusing rating inputs, Monte Carlo simulations, and Kalshi order book intelligence into a single automated run.

1. Assemble Inputs – Pull team power ratings, load the win distribution tables published by Inpredict NFL to anchor game-level priors, and ingest the latest Kalshi win-total, exact-win, and futures order books.
2. Run Season Simulations – nfl/scripts/bash/simulate_win_totals_and_exact_wins_and_futures runs a league-wide Monte Carlo that samples the Inpredict odds, applies custom tie-breaker logic for division and conference standings, and stores outcome frequencies for every team and market tenor.
3. Summarize Outputs – The simulator exports distributions for season win totals, exact win counts, and futures probabilities, which become the quantitative inputs for pricing edges against Kalshi markets.
4. Refresh Position State – `scrip

tom-wagner

// const axios = require('axios');
// const cheerio = require('cheerio');
// const fs = require('fs')
// Set headers
const headers = {
    "accept": "*/*",
    "accept-language": "en-US,en;q=0.9,la;q=0.8",
    // "priority": "u=1, i",
    "sec-ch-ua": "\"Chromium\";v=\"130\", \"Google Chrome\";v=\"130\", \"Not?A_Brand\";v=\"99\"",
    "sec-ch-ua-mobile": "?0",

tom-wagner

# raw data as of 5:01pm EST on 10/31

raw_data = {'status': {'cd': 0, 'mg': 'successful', 'tp': 'A'},
            'value': {
    'L:101': {'so': 0, 'pl': [{'cd': '9', 'cn': 89, 'tc': 556}]},
    'L:103': {'so': 0, 'pl': [{'cd': '9', 'cn': 94, 'tc': 519}]},
    'L:105': {'so': 0, 'pl': [{'cd': '9', 'cn': 89, 'tc': 656}]},
    'L:107': {'so': 0, 'pl': [{'cd': '3', 'cn': 31, 'tc': 537}]},
    'L:108': {'so': 0, 'pl': [{'cd': '3', 'cn': 0, 'tc': 701}]},
    'L:109': {'so': 0, 'pl': [{'cd': '2', 'cn': 18, 'tc': 534},

tom-wagner

LIKELIHOODS = {0.0: 0.5, 0.1: 0.5, 0.2: 0.5, 0.3: 0.5, 0.4: 0.5, 0.5: 0.5, 0.6: 0.5, 0.7: 0.52, 0.8: 0.52, 0.9: 0.52,
               1.0: 0.52, 1.1: 0.52, 1.2: 0.52, 1.3: 0.52, 1.4: 0.52, 1.5: 0.52, 1.6: 0.52, 1.7: 0.52, 1.8: 0.52,
               1.9: 0.52, 2.0: 0.52, 2.1: 0.52, 2.2: 0.55, 2.3: 0.55, 2.4: 0.55, 2.5: 0.55, 2.6: 0.55, 2.7: 0.55,
               2.8: 0.55, 2.9: 0.55, 3.0: 0.55, 3.1: 0.55, 3.2: 0.55, 3.3: 0.58, 3.4: 0.58, 3.5: 0.58, 3.6: 0.58,
               3.7: 0.58, 3.8: 0.58, 3.9: 0.58, 4.0: 0.58, 4.1: 0.58, 4.2: 0.58, 4.3: 0.58, 4.4: 0.58, 4.5: 0.58,
               4.6: 0.58, 4.7: 0.58, 4.8: 0.61, 4.9: 0.61, 5.0: 0.61, 5.1: 0.61, 5.2: 0.61, 5.3: 0.61, 5.4: 0.61,
               5.5: 0.61, 5.6: 0.61, 5.7: 0.61, 5.8: 0.61, 5.9: 0.61, 6.0: 0.61, 6.1: 0.61, 6.2: 0.61, 6.3: 0.61,
               6.4: 0.64, 6.5: 0.64, 6.6: 0.64, 6.7: 0.64, 6.8: 0.64, 6.9: 0.64, 7.0: 0.64, 7.1: 0.64, 7.2: 0.64,
               7.3: 0.64, 7.4: 0.64, 7.5: 0.64, 7.6: 0.64, 7.7: 0.64, 7.8: 0.64, 7.9: 0.64, 8.0: 0.64, 8.1: 

tom-wagner

# Let's write some functions to explore set math a bit more.
# We're going to be using this COURSES dict in all of the examples. Don't change it, though!
# So, first, write a function named covers that accepts a single parameter, a set of topics.
# Have the function return a list of courses from COURSES where the supplied set and the course's value (also a set) overlap.
# For example, covers({"Python"}) would return ["Python Basics"].

COURSES = {
    "Python Basics": {"Python", "functions", "variables",
                      "booleans", "integers", "floats",
                      "arrays", "strings", "exceptions",

tom-wagner

import re

def rotate_if_letter(char):
    if re.search('[a-zA-Z]', char):
        return chr(ord(char) + 13) if re.search('[a-mA-M]', char) else chr(ord(char) - 13)
    return char

def rot13(message):
    return ''.join([rotate_if_letter(char) for char in message])

tom-wagner

// iterative solution --> time complexity O(width * height * height) aka n^3
const gridSum = (grid) => {
  const upsideDownGrid = grid.reverse();
  let maxSum = 0;

  for (let i = 0; i < upsideDownGrid.length; i++) {
    let currSum = 0;
    let maxForRow = 0;
    for (let j = 0; j < upsideDownGrid[i].length; j++) {
      if (upsideDownGrid[i][j] === 1) {

tom-wagner

const generateNextIteration = (arr, patterns) => {
  return arr.map((val, idx) => {
    const currentPattern = arr[idx - 2] + arr[idx - 1] + arr[idx] + arr[idx + 1] + arr[idx + 2];
    return patterns.hasOwnProperty(currentPattern) ? patterns[currentPattern] : '.';
  });
};

const pad50 = () => new Array(50).fill('.');

const addPadding = (arr) => [...pad50(), ...arr, ...pad50()];

tom-wagner

// let arr = [
//  { position: [9, 1], velocity: [0, 2] },
//  { position: [7, 0], velocity: [-1, 0] },
//  { position: [3, -2], velocity: [-1, 1] },
//  { position: [6, 10], velocity: [-2, -1] },
//  { position: [2, -4], velocity: [2, 2], },
//  { position: [-6, 10], velocity: [2, -2] },
//  { position: [1, 8], velocity: [1, -1] },
//  { position: [1, 7], velocity: [1, 0] },
//  { position: [-3, 11], velocity: [1, -2] },

tom-wagner

const polymer = (string) => {
  const arr = string.split('');
  const stack = [];
  for (let i = 0; i < arr.length; i++) {
    let lastStackVal = stack[stack.length - 1];
    let currOppositeCase = arr[i] === arr[i].toUpperCase() ? arr[i].toLowerCase() : arr[i].toUpperCase();
    if (lastStackVal === currOppositeCase) {
      stack.pop();
    } else {
      stack.push(arr[i]);