added more docs

marketmaker.ml

@@ -1,3 +1,11 @@
+(********************************************************************
+   Market Maker
+
+   The Market Maker module holds information about the MM's previous
+   bid asks spreads.  It also includes getters and setters and
+   basic statistics.  For more statistics, we draw from the statistics module.
+ ********************************************************************)
+
 open Pervasives
 open Sys
 

marketmaker.mli

@@ -1,11 +1,12 @@
-(** Marketmaker
+(******************************************************************
+                            Marketmaker
     Representation of the marketmaker - player - during the course of the game.
 
     This module keeps track of the marketmaker's bid/ask prices and the 
     transactions that take place between the player and traders. It contains 
     structs to hold this data and has functions to implement changes to and 
     display the data.
-*)
+ ********************************************************************)
 (** The abstract type of values representing adventures. *)
 type bidask = {
   trade_type : string;

parse.ml

@@ -1,3 +1,8 @@
+(********************************************************************
+   Parser
+
+   The parser module parses the "fermi.json" file and extracts its fields.
+ ********************************************************************)
 open Yojson.Basic.Util
 open String
 

stats.ml

@@ -9,6 +9,7 @@ open Marketmaker
   *    - Newton-Raphson Method for Curve approximation (Secant estimate)
   *    - Chebyschev Variance Probability Calculator
   *    - Markov Chain analysis using three features
+  *    - Graph Analysis
   *
   *             DATA ANALYSIS
   *    - Mean spread, bids, asks, trade count
@@ -17,6 +18,17 @@ open Marketmaker
   *     In implementaiton 1, the cheat command currently compares previous 
   *     bid/asks and uses a linear regression model to suggest the next move, 
   *     referencing the actual value.
+  *
+  *     In implementation II, the cheat command factors in a least-squares 
+  *    regression model to predict your next move.  It compares this to
+  *    the actual value of the dice roll.  Based off of your previous ask/bids
+  *    we apply a Gaussian blur matrix to this to find another average value.
+  *    We then use Chebyschev's inequality and the variance of your previous
+  *    moves to appropriately offset this second average from the expected 
+  *    rolls.  Finally, we incorporate the first average, subtract the two
+  *    and present that as a good guess for the next move.  
+  *    This is essentially the procedure a market maker would use to price 
+  *    the next bid/ask spread.
   *)
 
 (* 

trader.ml

@@ -1,3 +1,13 @@
+(********************************************************************
+   Trader 
+
+   The Trader module contains the trader type, getters and setters
+   as well as basic statistics analysis.  It also includes at least 
+   four AI methods which are used as the backend for trading opponents.
+   There is also a contention function which decides which trader
+   gets to trade if multiple traders wish to transact.
+ ********************************************************************)
+
 open Pervasives
 open Random