cw1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First Attempt: market setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial Setup:\n",
    "# Import all the libraries we need\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import csv\n",
    "import math\n",
    "import random\n",
    "\n",
    "from BSE import market_session\n",
    "\n",
    "# The next are helper functions that you will use later, if they don't make \n",
    "# much sense now, don't worry too much about it they will become clearer later:\n",
    "\n",
    "# Use this to plot trades of a single experiment\n",
    "def plot_trades(trial_id):\n",
    "    prices_fname = trial_id + '_tape.csv'\n",
    "    x = np.empty(0)\n",
    "    y = np.empty(0)\n",
    "    with open(prices_fname, newline='') as csvfile:\n",
    "        reader = csv.reader(csvfile)\n",
    "        for row in reader:\n",
    "            time = float(row[1])\n",
    "            price = float(row[2])\n",
    "            x = np.append(x,time)\n",
    "            y = np.append(y,price)\n",
    "\n",
    "    plt.plot(x, y, 'x', color='black') \n",
    "    \n",
    "# Use this to run an experiment n times and plot all trades\n",
    "def n_runs_plot_trades(n, trial_id, start_time, end_time, traders_spec, order_sched):\n",
    "    x = np.empty(0)\n",
    "    y = np.empty(0)\n",
    "\n",
    "    for i in range(n):\n",
    "        trialId = trial_id + '_' + str(i)\n",
    "        tdump = open(trialId + '_avg_balance.csv','w')\n",
    "\n",
    "        market_session(trialId, start_time, end_time, traders_spec, order_sched, tdump, True, False)\n",
    "        \n",
    "        tdump.close()\n",
    "\n",
    "        with open(trialId + '_tape.csv', newline='') as csvfile:\n",
    "            reader = csv.reader(csvfile)\n",
    "            for row in reader:\n",
    "                time = float(row[1])\n",
    "                price = float(row[2])\n",
    "                x = np.append(x,time)\n",
    "                y = np.append(y,price)\n",
    "\n",
    "    plt.plot(x, y, 'x', color='black');\n",
    "\n",
    "# !!! Don't use on it's own   \n",
    "def getorderprice(i, sched, n, mode):\n",
    "    pmin = min(sched[0][0], sched[0][1])\n",
    "    pmax = max(sched[0][0], sched[0][1])\n",
    "    prange = pmax - pmin\n",
    "    stepsize = prange / (n - 1)\n",
    "    halfstep = round(stepsize / 2.0)\n",
    "\n",
    "    if mode == 'fixed':\n",
    "        orderprice = pmin + int(i * stepsize)\n",
    "    elif mode == 'jittered':\n",
    "        orderprice = pmin + int(i * stepsize) + random.randint(-halfstep, halfstep)\n",
    "    elif mode == 'random':\n",
    "        if len(sched) > 1:\n",
    "            # more than one schedule: choose one equiprobably\n",
    "            s = random.randint(0, len(sched) - 1)\n",
    "            pmin = min(sched[s][0], sched[s][1])\n",
    "            pmax = max(sched[s][0], sched[s][1])\n",
    "        orderprice = random.randint(pmin, pmax)\n",
    "    return orderprice    \n",
    "\n",
    "# !!! Don't use on it's own\n",
    "def make_supply_demand_plot(bids, asks):\n",
    "    # total volume up to current order\n",
    "    volS = 0\n",
    "    volB = 0\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    plt.ylabel('Price')\n",
    "    plt.xlabel('Quantity')\n",
    "    \n",
    "    pr = 0\n",
    "    for b in bids:\n",
    "        if pr != 0:\n",
    "            # vertical line\n",
    "            ax.plot([volB,volB], [pr,b], 'r-')\n",
    "        # horizontal lines\n",
    "        line, = ax.plot([volB,volB+1], [b,b], 'r-')\n",
    "        volB += 1\n",
    "        pr = b\n",
    "    if bids:\n",
    "        line.set_label('Demand')\n",
    "        \n",
    "    pr = 0\n",
    "    for s in asks:\n",
    "        if pr != 0:\n",
    "            # vertical line\n",
    "            ax.plot([volS,volS], [pr,s], 'b-')\n",
    "        # horizontal lines\n",
    "        line, = ax.plot([volS,volS+1], [s,s], 'b-')\n",
    "        volS += 1\n",
    "        pr = s\n",
    "    if asks:\n",
    "        line.set_label('Supply')\n",
    "        \n",
    "    if bids or asks:\n",
    "        plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "# Use this to plot supply and demand curves from supply and demand ranges and stepmode\n",
    "def plot_sup_dem(seller_num, sup_ranges, buyer_num, dem_ranges, stepmode):\n",
    "    asks = []\n",
    "    for s in range(seller_num):\n",
    "        asks.append(getorderprice(s, sup_ranges, seller_num, stepmode))\n",
    "    asks.sort()\n",
    "    bids = []\n",
    "    for b in range(buyer_num):\n",
    "        bids.append(getorderprice(b, dem_ranges, buyer_num, stepmode))\n",
    "    bids.sort()\n",
    "    bids.reverse()\n",
    "    \n",
    "    make_supply_demand_plot(bids, asks) \n",
    "\n",
    "# plot sorted trades, useful is some situations - won't be used in this worksheet\n",
    "def in_order_plot(trial_id):\n",
    "    prices_fname = trial_id + '_tape.csv'\n",
    "    y = np.empty(0)\n",
    "    with open(prices_fname, newline='') as csvfile:\n",
    "        reader = csv.reader(csvfile)\n",
    "        for row in reader:\n",
    "            price = float(row[2])\n",
    "            y = np.append(y,price)\n",
    "    y = np.sort(y)\n",
    "    x = list(range(len(y)))\n",
    "\n",
    "    plt.plot(x, y, 'x', color='black')   \n",
    "\n",
    "# plot offset function\n",
    "def plot_offset_fn(offset_fn, total_time_seconds):   \n",
    "    x = list(range(total_time_seconds))\n",
    "    offsets = []\n",
    "    for i in range(total_time_seconds):\n",
    "        offsets.append(offset_fn(i))\n",
    "    plt.plot(x, offsets, 'x', color='black')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Static Market Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, configure the trader specifications\n",
    "sellers_spec = [('PRDE', 30, {'k': 4, 'f_value':0.8, 's_min': -1.0, 's_max': +1.0}),]\n",
    "buyers_spec = sellers_spec\n",
    "\n",
    "traders_spec = {'sellers': sellers_spec, 'buyers': buyers_spec}\n",
    "\n",
    "# Next, confiure the supply and demand (and plot it)\n",
    "sup_range = (60,140)\n",
    "dem_range = (60,140)\n",
    "\n",
    "plot_sup_dem(10, [sup_range], 10, [dem_range], 'fixed')\n",
    "\n",
    "# Next, configure order schedules\n",
    "start_time = 0\n",
    "hours=48\n",
    "end_time = 60*60*hours\n",
    "supply_schedule = [{'from': start_time, 'to': end_time, 'ranges': [sup_range], 'stepmode': 'fixed'}]\n",
    "demand_schedule = [{'from': start_time, 'to': end_time, 'ranges': [dem_range], 'stepmode': 'fixed'}]\n",
    "\n",
    "order_interval = 10\n",
    "order_sched = {'sup': supply_schedule, 'dem': demand_schedule, 'interval': 10, 'timemode': 'drip-poisson'}\n",
    "\n",
    "trial_id = 'prde'\n",
    "tdump = open('prde_avg_balance.csv','w')\n",
    "dump_all = True\n",
    "verbose = True\n",
    "\n",
    "# Now, run the market session\n",
    "market_session(trial_id, start_time, end_time, traders_spec, order_sched, tdump, dump_all, verbose)\n",
    "\n",
    "tdump.close()\n",
    "\n",
    "# Finally, plot the trades that executed during the market session\n",
    "plot_trades('prde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dynamic Market Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sellers_spec = [('PRDE', 30, {'k': 4, 's_min': -1.0, 's_max': +1.0}),\n",
    "                ('PRDE', 30, {'k': 4, 's_min': -1.0, 's_max': +1.0})]\n",
    "buyers_spec = sellers_spec\n",
    "\n",
    "range1 = (10, 190, schedule_offsetfn)\n",
    "range2 = (200, 300, schedule_offsetfn)\n",
    "\n",
    "# Here is an example of how to switch from range1 to range2 and then back to range1,\n",
    "# introducing two \"market shocks\"\n",
    "# -- here the timings of the shocks are at 1/3 and 2/3 into the duration of the session.\n",
    "\n",
    "supply_schedule = [ {'from':start_time, 'to':duration/3, 'ranges':[range1], 'stepmode':'fixed'},\n",
    "                    {'from':duration/3, 'to':2*duration/3, 'ranges':[range2], 'stepmode':'fixed'},\n",
    "                    {'from':2*duration/3, 'to':end_time, 'ranges':[range1], 'stepmode':'fixed'}\n",
    "                  ]\n",
    "demand_schedule = supply_schedule\n",
    "\n",
    " # The code below sets up flat (perfectly elastic) supply and demand curves at prices of 50 and 150, P0=100\n",
    "\n",
    "    #range1 = (60, 60)\n",
    "    #supply_schedule = [{'from': start_time, 'to': end_time, 'ranges': [range1], 'stepmode': 'fixed'}]\n",
    "\n",
    "    #range2 = (140, 140)\n",
    "    #demand_schedule = [{'from': start_time, 'to': end_time, 'ranges': [range2], 'stepmode': 'fixed'}]\n",
    "\n",
    "    # new customer orders arrive at each trader approx once every order_interval seconds\n",
    "order_interval = 15\n",
    "\n",
    "order_sched = {'sup': supply_schedule, 'dem': demand_schedule,\n",
    "               'interval': order_interval, 'timemode': 'drip-poisson'}\n",
    "\n",
    "trial_id = 'test_5'\n",
    "tdump = open('test_5_avg_balance.csv','w')\n",
    "\n",
    "market_session(trial_id, start_time, end_time, traders_spec, order_sched, tdump, True, False)\n",
    "\n",
    "tdump.close()\n",
    "\n",
    "plot_trades('test_5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running Homogenous Trials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#trial that runs a session for all k (4,12) and for all f_value(0,2)\n",
    "trail_id = 'all_k_to_all_f'\n",
    "fname = trail_id + '_balances.csv'\n",
    "\n",
    "tdump = open(fname, 'w')\n",
    "\n",
    "for k in range(4,12):\n",
    "    for f in np.arange(0.0,2.0,0.1):\n",
    "        buyers_spec = [('PRDE', 30, {'k': k, 'f_value':f, 's_min': -1.0, 's_max': +1.0})] \n",
    "        sellers_spec = buyers_spec\n",
    "        traders_spec = {'sellers': sellers_spec, 'buyers': buyers_spec}\n",
    "\n",
    "        trial = 1\n",
    "        while trial <= 1:\n",
    "            trial_id = '%02d_%02f%s' % (k, f, trail_id)\n",
    "            market_session(trial_id, start_time, end_time, traders_spec,\n",
    "                           order_sched, tdump, False, True)\n",
    "            tdump.flush()\n",
    "            trial+=1\n",
    "\n",
    "tdump.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-268-9fd2eedaf591>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     17\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[0mtrial\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m             \u001b[0mtrial_id\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'%02d_%02f'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m             market_session(trial_id, start_time, end_time, traders_spec,\n\u001b[0m\u001b[0;32m     20\u001b[0m                            order_sched, tdump, False, True)\n\u001b[0;32m     21\u001b[0m             \u001b[0mtdump\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\UNI\\GitHub\\fintech_cw_2022\\BSE.py\u001b[0m in \u001b[0;36mmarket_session\u001b[1;34m(sess_id, starttime, endtime, trader_spec, order_schedule, avg_bals, dump_all, verbose)\u001b[0m\n\u001b[0;32m   1982\u001b[0m             \u001b[1;31m# send order to exchange\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1983\u001b[0m             \u001b[0mtraders\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtid\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_quotes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1984\u001b[1;33m             \u001b[0mtrade\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexchange\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprocess_order2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprocess_verbose\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1985\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mtrade\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1986\u001b[0m                 \u001b[1;31m# trade occurred,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\UNI\\GitHub\\fintech_cw_2022\\BSE.py\u001b[0m in \u001b[0;36mprocess_order2\u001b[1;34m(self, time, order, verbose)\u001b[0m\n\u001b[0;32m    282\u001b[0m         \u001b[0moprice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprice\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    283\u001b[0m         \u001b[0mcounterparty\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 284\u001b[1;33m         \u001b[1;33m[\u001b[0m\u001b[0mqid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_order\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# add it to the order lists -- overwriting any previous order\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    285\u001b[0m         \u001b[0morder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mqid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mqid\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    286\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\UNI\\GitHub\\fintech_cw_2022\\BSE.py\u001b[0m in \u001b[0;36madd_order\u001b[1;34m(self, order, verbose)\u001b[0m\n\u001b[0;32m    236\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbids\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_tid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbids\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlob\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mbest_price\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    237\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 238\u001b[1;33m             \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook_add\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    239\u001b[0m             \u001b[0mbest_price\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlob_anon\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    240\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_price\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbest_price\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\UNI\\GitHub\\fintech_cw_2022\\BSE.py\u001b[0m in \u001b[0;36mbook_add\u001b[1;34m(self, order)\u001b[0m\n\u001b[0;32m    151\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m         \u001b[1;31m# if this is an ask, does the price set a new extreme-high record?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 153\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbooktype\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'Ask'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msession_extreme\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprice\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msession_extreme\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    154\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msession_extreme\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    155\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# trial where we keep k set to a specific number and evaluate all f (0,2)\n",
    "# (k determined previously running expermienets on which k is the best for f_value = 0.8)\n",
    "\n",
    "trail_id = 'set_k_to_all_f'\n",
    "fname = trail_id + 'balances.csv'\n",
    "\n",
    "tdump = open(fname, 'w')\n",
    "\n",
    "for k in range(4):\n",
    "    for f in np.arange(0.0,1.0,0.1):\n",
    "        buyers_spec = [('PRDE', 30, {'k': k, 'f_value':f, 's_min': -1.0, 's_max': +1.0})] \n",
    "        sellers_spec = buyers_spec\n",
    "        traders_spec = {'sellers': sellers_spec, 'buyers': buyers_spec}\n",
    "\n",
    "        trial = 1\n",
    "        while trial <= 1:\n",
    "            trial_id = '%02d_%02f' % (k, f)\n",
    "            market_session(trial_id, start_time, end_time, traders_spec,\n",
    "                           order_sched, tdump, False, True)\n",
    "            tdump.flush()\n",
    "            trial+=1\n",
    "\n",
    "tdump.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65315.0\n",
      "53245.0\n",
      "90.71527777777777\n",
      "73.95138888888889\n"
     ]
    }
   ],
   "source": [
    "# open blotters.csv\n",
    "# seperate buyers and sellers traders\n",
    "# for each buyers, add the (trade price + limit price(140))\n",
    "# for each seller, add the (trade price + limit price(60))\n",
    "\n",
    "import pandas as pd\n",
    "prices_fname = trial_id + '_blotters.csv'\n",
    "df = pd.read_csv(prices_fname, names=['trader_id', 'num_trades', 'time', 'traded_price', 'trader_1','trader_2', 'quantity'])\n",
    "\n",
    "s_sum = 0\n",
    "b_sum = 0\n",
    "total_sum = 0\n",
    "trader_num = len(df.index)//2\n",
    "\n",
    "df['traded_price'] = df['traded_price'].fillna(0)\n",
    "\n",
    "for i in range(trader_num):\n",
    "    price = df.loc[i]['traded_price']\n",
    "    total_sum += price\n",
    "    s_sum += abs(price-60)\n",
    "    b_sum += abs(140-price)\n",
    "\n",
    "print(s_sum)\n",
    "print(b_sum)\n",
    "\n",
    "def calculate_pps(s_sum, b_sum, time_p):\n",
    "    print(s_sum/time_p)\n",
    "    print(b_sum/time_p)\n",
    "    \n",
    "    \n",
    "calculate_pps(s_sum, b_sum, 720)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the moving average of a single trail\n",
    "df = pd.read_csv('prde_strats.csv',header=None,)\n",
    "\n",
    "buyer_avg = []\n",
    "seller_avg = []\n",
    "total_avg = []\n",
    "\n",
    "i=0\n",
    "for row in range(0,len(df.index)):\n",
    "    b_sum = 0\n",
    "    s_sum = 0\n",
    "    for col in range (8, 219 ,7):\n",
    "        b_sum +=df.loc[row][col]\n",
    "        \n",
    "    for col in range (225, 428,7):\n",
    "        s_sum +=df.loc[row][col]\n",
    "    \n",
    "    if(i!=0):\n",
    "        buyer_avg.append(b_sum + buyer_avg[i-1])\n",
    "        seller_avg.append(s_sum + seller_avg[i-1])\n",
    "        total_avg.append(b_sum + s_sum + total_avg[i-1])\n",
    "    else:\n",
    "        buyer_avg.append(b_sum)\n",
    "        seller_avg.append(s_sum)\n",
    "        total_avg.append(b_sum + s_sum)\n",
    "    i+=1\n",
    "    \n",
    "moving_avg_b = []\n",
    "moving_avg_s = []\n",
    "moving_avg_t = []\n",
    "\n",
    "for i in range(1, len(buyer_avg)+1):\n",
    "    moving_avg_b.append(buyer_avg[i-1]/i)\n",
    "    moving_avg_s.append(seller_avg[i-1]/i)\n",
    "    moving_avg_t.append(total_avg[i-1]/i)\n",
    "\n",
    "t_period = np.linspace(0,hours,num=hours)\n",
    "plt.plot(t_period, moving_avg_t, color='black',linestyle='dotted', marker='x') \n",
    "plt.plot(t_period, moving_avg_b, color='red', linestyle='dotted', marker='x') \n",
    "plt.plot(t_period, moving_avg_s, color='blue', linestyle='dotted', marker='x') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        , 18.8300515 , 12.55336767, 19.710929  , 24.0891322 ,\n",
       "       27.42139417, 29.793134  , 31.72520712, 33.20739878, 33.4750366 ,\n",
       "       33.69379836, 34.78588192, 35.69007077, 36.52917936, 37.27262647,\n",
       "       37.55478638, 37.79740971, 38.32039217, 38.78137511, 38.89532665,\n",
       "       39.0069479 , 39.20316627, 39.38617183, 39.7158645 , 40.00792664,\n",
       "       40.27075142, 40.51553922, 40.73152286, 40.93446786, 41.0432453 ,\n",
       "       41.14810058, 41.30540422, 41.46061245, 41.55739947, 41.64635046,\n",
       "       41.70804111, 41.77648938, 41.98051218, 42.17098449, 42.16752525,\n",
       "       42.16755329, 42.07622307, 41.99514098, 42.09890434, 42.19681564,\n",
       "       42.28486815, 42.36619483, 42.41965787])"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculating the moving average of pps for one trail - function\n",
    "\n",
    "def moving_avg(trader_avg):\n",
    "    trader_mov_avg = []\n",
    "    \n",
    "    for i in range(1, len(trader_avg)+1):\n",
    "        trader_mov_avg.append(trader_avg[i-1]/i)\n",
    "        \n",
    "    return trader_mov_avg\n",
    "    \n",
    "def calculate_plot_ppc(file_name, hours):\n",
    "    df = pd.read_csv(file_name+'_strats.csv',header=None)\n",
    "    buyer_avg = []\n",
    "    seller_avg = []\n",
    "    \n",
    "    i=0\n",
    "    for row in range(0,len(df.index)):\n",
    "        b_sum = 0\n",
    "        s_sum = 0\n",
    "        for col in range (8, 219 ,7):\n",
    "            b_sum +=df.loc[row][col]\n",
    "\n",
    "        for col in range (225, 428,7):\n",
    "            s_sum +=df.loc[row][col]\n",
    "\n",
    "        if(i!=0):\n",
    "            buyer_avg.append(b_sum + buyer_avg[i-1])\n",
    "            seller_avg.append(s_sum + seller_avg[i-1])\n",
    "        else:\n",
    "            buyer_avg.append(b_sum)\n",
    "            seller_avg.append(s_sum)\n",
    "        i+=1\n",
    "\n",
    "    b_moving_avg = moving_avg(buyer_avg)\n",
    "    s_moving_avg = moving_avg(seller_avg)\n",
    "    t_moving_avg = np.add(b_moving_avg,s_moving_avg)\n",
    "\n",
    "#     t_period = list(range(0,hours))\n",
    "#     plt.plot(t_period, t_moving_avg, color='black',linestyle='dotted') \n",
    "#     plt.plot(t_period, b_moving_avg, color='red', linestyle='dotted') \n",
    "#     plt.plot(t_period, s_moving_avg, color='blue', linestyle='dotted') \n",
    "#     plt.show()\n",
    "    return t_moving_avg\n",
    "    \n",
    "calculate_plot_ppc('prde_0', hours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the moving average of a multiple trails\n",
    "\n",
    "def process_multiple_ppc(num_trails, hours):\n",
    "    colors = ['black', 'pink', 'red', 'green', 'blue']\n",
    "    all_trail_t_mov_avg = []\n",
    "    \n",
    "    for trial in list(range(1,num_trails)):\n",
    "        trail_t_mov_avg = calculate_plot_ppc('prde_%d'%trial, hours)\n",
    "        all_trail_t_mov_avg.append(trail_t_mov_avg)\n",
    "        \n",
    "        t_period = list(range(0,hours))\n",
    "        plt.plot(t_period, trail_t_mov_avg, color=colors[trial],linestyle='dotted') \n",
    "        \n",
    "    plt.show()\n",
    "    return(all_trail_t_mov_avg)\n",
    "\n",
    "all_trail_t_mov_avg = process_multiple_ppc(5, 48)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 184.06145319381406\n"
     ]
    }
   ],
   "source": [
    "#calculaes the average of each total moving avg and finds the index of the highest one\n",
    "\n",
    "all_means = []\n",
    "for i in range(len(all_trail_t_mov_avg)):\n",
    "\n",
    "    mean_i = np.mean(all_trail_t_mov_avg[i])\n",
    "    all_means.append(mean_i)\n",
    "\n",
    "print(np.argmax(all_means), max(all_means))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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