Optimal trading with order-flow and short-term alpha

OrderFlow.ipynb

OrderFlow_helpers.ipynb

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   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import interpolate\n",
    "# random.seed(20)\n",
    "np.random.seed(30)\n",
    "np.seterr(divide='ignore', invalid='ignore')\n",
    "font = {'family': 'serif',\n",
    "        'style': 'italic',\n",
    "        # 'color': 'darkred',\n",
    "        'weight': 1,\n",
    "        'size': 16,\n",
    "        }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MakeHeatMap(t, y, fignum=False, nct=20, lower_threshold=0.1, upper_threshold=0.48):\n",
    "\n",
    "    Nbins = 100\n",
    "\n",
    "    miny = np.nanmin(y)\n",
    "    maxy = np.nanmax(y)\n",
    "    dy = (maxy - miny) / Nbins\n",
    "\n",
    "    bins = np.linspace(miny, maxy, 100)\n",
    "\n",
    "\n",
    "    myNdt = 200\n",
    "    yr = np.full([myNdt, len(bins)], np.nan)\n",
    "\n",
    "    mydt = (t[-1] - t[0]) / (myNdt - 1)\n",
    "\n",
    "    tr = np.full([myNdt, ], np.nan)\n",
    "\n",
    "    for i in range(myNdt):\n",
    "        kk = np.where(t < t[0] + mydt * (i + 1))[-1][-1]\n",
    "        count = np.histogram(y[:, kk], np.arange(miny, maxy + 0.00001, dy))\n",
    "        yr[i, :] = count[0]\n",
    "        tr[i] = t[kk].item()\n",
    "\n",
    "    zr = yr.T / len(y[:, 0])\n",
    "\n",
    "    if not fignum:\n",
    "        fig = plt.figure()\n",
    "    else:\n",
    "        fig = plt.figure(fignum)\n",
    "    plt.tick_params(direction='in', bottom=True, top=True, left=True, right=True)\n",
    "    axes = fig.gca()\n",
    "    axes.set_xlim(left=0)\n",
    "    axes.set_ylim(bottom=0, top=np.max(maxy))\n",
    "    x_cord_i, y_cord_i = np.meshgrid(tr, bins)\n",
    "\n",
    "    zr[zr < np.max(zr)*lower_threshold] = 0\n",
    "    zr[zr > np.max(zr)*upper_threshold] = np.max(zr)*upper_threshold\n",
    "    cmap = plt.get_cmap('YlOrRd')\n",
    "    #print(x_cord_i.shape)\n",
    "    #print(y_cord_i.shape)\n",
    "    #print(zr.shape)\n",
    "    plt.contour(x_cord_i, y_cord_i, zr, nct, cmap=cmap, levels=np.linspace(zr.min(), zr.max(), 1000))\n",
    "\n",
    "    lim = np.around(np.max(zr), int(np.around(-(np.log(0.10972799999999999)/np.log(10)))))\n",
    "    plt.colorbar(ticks=np.arange(0, np.max(zr), lim/10))\n",
    "\n",
    "    return [fig, tr, yr, zr]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotPath(t, T, Y, idxfig, sigma, y_range, title, lw=1):\n",
    "    fig_1 = plt.figure()\n",
    "    plt.tick_params(direction='in', bottom=True, top=True, left=True, right=True)\n",
    "    axes = fig_1.gca()\n",
    "    axes.set_xlim([0, T])\n",
    "    axes.set_ylim(y_range)\n",
    "\n",
    "    for i in range(len(idxfig)):\n",
    "        plt.plot(t, Y[idxfig[i]], linewidth=lw, label=i+1)\n",
    "\n",
    "    plt.ylabel(title,  fontdict=font)\n",
    "    plt.xlabel('Time (Day) ',  fontdict=font)\n",
    "    plt.legend()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotHeatMap(t, Y, Y_AC, title, n=20, lower_threshold=0.1, upper_threshold=0.48):\n",
    "    out = MakeHeatMap(t, Y, nct=n)\n",
    "    [fig, tr, yr, zr] = out\n",
    "    plt.plot(t, Y_AC, '--k')\n",
    "    plt.ylabel(title,  fontdict=font)\n",
    "    plt.xlabel('Time (Day) ',  fontdict=font)\n",
    "    plt.show()"
   ]
  }
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