TFlearn-Ichimoku .ipynb

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   "source": [
    "# TFLearnでテクニカル指標　 Ichimokuの場合\n",
    "TFLearnっていう、TensorFlowをもっと簡単に使えるライブラリがあるということなので、それも入れてみた。\n",
    "http://tflearn.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# グラフの定義(可視化)\n",
    "\n",
    "TFlearnで使うグラフの定義ですが、今回は線形モデルだとわかっているものとして、単に入力層から線形結合させたものを定義します。あと、バイアスも使わないので、bias=Falseとしておきます。\n",
    "\n",
    "ただ、regressionについては、デフォルトのままだとあまりよい結果がでなかったので、SGDメソッドを使って学習率を徐々に下げていくよう調整を行いました。"
   ]
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    " # Graph definition\n",
    "layer_in = tflearn.input_data(shape=[None, N])\n",
    "layer1 = tflearn.fully_connected(layer_in, 1, activation='linear', bias=False)\n",
    "sgd = tflearn.optimizers.SGD(learning_rate=0.01, lr_decay=0.95, decay_step=100)\n",
    "regression = tflearn.regression(layer1, optimizer=sgd, loss='mean_square')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 教師データの処理\n",
    "\n",
    "前回の例は、２入力の線形和を求めるだけのモデルだったのですが、二つの入力データの差が小さいと誤差が小さくなってしまい、学習がうまくできていませんでした。\n",
    "\n",
    "そこで、こんどは、二つのデータの差が１以上のみを教師データとしてみました。\n",
    "\n",
    "## 5分足(2007/07～2017/07)　"
   ]
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   "cell_type": "code",
   "execution_count": 9,
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     "text": [
      "Training Step: 10000  | total loss: \u001b[1m\u001b[32m3.34792\u001b[0m\u001b[0m | time: 0.011s\n",
      "| SGD | epoch: 10000 | loss: 3.34792 -- iter: 31/31\n",
      "\n",
      "weights\n",
      "W[0] = [ 0.63396788]\n",
      "W[1] = [ 0.37587214]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import tflearn\n",
    "\n",
    "file = 'USDJPY5 Ichimoku.txt'\n",
    "ohlc = pd.read_csv(file, index_col='Time', parse_dates=True)\n",
    "close = ohlc.Close.values\n",
    "ind1 = ohlc.Ind1.values\n",
    "\n",
    "N = 2\n",
    "X = np.empty((0,N))\n",
    "Y = np.empty((0,1))\n",
    "for i in range(200):\n",
    "    if abs(close[i]-close[i+1]) >= 1.0:\n",
    "        X = np.vstack((X, close[i:i+N]))\n",
    "        Y = np.vstack((Y, ind1[i+N-1:i+N]))\n",
    "\n",
    "# Graph definition\n",
    "layer_in = tflearn.input_data(shape=[None, N])\n",
    "layer1 = tflearn.fully_connected(layer_in, 1, activation='linear', bias=False)\n",
    "sgd = tflearn.optimizers.SGD(learning_rate=0.01, lr_decay=0.95, decay_step=100)\n",
    "regression = tflearn.regression(layer1, optimizer=sgd, loss='mean_square')\n",
    "\n",
    "# Model training\n",
    "m = tflearn.DNN(regression)\n",
    "m.fit(X, Y, n_epoch=10000, snapshot_epoch=False, run_id='Ichimokulearn')\n",
    "\n",
    "# Weights\n",
    "print('\\nweights')\n",
    "for i in range(N):\n",
    "    print('W['+str(i)+'] =' ,m.get_weights(layer1.W)[i])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ノイズ入りの教師データ\n",
    "\n",
    "そもそもニューラルネットワークは、正確な数値予測のためではなく、結構アバウトな予測を行うためのものみたいです。\n",
    "\n",
    "そこで、こんどは指標値に平均0、標準偏差0.1のガウスノイズを付加したものを教師データとしてみました。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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     "name": "stdout",
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     "text": [
      "Training Step: 10000  | total loss: \u001b[1m\u001b[32m0.50975\u001b[0m\u001b[0m | time: 0.011s\n",
      "| SGD | epoch: 10000 | loss: 0.50975 -- iter: 31/31\n",
      "\n",
      "weights\n",
      "W[0] = [ 0.66791338]\n",
      "W[1] = [ 0.33452979]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import tflearn\n",
    "\n",
    "file = 'USDJPY5 Ichimoku.txt'\n",
    "ohlc = pd.read_csv(file, index_col='Time', parse_dates=True)\n",
    "close = ohlc.Close.values\n",
    "ind1 = ohlc.Ind1.values\n",
    "\n",
    "N = 2\n",
    "X = np.empty((0,N))\n",
    "Y = np.empty((0,1))\n",
    "for i in range(200):\n",
    "    if abs(close[i]-close[i+1]) >= 1.0:\n",
    "        X = np.vstack((X, close[i:i+N]))\n",
    "        noise = np.random.normal(0,0.1)\n",
    "        Y = np.vstack((Y, ind1[i+N-1:i+N]+noise))\n",
    "# Graph definition\n",
    "layer_in = tflearn.input_data(shape=[None, N])\n",
    "layer1 = tflearn.fully_connected(layer_in, 1, activation='linear', bias=False)\n",
    "sgd = tflearn.optimizers.SGD(learning_rate=0.01, lr_decay=0.95, decay_step=100)\n",
    "regression = tflearn.regression(layer1, optimizer=sgd, loss='mean_square')\n",
    "\n",
    "# Model training\n",
    "m = tflearn.DNN(regression)\n",
    "m.fit(X, Y, n_epoch=10000, snapshot_epoch=False, run_id=' Ichimokulearn')\n",
    "\n",
    "# Weights\n",
    "print('\\nweights')\n",
    "for i in range(N):\n",
    "    print('W['+str(i)+'] =' ,m.get_weights(layer1.W)[i])"
   ]
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