diff --git "a/fin_rl_qlearning_v1.ipynb" "b/fin_rl_qlearning_v1.ipynb" --- "a/fin_rl_qlearning_v1.ipynb" +++ "b/fin_rl_qlearning_v1.ipynb" @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 1, "metadata": { "id": "LNXxxKojNTiL" }, @@ -49,22 +49,50 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 3, "metadata": { "id": "dmAuEhZZNTiL" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3015\n", + "1866\n" + ] + }, + { + "data": { + "text/plain": [ + "1664" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Get data\n", "eth_usd = yf.Ticker(\"ETH-USD\")\n", "eth = eth_usd.history(period=\"max\")\n", - "eth_train = eth[-900:-200]\n", - "eth_test = eth[-200:]" + "\n", + "btc_usd = yf.Ticker(\"BTC-USD\")\n", + "btc = btc_usd.history(period=\"max\")\n", + "print(len(btc))\n", + "print(len(eth))\n", + "\n", + "btc_train = eth[-3015:-200]\n", + "# btc_test = eth[-200:]\n", + "eth_train = eth[-1864:-200]\n", + "eth_test = eth[-200:]\n", + "# len(eth_train)" ] }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -75,17 +103,21 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "# Policy\n", "\n", "def greedy_policy(Qtable, state):\n", - " # Exploitation: take the action with the highest state, action value\n", - " action = np.argmax(Qtable[state])\n", - " \n", - " return action\n", + " # Exploitation: take the action with the highest state, action value\n", + " # if we dont have a state with values return DO_NOTHING \n", + " if abs(np.max(Qtable[state])) > 0:\n", + " action = np.argmax(Qtable[state])\n", + " else:\n", + " action = 2\n", + " # action = np.argmax(Qtable[state])\n", + " return action\n", "\n", "\n", "def epsilon_greedy_policy(Qtable, state, epsilon, env):\n", @@ -106,13 +138,15 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": { "id": "wlC-EdLENTiN" }, "outputs": [], "source": [ "def train(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable, learning_rate, gamma):\n", + " state_history = []\n", + " \n", " for episode in range(n_training_episodes):\n", " # Reduce epsilon (because we need less and less exploration)\n", " epsilon = min_epsilon + (max_epsilon - min_epsilon)*np.exp(-decay_rate*episode)\n", @@ -139,12 +173,15 @@ " \n", " # Our next state is the new state\n", " state = new_state\n", - " return Qtable" + "\n", + " state_history.append(state) \n", + "\n", + " return Qtable, state_history" ] }, { "cell_type": "code", - "execution_count": 327, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -155,7 +192,6 @@ " Do_nothing = 2\n", "\n", "class CustTradingEnv(gym.Env):\n", - " metadata = {'render.modes': ['human']}\n", "\n", " def __init__(self, df, max_steps=0):\n", " self.seed()\n", @@ -276,6 +312,7 @@ " last_price = self.prices[self._current_tick - 1]\n", " price_diff = current_price - last_price\n", "\n", + " penalty = -1 * last_price * 0.01\n", " # OPEN BUY - 1\n", " if action == Actions.Buy.value and self._position == 0:\n", " self._position = 1\n", @@ -284,7 +321,7 @@ " self._position_history.append(1)\n", "\n", " elif action == Actions.Buy.value and self._position > 0:\n", - " step_reward += 0\n", + " step_reward += penalty\n", " self._position_history.append(-1)\n", " # CLOSE SELL - 4\n", " elif action == Actions.Buy.value and self._position < 0:\n", @@ -308,7 +345,7 @@ " self._position_history.append(2)\n", " self._trade_history.append(step_reward)\n", " elif action == Actions.Sell.value and self._position < 0:\n", - " step_reward += 0\n", + " step_reward += penalty\n", " self._position_history.append(-1)\n", "\n", " # DO NOTHING - 0\n", @@ -361,13 +398,13 @@ }, { "cell_type": "code", - "execution_count": 330, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "# Training parameters\n", - "n_training_episodes = 10000 # Total training episodes\n", - "learning_rate = 0.5 # Learning rate\n", + "n_training_episodes = 20000 # Total training episodes\n", + "learning_rate = 0.2 # Learning rate\n", "\n", "# Environment parameters\n", "max_steps = 20 # Max steps per episode\n", @@ -375,13 +412,15 @@ "\n", "# Exploration parameters\n", "max_epsilon = 1.0 # Exploration probability at start\n", + "# max_epsilon = 1.0 # Exploration probability at start\n", "min_epsilon = 0.05 # Minimum exploration probability \n", + "# min_epsilon = 0.05 # Minimum exploration probability \n", "decay_rate = 0.0005 # Exponential decay rate for exploration prob" ] }, { "cell_type": "code", - "execution_count": 331, + "execution_count": 68, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -397,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 332, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -411,29 +450,36 @@ }, { "cell_type": "code", - "execution_count": 333, + "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "690" + "997" ] }, - "execution_count": 333, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "Qtable_trading = train(n_training_episodes, min_epsilon, max_epsilon, \n", + "# train with ETH\n", + "Qtable_trading, state_history = train(n_training_episodes, min_epsilon, max_epsilon, \n", " decay_rate, env, max_steps, Qtable_trading, learning_rate, gamma )\n", - "len(np.where( Qtable_trading > 0 )[0])" + "len(np.where( Qtable_trading > 0 )[0])\n", + "\n", + "# #train with BTC\n", + "# env = CustTradingEnv(df=btc_train, max_steps=max_steps)\n", + "# Qtable_trading, state_history = train(n_training_episodes, min_epsilon, max_epsilon, \n", + "# decay_rate, env, max_steps, Qtable_trading, learning_rate, gamma )\n", + "# len(np.where( Qtable_trading > 0 )[0])" ] }, { "cell_type": "code", - "execution_count": 334, + "execution_count": 71, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -445,7 +491,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -465,20 +511,21 @@ }, { "cell_type": "code", - "execution_count": 335, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[152.30224609375,\n", - " 209.1220703125,\n", - " 305.837158203125,\n", - " 11.605224609375,\n", - " 92.665771484375]" + "[919.390869140625,\n", + " 488.588623046875,\n", + " 626.90869140625,\n", + " 29.600830078125,\n", + " -8.8203125,\n", + " 166.931396484375]" ] }, - "execution_count": 335, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -489,7 +536,53 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "351" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(np.unique(state_history, return_counts=True)[1])\n", + "# count = 0\n", + "# for i in range(len(state_history)):\n", + "# if state_history[i] == 1987:\n", + "# count +=1\n", + "# count" + ] + }, + { + "cell_type": "code", + "execution_count": 438, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "352" + ] + }, + "execution_count": 438, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Qtable_trading[1987]\n", + "len(np.unique(env.signal_features))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -536,18 +629,18 @@ }, { "cell_type": "code", - "execution_count": 325, + "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a0c2015a163743448978dd27f700d2e9", + "model_id": "746362c4808b4d068d499fd7b96419b0", "version_major": 2, "version_minor": 0 }, "text/plain": [ - " 0%| | 0/200 [00:00" ] @@ -596,6 +689,34 @@ "env_test.render()" ] }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "173 151 0.8728323699421965\n" + ] + } + ], + "source": [ + "def count_equal(env, Qtable):\n", + " count=0\n", + " for i in env.signal_features:\n", + " if abs(np.max(Qtable[i])) > 0:\n", + " count+=1\n", + " # else:\n", + " # print(i)\n", + " # assert 0\n", + " \n", + " print(len(env.signal_features), count, count / len(env.signal_features))\n", + "\n", + "count_equal(env_test, Qtable_trading)" + ] + }, { "cell_type": "code", "execution_count": null,