[examples] MarketShare

docs/source/reference/agents.rst

@@ -9,4 +9,5 @@ Agents
     agents.Agent
     agents.HighFrequencyAgent
     agents.FCNAgent
-    agents.ArbitrageAgent
+    agents.ArbitrageAgent
+    agents.MarketShareFCNAgent

docs/source/user_guide/config.rst

@@ -94,6 +94,10 @@ Json config
             "timeWindowSize": JsonRandomFormat,
             "orderMargin": JsonRandomFormat,
             "marginType": "fixed" or "normal" (Optional; default fixed)
+        },
+	    "MarketShareFCNAgents": {
+            "class": "MarketShareFCNAgent",
+            "extends": "FCNAgent"
         },
         "ArbitrageAgent": {
             "class": "ArbitrageAgent",
@@ -102,4 +106,11 @@ Json config
             "orderThresholdPrice": float,
             "orderTimeLength": int (Optional, default 1),
         },
+        "MarketMakerAgent": {
+            "class": "MarketMakerAgent",
+            "extends": "Agents",
+            "targetMarket": string required,
+            "netInterestSpread": float required,
+            "orderTimeLength": int optional; default 2,
+        }
     }

examples/fat_finger.ipynb

@@ -0,0 +1,191 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "58xSRq9jpa-2"
+   },
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/masanorihirano/pams/blob/main/examples/fat_finger.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "OwaI8_xbpa-5",
+    "outputId": "1df64bd6-6e6b-4365-8f03-c6f3fc583f5e"
+   },
+   "outputs": [],
+   "source": [
+    "# Please remove comment-out if necessary\n",
+    "#! pip install pams matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ixLeaU7Epa-5"
+   },
+   "outputs": [],
+   "source": [
+    "config = {\n",
+    "\t\"simulation\": {\n",
+    "\t\t\"markets\": [\"Market\"],\n",
+    "\t\t\"agents\": [\"FCNAgents\"],\n",
+    "\t\t\"sessions\": [\n",
+    "\t\t\t{\t\"sessionName\": 0,\n",
+    "\t\t\t\t\"iterationSteps\": 100,\n",
+    "\t\t\t\t\"withOrderPlacement\": True,\n",
+    "\t\t\t\t\"withOrderExecution\": False,\n",
+    "\t\t\t\t\"withPrint\": True\n",
+    "\t\t\t},\n",
+    "\t\t\t{\t\"sessionName\": 1,\n",
+    "\t\t\t\t\"iterationSteps\": 500,\n",
+    "\t\t\t\t\"withOrderPlacement\": True,\n",
+    "\t\t\t\t\"withOrderExecution\": True,\n",
+    "\t\t\t\t\"withPrint\": True,\n",
+    "\t\t\t\t\"events\": [\"OrderMistakeShock\"]\n",
+    "\t\t\t}\n",
+    "\t\t]\n",
+    "\t},\n",
+    "\n",
+    "\t\"OrderMistakeShock\": {\n",
+    "\t\t\"class\": \"OrderMistakeShock\",\n",
+    "\t\t\"target\": \"Market\",\n",
+    "\t\t\"triggerTime\": 100,       \"MEMO\": \"At the 100th step of the session 2\",\n",
+    "\t\t\"priceChangeRate\": -0.05, \"MEMO\": \"Sign: negative for down; positive for up; zero for no change\",\n",
+    "\t\t\"orderVolume\": 10000,     \"MEMO\": \"Very much\",\n",
+    "\t\t\"orderTimeLength\": 10000, \"MEMO\": \"Very long\",\n",
+    "\t\t\"enabled\": True\n",
+    "\t},\n",
+    "\n",
+    "\t\"Market\": {\n",
+    "\t\t\"class\": \"Market\",\n",
+    "\t\t\"tickSize\": 0.00001,\n",
+    "\t\t\"marketPrice\": 300.0,\n",
+    "\t\t\"outstandingShares\": 25000\n",
+    "\t},\n",
+    "\n",
+    "\t\"FCNAgents\": {\n",
+    "\t\t\"class\": \"FCNAgent\",\n",
+    "\t\t\"numAgents\": 100,\n",
+    "\n",
+    "\t\t\"MEMO\": \"Agent class\",\n",
+    "\t\t\"markets\": [\"Market\"],\n",
+    "\t\t\"assetVolume\": 50,\n",
+    "\t\t\"cashAmount\": 10000,\n",
+    "\n",
+    "\t\t\"MEMO\": \"FCNAgent class\",\n",
+    "\t\t\"fundamentalWeight\": {\"expon\": [1.0]},\n",
+    "\t\t\"chartWeight\": {\"expon\": [0.0]},\n",
+    "\t\t\"noiseWeight\": {\"expon\": [1.0]},\n",
+    "\t\t\"noiseScale\": 0.001,\n",
+    "\t\t\"timeWindowSize\": [100, 200],\n",
+    "\t\t\"orderMargin\": [0.0, 0.1]\n",
+    "\t}\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "xUUUfulSpa-6"
+   },
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pams.runners import SequentialRunner\n",
+    "from pams.logs.market_step_loggers import MarketStepSaver"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "3QXSkEw2pa-6",
+    "outputId": "fb454d0c-57b3-4cb6-d6ad-4512dbe429c1"
+   },
+   "outputs": [],
+   "source": [
+    "saver = MarketStepSaver()\n",
+    "\n",
+    "runner = SequentialRunner(\n",
+    "    settings=config,\n",
+    "    prng=random.Random(42),\n",
+    "    logger=saver,\n",
+    ")\n",
+    "runner.main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "P5_pyTa9pa-6"
+   },
+   "outputs": [],
+   "source": [
+    "market_prices = dict(sorted(map(lambda x: (x[\"market_time\"], x[\"market_price\"]), saver.market_step_logs)))\n",
+    "fundamental_prices = dict(sorted(map(lambda x: (x[\"market_time\"], x[\"fundamental_price\"]), saver.market_step_logs)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 449
+    },
+    "id": "c__AgWzapa-7",
+    "outputId": "456ed7cb-0b14-4774-845c-da50b68a7031",
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(list(market_prices.keys()), list(market_prices.values()))\n",
+    "plt.plot(list(fundamental_prices.keys()), list(fundamental_prices.values()), color='black')\n",
+    "plt.xlabel(\"ticks\")\n",
+    "plt.ylabel(\"market price\")\n",
+    "plt.ylim([270, 330])\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}