refresh correlation.ipynb

correlation.ipynb

@@ -22,6 +22,7 @@
     "from time import time, sleep\n",
     "import os\n",
     "import pandas as pd\n",
+    "import asyncio\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt"
    ]
@@ -64,35 +65,19 @@
    "outputs": [],
    "source": [
     "info = await bot.public_get(bot.endpoints[\"exchange_info\"])\n",
-    "symbols = [e[\"symbol\"] for e in info[\"symbols\"] if e[\"symbol\"].endswith(\"USDT\")]\n",
-    "\n",
-    "interval = \"5m\"\n",
-    "\n",
-    "fpath = f\"logs/binance/ohlcvs_{interval}/\"\n",
-    "if os.path.exists(fpath):\n",
-    "    print(\"loading cache\")\n",
-    "    csvs = {}\n",
-    "    for f in [x for x in os.listdir(fpath) if x.endswith(\".csv\")]:\n",
-    "        csvs[f[:-4]] = pd.read_csv(fpath + f)\n",
-    "else:\n",
-    "    fpath = make_get_filepath(f\"logs/binance/ohlcvs_{interval}/\")\n",
-    "\n",
-    "    data = {}\n",
-    "    for s in sorted(symbols):\n",
-    "        print(s, end=\" \")\n",
-    "        ohlcvs = await bot.fetch_ohlcvs(symbol=s, interval=interval)\n",
-    "        data[s] = ohlcvs\n",
-    "        sleep(0.5)\n",
-    "    # cache as csv for later use\n",
-    "    csvs = {}\n",
-    "    fpath = make_get_filepath(f\"logs/binance/ohlcvs_{interval}/\")\n",
-    "    for s in data:\n",
-    "        csvs[s] = pd.DataFrame(data[s])\n",
-    "        csvs[s].to_csv(f\"{fpath}{s}.csv\")\n",
-    "n_days = (\n",
-    "    (csvs[symbols[0]].timestamp.iloc[-1] - csvs[symbols[0]].timestamp.iloc[0]) / 1000 / 60 / 60 / 24\n",
-    ")\n",
-    "print(\"n days\", n_days)"
+    "csvs = {}\n",
+    "syms = [x['symbol'] for x in info['symbols'] if x['symbol'].endswith('USDT')]\n",
+    "n = 7\n",
+    "interval = '5m'\n",
+    "while True:\n",
+    "    if not syms:\n",
+    "        break\n",
+    "    subset = syms[:n]\n",
+    "    syms = syms[n:]\n",
+    "    res = await asyncio.gather(*[bot.fetch_ohlcvs(symbol=s, interval=interval) for s in subset])\n",
+    "    for s, r in zip(subset, res):\n",
+    "        csvs[s] = pd.DataFrame(r)\n",
+    "    print(subset, len(syms))"
    ]
   },
   {
@@ -163,17 +148,8 @@
    "outputs": [],
    "source": [
     "# sum correlation of each symbol to every other symbol\n",
-    "sorted([(sum(corrs_dict[s].values()), s) for s in corrs_dict])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# sum abs correlation of each symbol to every other symbol\n",
-    "sorted([(sum(corrs_dict_abs[s].values()), s) for s in corrs_dict])"
+    "corr_sorted = sorted([(sum(corrs_dict[s].values()), s) for s in corrs_dict])\n",
+    "corr_sorted"
    ]
   },
   {
@@ -186,7 +162,8 @@
     "mean_prices = pd.concat([e.close / e.close.iloc[0] for e in csvs.values()], axis=1).mean(axis=1)\n",
     "\n",
     "# compare individual symbol to mean\n",
-    "symbol = \"SOLUSDT\"\n",
+    "symbol = \"SXPUSDT\"\n",
+    "# symbol = corr_sorted[0][1]  # least correlated symbol\n",
     "mean_prices.rename(\"mean_prices\").plot()\n",
     "(csvs[symbol].close / csvs[symbol].close.iloc[0]).rename(symbol).plot()\n",
     "plt.legend()"
@@ -230,7 +207,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,