diff --git a/.gitignore b/.gitignore index 300a17c1..3779bee6 100644 --- a/.gitignore +++ b/.gitignore @@ -62,3 +62,11 @@ target/ #Ipython Notebook .ipynb_checkpoints + +# Data +cn_data/ +cn_data_rvol/ +cn_data_daily_bar/ +cn_data_weekly_bar/ +cn_data_range_vol/ +cn_data_minute/ \ No newline at end of file diff --git a/get.ipynb b/get.ipynb new file mode 100644 index 00000000..d84e2b51 --- /dev/null +++ b/get.ipynb @@ -0,0 +1,544 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pytdx.hq import TdxHq_API\n", + "from pytdx.params import TDXParams\n", + "from datetime import datetime, timedelta\n", + "import pandas as pd\n", + "import polars as pl\n", + "import os\n", + "from tqdm import tqdm\n", + "\n", + "HOST = '119.147.212.81'\n", + "PORT = 7709\n", + "\n", + "API_TICK_SIZE = 2000" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "api = TdxHq_API()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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The Series length 33 differs from the DataFrame height: 730\"))", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/zgy/disk2/client-python/get.ipynb Cell 8\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m get(\u001b[39m'\u001b[39;49m\u001b[39m002594\u001b[39;49m\u001b[39m'\u001b[39;49m)\n", + "\u001b[1;32m/home/zgy/disk2/client-python/get.ipynb Cell 8\u001b[0m in \u001b[0;36mget\u001b[0;34m(code)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[39m# df_rvol[(len(df_rvol) - 33):]\u001b[39;00m\n\u001b[1;32m 6\u001b[0m df \u001b[39m=\u001b[39m pl\u001b[39m.\u001b[39mread_parquet(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mcn_data_daily_bar/data/\u001b[39m\u001b[39m{\u001b[39;00mcode\u001b[39m}\u001b[39;00m\u001b[39m.parquet\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m----> 7\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mrvol_30\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m df_rvol[\u001b[39m'\u001b[39m\u001b[39mrvol_30\u001b[39m\u001b[39m'\u001b[39m][(\u001b[39mlen\u001b[39m(df_rvol) \u001b[39m-\u001b[39m \u001b[39m33\u001b[39m):]\n\u001b[1;32m 8\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mdatetime\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m df_rvol[\u001b[39m'\u001b[39m\u001b[39mtime\u001b[39m\u001b[39m'\u001b[39m][(\u001b[39mlen\u001b[39m(df_rvol) \u001b[39m-\u001b[39m \u001b[39m33\u001b[39m):]\n\u001b[1;32m 10\u001b[0m \u001b[39m#\u001b[39;00m\n", + "File \u001b[0;32m~/.pyenv/versions/3.8.4/lib/python3.8/site-packages/polars/internals/frame.py:1453\u001b[0m, in \u001b[0;36mDataFrame.__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 1451\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreplace(key, pli\u001b[39m.\u001b[39mSeries(key, value))\n\u001b[1;32m 1452\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m:\n\u001b[0;32m-> 1453\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhstack([pli\u001b[39m.\u001b[39;49mSeries(key, value)], in_place\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[1;32m 1454\u001b[0m \u001b[39m# df[[\"C\", \"D\"]]\u001b[39;00m\n\u001b[1;32m 1455\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(key, \u001b[39mlist\u001b[39m):\n", + "File \u001b[0;32m~/.pyenv/versions/3.8.4/lib/python3.8/site-packages/polars/internals/frame.py:3123\u001b[0m, in \u001b[0;36mDataFrame.hstack\u001b[0;34m(self, columns, in_place)\u001b[0m\n\u001b[1;32m 3121\u001b[0m columns \u001b[39m=\u001b[39m columns\u001b[39m.\u001b[39mget_columns()\n\u001b[1;32m 3122\u001b[0m \u001b[39mif\u001b[39;00m in_place:\n\u001b[0;32m-> 3123\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_df\u001b[39m.\u001b[39;49mhstack_mut([s\u001b[39m.\u001b[39;49minner() \u001b[39mfor\u001b[39;49;00m s \u001b[39min\u001b[39;49;00m columns])\n\u001b[1;32m 3124\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 3125\u001b[0m \u001b[39melse\u001b[39;00m:\n", + "\u001b[0;31mRuntimeError\u001b[0m: Any(ShapeMisMatch(\"Could not horizontally stack Series. The Series length 33 differs from the DataFrame height: 730\"))" + ] + } + ], + "source": [ + "get('002594')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 100/100 [00:00<00:00, 2062.07it/s]\n" + ] + } + ], + "source": [ + "dfs = []\n", + "\n", + "for i in tqdm(list(range(len(df_cn_stks[:100])))):\n", + " mkt = df_cn_stks['tdx_api_mkt'][i]\n", + " code = df_cn_stks['code'][i]\n", + "\n", + " dftmp = get(code)\n", + " dfs.append(dftmp)\n", + "\n", + "pl.concat(dfs).to_parquet('cn_data_rvol/visual_output/100stks_rvol.1d.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.4" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "82328c1ac27e33c7f9ad051b9117ee9a5989011e2ef55b48106b5a4895113660" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/get_minute.ipynb b/get_minute.ipynb new file mode 100644 index 00000000..d3987575 --- /dev/null +++ b/get_minute.ipynb @@ -0,0 +1,1054 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pytdx.hq import TdxHq_API\n", + "from pytdx.params import TDXParams\n", + "from datetime import datetime, timedelta\n", + "import pandas as pd\n", + "import polars as pl\n", + "import os\n", + "from tqdm import tqdm\n", + "\n", + "HOST = '119.147.212.81'\n", + "PORT = 7709" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "api = TdxHq_API()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df_date_vol.to_pandas()['vol'].plot()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 过去n个交易日的 range_vol 合并\n", + "- 给定价格，filter 上下 xATR 范围" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['2020-12-30.parquet',\n", + " '2020-12-31.parquet',\n", + " '2021-01-04.parquet',\n", + " '2021-01-05.parquet',\n", + " '2021-01-06.parquet']" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fnames = os.listdir('cn_data_range_vol/002594/')\n", + "fnames.sort()\n", + "fnames[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 99%|█████████▉| 496/499 [00:06<00:00, 78.98it/s]\n" + ] + }, + { + "ename": "IndexError", + "evalue": "list index out of range", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/zgy/disk2/client-python/get_minute.ipynb Cell 16\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m tqdm(\u001b[39mlist\u001b[39m(\u001b[39mrange\u001b[39m(\u001b[39m1\u001b[39m, \u001b[39m500\u001b[39m))):\n\u001b[0;32m----> 2\u001b[0m date_str \u001b[39m=\u001b[39m fnames[\u001b[39m-\u001b[39;49mi]\u001b[39m.\u001b[39msplit(\u001b[39m'\u001b[39m\u001b[39m.\u001b[39m\u001b[39m'\u001b[39m)[\u001b[39m0\u001b[39m]\n\u001b[1;32m 3\u001b[0m df_joint \u001b[39m=\u001b[39m pl\u001b[39m.\u001b[39mread_parquet(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mcn_data_range_vol/002594/\u001b[39m\u001b[39m{\u001b[39;00mdate_str\u001b[39m}\u001b[39;00m\u001b[39m.parquet\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 5\u001b[0m index \u001b[39m=\u001b[39m fnames\u001b[39m.\u001b[39mindex(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mdate_str\u001b[39m}\u001b[39;00m\u001b[39m.parquet\u001b[39m\u001b[39m'\u001b[39m)\n", + "\u001b[0;31mIndexError\u001b[0m: list index out of range" + ] + } + ], + "source": [ + "for i in tqdm(list(range(1, 500))):\n", + " date_str = fnames[-i].split('.')[0]\n", + " df_joint = pl.read_parquet(f'cn_data_range_vol/002594/{date_str}.parquet')\n", + "\n", + " index = fnames.index(f'{date_str}.parquet')\n", + "\n", + " for i in range(index-1, index-1-20, -1):\n", + " # print(fnames[i])\n", + " df_2 = pl.read_parquet(f'cn_data_range_vol/002594/{fnames[i]}')\n", + "\n", + " df_joint = df_joint.join(df_2, on='range', how='outer')\n", + " df_joint['vol'] = df_joint['vol'].fill_null(0.0) \\\n", + " + df_joint['vol_right'].fill_null(0.0)\n", + " df_joint = df_joint[['range', 'vol']]\n", + "\n", + " df_joint.to_parquet(f'cn_data_range_vol/period_20/002594/{date_str}.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "def get_support_rate(cur_price, date_str):\n", + " df_joint = pl.read_parquet(f'cn_data_range_vol/period_20/002594/{date_str}.parquet')\n", + "\n", + " cur_price = cur_price * 100\n", + "\n", + " df_above = df_joint.filter(pl.col('range') < cur_price + 20 * 100) \\\n", + " .filter(pl.col('range') > cur_price)\n", + " df_below = df_joint.filter(pl.col('range') > cur_price - 20 * 100) \\\n", + " .filter(pl.col('range') <= cur_price)\n", + "\n", + " rate = round(df_below['vol'].sum() / (df_below['vol'].sum() + df_above['vol'].sum()), 2)\n", + " return rate" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.87" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_support_rate(271, '2023-01-11')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.4 (default, Jan 10 2023, 13:17:10) \n[GCC 7.5.0]" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "82328c1ac27e33c7f9ad051b9117ee9a5989011e2ef55b48106b5a4895113660" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/get_weekly.ipynb b/get_weekly.ipynb new file mode 100644 index 00000000..0d5bf511 --- /dev/null +++ b/get_weekly.ipynb @@ -0,0 +1,793 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from pytdx.hq import TdxHq_API\n", + "from pytdx.params import TDXParams\n", + "from datetime import datetime, timedelta\n", + "import pandas as pd\n", + "import polars as pl\n", + "import os\n", + "from tqdm import tqdm\n", + "\n", + "HOST = '119.147.212.81'\n", + "PORT = 7709\n", + "\n", + "API_TICK_SIZE = 2000" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "api = TdxHq_API()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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first_valid_index" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "729 2023-01-11 00:00:00\n", + "14.35\n", + "182 723 2023-01-03 00:00:00 12.99\n", + "181 721 2022-12-29 00:00:00 12.99\n", + "180 717 2022-12-23 00:00:00 12.55\n", + "-- invalid: 179 713 2022-12-19 00:00:00 13.37\n", + "-- invalid: 178 704 2022-12-06 00:00:00 16.12\n", + "-- invalid: 177 702 2022-12-02 00:00:00 16.06\n", + "-- invalid: 176 697 2022-11-25 00:00:00 15.84\n", + "Last valid pivot price: 12.99\n" + ] + }, + { + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_last_valid_pivot_low_price(len(df)-1, show_print=True, return_length=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "MAX_INVALID_NUM = 3\n", + "\n", + "\n", + "def get_last_valid_pivot_price(index, show_print=False, return_price=False, return_length=False):\n", + " if show_print:\n", + " print(index, df['datetime'][index])\n", + "\n", + " p = df['concrete_high'][index]\n", + " if show_print:\n", + " print(p)\n", + "\n", + " invalid_num = 0\n", + "\n", + " last_valid_pivot = None\n", + "\n", + " df_tmp = df[:index]\n", + "\n", + " first_valid_index = None\n", + "\n", + " high_index = df_tmp[ df_tmp['is_pivot_high'] ][['pivot_high_price', 'index']]['index'].to_list()\n", + " for i in reversed( list(range(len(high_index))) ):\n", + " i_raw = high_index[i]\n", + "\n", + " cur_price = df['pivot_high_price'][i_raw]\n", + "\n", + " if cur_price >= p:\n", + " first_valid_index = i_raw\n", + " if last_valid_pivot is None:\n", + " last_valid_pivot = df['pivot_high_price'][i_raw]\n", + " p = cur_price\n", + " if show_print:\n", + " print(i, i_raw, df['datetime'][i_raw], df['pivot_high_price'][i_raw])\n", + " invalid_num = 0\n", + " else:\n", + " if show_print:\n", + " print('-- invalid: ', i, i_raw, df['datetime'][i_raw], df['pivot_high_price'][i_raw])\n", + " invalid_num += 1\n", + " if invalid_num > MAX_INVALID_NUM:\n", + " break\n", + "\n", + " if show_print:\n", + " print('Last valid pivot price: ', last_valid_pivot)\n", + "\n", + " if return_price:\n", + " return last_valid_pivot\n", + " if return_length:\n", + " if first_valid_index is None:\n", + " return 0\n", + " return index - first_valid_index" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "723 2023-01-03 00:00:00\n", + "13.38\n", + "-- invalid: 186 719 2022-12-27 00:00:00 13.33\n", + "185 712 2022-12-16 00:00:00 14.11\n", + "184 706 2022-12-08 00:00:00 16.4\n", + "183 705 2022-12-07 00:00:00 16.4\n", + "182 703 2022-12-05 00:00:00 16.76\n", + "181 701 2022-12-01 00:00:00 16.78\n", + "-- invalid: 180 697 2022-11-25 00:00:00 16.43\n", + "179 693 2022-11-21 00:00:00 17.12\n", + "-- invalid: 178 689 2022-11-15 00:00:00 16.86\n", + "177 687 2022-11-11 00:00:00 17.15\n", + "-- invalid: 176 675 2022-10-26 00:00:00 13.38\n", + "-- invalid: 175 673 2022-10-24 00:00:00 13.26\n", + "-- invalid: 174 669 2022-10-18 00:00:00 13.63\n", + "-- invalid: 173 667 2022-10-14 00:00:00 13.42\n", + "Last valid pivot price: 14.11\n" + ] + }, + { + "data": { + "text/plain": [ + "36" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_last_valid_pivot_price(len(df)-7, show_print=True, return_length=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "df['last_valid_LH_pivot_price'] = df['index'].apply(lambda x: get_last_valid_pivot_price(index=x, return_price=True)).to_list()\n", + "df['valid_LH_length'] = df['index'].apply(lambda x: get_last_valid_pivot_price(index=x, return_length=True)).to_list()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "df['last_valid_HL_pivot_price'] = df['index'].apply(lambda x: get_last_valid_pivot_low_price(index=x, return_price=True)).to_list()\n", + "df['valid_HL_length'] = df['index'].apply(lambda x: get_last_valid_pivot_low_price(index=x, return_length=True)).to_list()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + "shape: (5, 22)\n", + "┌───────┬───────┬───────┬───────┬─────┬───────┬───┬───────┬───┐\n", + "│ open ┆ close ┆ high ┆ low ┆ ... ┆ last_ ┆ v ┆ last_ ┆ v │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ valid ┆ a ┆ valid ┆ a │\n", + "│ f64 ┆ f64 ┆ f64 ┆ f64 ┆ ┆ _LH_p ┆ l ┆ _HL_p ┆ l │\n", + "│ ┆ ┆ ┆ ┆ ┆ ivot_ ┆ i ┆ ivot_ ┆ i │\n", + "│ ┆ ┆ ┆ ┆ ┆ price ┆ d ┆ price ┆ d │\n", + "│ ┆ ┆ ┆ ┆ ┆ --- ┆ _ ┆ --- ┆ _ │\n", + "│ ┆ ┆ ┆ ┆ ┆ f64 ┆ L ┆ f64 ┆ H │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ H ┆ ┆ L │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ _ ┆ ┆ _ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ l ┆ ┆ l │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ e ┆ ┆ e │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ n ┆ ┆ n │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ g ┆ ┆ g │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ t ┆ ┆ t │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ h ┆ ┆ h │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ - ┆ ┆ - │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ - ┆ ┆ - │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ - ┆ ┆ - │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ i ┆ ┆ i │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ 6 ┆ ┆ 6 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ 4 ┆ ┆ 4 │\n", + "╞═══════╪═══════╪═══════╪═══════╪═════╪═══════╪═══╪═══════╪═══╡\n", + "│ 22.72 ┆ 23.58 ┆ 23.59 ┆ 22.56 ┆ ... ┆ null ┆ 0 ┆ null ┆ 0 │\n", + "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌┤\n", + "│ 23.58 ┆ 23.33 ┆ 23.9 ┆ 23.0 ┆ ... ┆ null ┆ 0 ┆ null ┆ 0 │\n", + "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌┤\n", + "│ 23.33 ┆ 24.21 ┆ 24.53 ┆ 23.26 ┆ ... ┆ null ┆ 0 ┆ null ┆ 0 │\n", + "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌┤\n", + "│ 24.66 ┆ 25.17 ┆ 25.69 ┆ 24.41 ┆ ... ┆ null ┆ 0 ┆ 23.33 ┆ 1 │\n", + "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌┤\n", + "│ 24.82 ┆ 24.45 ┆ 25.02 ┆ 24.29 ┆ ... ┆ 25.17 ┆ 1 ┆ 23.33 ┆ 2 │\n", + "└───────┴───────┴───────┴───────┴─────┴───────┴───┴───────┴───┘" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "逻辑1\n", + "- 上一个周期 LH\n", + "- 当前价 broke LH\n", + " - last valid LH pivot price\n", + " - if cur > last valid LH pivot\n", + "- 当前价 有HL\n", + "\n", + "再拆解\n", + "- break 上个周期的 trend" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "shape: (10,)\n", + "Series: 'last_valid_LH_pivot_price' [f64]\n", + "[\n", + "\t13.33\n", + "\t13.33\n", + "\t13.33\n", + "\t14.11\n", + "\t13.38\n", + "\t14.11\n", + "\t16.4\n", + "\t16.4\n", + "\t14.8\n", + "\tnull\n", + "]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['last_valid_LH_pivot_price'].tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "shape: (10,)\n", + "Series: 'high_broke_last_priod_LH' [bool]\n", + "[\n", + "\tfalse\n", + "\tfalse\n", + "\tfalse\n", + "\tfalse\n", + "\tfalse\n", + "\ttrue\n", + "\ttrue\n", + "\tfalse\n", + "\tfalse\n", + "\ttrue\n", + "]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 进仓条件\n", + "df['high_broke_last_priod_LH'] = ( \\\n", + " (df['concrete_high'] > df['last_valid_LH_pivot_price'].shift()) \\\n", + " & (df['valid_LH_length'].shift() > 12) \\\n", + " & (df['valid_HL_length'].shift() > 6) \\\n", + " ).fill_null(False)\n", + "df['high_broke_last_priod_LH'].tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "# 导出 Visual 数据文件\n", + "df.to_parquet(f'../analyzer.view.server/dump/cn_pivot/compute/{symbol}_with_length_threshold.1d.parquet')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "策略测试\n", + "- 日k高点 broke 上个周期 LH\n", + "- Entry: 上个周期的 last_valid_LH_price\n", + "- Exit: Open of next mkt day" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "# 进仓条件列类型是 Bool, 和Float相乘会只保留 条件为True的行\n", + "# 进仓价是前日的 last_valid_LH_pivot_price 指标\n", + "df['entry'] = df['last_valid_LH_pivot_price'].shift() * df['high_broke_last_priod_LH'] * 1.005 # 设定了 0.005 的滑点\n", + "\n", + "# 以下一天的开盘作为出仓价，注意因为最后一天还不知道第二天的开盘价，这里设定了当日收盘价来计算\n", + "df['exit'] = df['open'].shift(-1).fill_null(df['close'][-1]) * 0.995 # 设定了 0.005 的滑点" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "df_pnl = ((df['exit'] - df['entry']) / df['entry'])[ df['high_broke_last_priod_LH'] ]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pd.Series( df_pnl ).plot(kind='bar')" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "32" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(df_pnl)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.005417386525550511, 0.03996492926747724)" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_pnl.mean(), df_pnl.std()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sharpe = 2.1518452951472486\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "print(f\"Sharpe = {np.sqrt(252) * df_pnl.mean() / df_pnl.std()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pd.Series(cumret).plot()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 策略结合SR筹码比例" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "def get_support_rate(cur_price, date_str):\n", + " fname = f'cn_data_range_vol/period_20/002594/{date_str}.parquet'\n", + " if not os.path.exists(fname):\n", + " # print(fname)\n", + " return 0.0\n", + " df_joint = pl.read_parquet(fname)\n", + " # print(df_joint)\n", + "\n", + "\n", + " cur_price = int(cur_price * 100)\n", + "\n", + " df_above = df_joint.filter(pl.col('range') < cur_price + 20 * 100) \\\n", + " .filter(pl.col('range') > cur_price)\n", + " df_below = df_joint.filter(pl.col('range') > cur_price - 20 * 100) \\\n", + " .filter(pl.col('range') <= cur_price)\n", + "\n", + " rate = round(df_below['vol'].sum() / (df_below['vol'].sum() + df_above['vol'].sum()), 2)\n", + " # print(rate)\n", + " return rate" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(datetime.datetime(2021, 2, 2, 0, 0), 15.43)\n", + "(datetime.datetime(2021, 6, 8, 0, 0), 17.09)\n", + "(datetime.datetime(2021, 7, 16, 0, 0), 17.16)\n", + "(datetime.datetime(2021, 8, 24, 0, 0), 19.11)\n", + "(datetime.datetime(2021, 8, 26, 0, 0), 19.49)\n", + "(datetime.datetime(2021, 9, 8, 0, 0), 19.19)\n", + "(datetime.datetime(2021, 9, 9, 0, 0), 19.65)\n", + "(datetime.datetime(2021, 12, 15, 0, 0), 25.8)\n", + "(datetime.datetime(2021, 12, 17, 0, 0), 26.0)\n", + "(datetime.datetime(2022, 1, 6, 0, 0), 24.4)\n", + "(datetime.datetime(2022, 2, 23, 0, 0), 19.34)\n", + "(datetime.datetime(2022, 2, 24, 0, 0), 19.86)\n", + "(datetime.datetime(2022, 3, 3, 0, 0), 19.7)\n", + "(datetime.datetime(2022, 5, 12, 0, 0), 13.79)\n", + "(datetime.datetime(2022, 5, 19, 0, 0), 14.41)\n", + "(datetime.datetime(2022, 7, 14, 0, 0), 14.3)\n", + "(datetime.datetime(2022, 7, 21, 0, 0), 13.95)\n", + "(datetime.datetime(2022, 7, 28, 0, 0), 14.59)\n", + "(datetime.datetime(2022, 10, 18, 0, 0), 13.42)\n", + "(datetime.datetime(2022, 10, 26, 0, 0), 13.26)\n", + "(datetime.datetime(2022, 11, 30, 0, 0), 16.43)\n", + "(datetime.datetime(2023, 1, 5, 0, 0), 13.38)\n", + "(datetime.datetime(2023, 1, 6, 0, 0), 14.11)\n", + "(datetime.datetime(2023, 1, 11, 0, 0), 14.8)\n" + ] + } + ], + "source": [ + "def sr_rate(x):\n", + " try:\n", + " date_str = datetime.strftime(x[0] - timedelta(days=1), '%Y-%m-%d')\n", + " entry = x[1]\n", + " if entry is not None and entry > 0.0:\n", + " rate = get_support_rate(entry, date_str)\n", + " return rate\n", + " return 0.0\n", + " except Exception as e:\n", + " print(x)\n", + " return 0.0\n", + "\n", + "\n", + "sr_rate_list = df[['datetime', 'entry']].apply(lambda x: sr_rate(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "730" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "df['sr_rate'] = sr_rate_list['apply']" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "df['sr_rate_valid'] = (df['sr_rate'] > 0.75)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "df['trend_sr_valid'] = (df['high_broke_last_priod_LH'] & df['sr_rate_valid'])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# 进仓条件列类型是 Bool, 和Float相乘会只保留 条件为True的行\n", + "# 进仓价是前日的 last_valid_LH_pivot_price 指标\n", + "df['entry_sr'] = df['last_valid_LH_pivot_price'].shift() * df['trend_sr_valid'] * 1.01\n", + "\n", + "# 以下一天的开盘作为出仓价，注意因为最后一天还不知道第二天的开盘价，这里设定了当日收盘价来计算\n", + "df['exit_sr'] = df['open'].shift(-1).fill_null(df['close'][-1]) * 0.99" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "df_pnl = ((df['exit_sr'] - df['entry_sr']) / df['entry_sr'])[ df['trend_sr_valid'] ]\n", + "if len(df_pnl) > 0:\n", + " pd.Series( df_pnl ).plot(kind='bar')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "unsupported operand type(s) for *: 'float' and 'NoneType'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_13528/1937716509.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Sharpe = {np.sqrt(252) * df_pnl.mean() / df_pnl.std()}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mcumret\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumprod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdf_pnl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcumret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'float' and 'NoneType'" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "print(f\"Sharpe = {np.sqrt(252) * df_pnl.mean() / df_pnl.std()}\")\n", + "cumret=(np.cumprod(1+df_pnl)-1)\n", + "pd.Series(cumret).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['open',\n", + " 'close',\n", + " 'high',\n", + " 'low',\n", + " 'vol',\n", + " 'amount',\n", + " 'datetime',\n", + " 'concrete_high',\n", + " 'concrete_low',\n", + " 'is_pivot_low',\n", + " 'pivot_low_price',\n", + " 'is_pivot_high',\n", + " 'pivot_high_price',\n", + " 'is_potential_low',\n", + " 'is_potential_high',\n", + " 'freq',\n", + " 'symbol',\n", + " 'index',\n", + " 'last_valid_LH_pivot_price',\n", + " 'valid_LH_length',\n", + " 'last_valid_HL_pivot_price',\n", + " 'valid_HL_length',\n", + " 'high_broke_last_priod_LH',\n", + " 'sr_rate',\n", + " 'sr_rate_valid',\n", + " 'trend_sr_valid',\n", + " 'entry_sr',\n", + " 'exit_sr']" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# df = df.drop('sr_rate_list')\n", + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "df = df.drop('exit')\n", + "df = df.drop('entry')\n", + "df = df.drop('exit_sr')\n", + "df = df.drop('entry_sr')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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first_valid_index" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "149 2023-01-06 00:00:00\n", + "12.99\n", + "33 147 2022-12-23 00:00:00 12.55\n", + "-- invalid: 32 143 2022-11-25 00:00:00 15.84\n", + "31 140 2022-11-04 00:00:00 12.12\n", + "-- invalid: 30 136 2022-09-30 00:00:00 12.86\n", + "-- invalid: 29 133 2022-09-09 00:00:00 15.68\n", + "-- invalid: 28 129 2022-08-12 00:00:00 13.85\n", + "-- invalid: 27 125 2022-07-15 00:00:00 13.11\n", + "Last valid pivot price: 12.55\n" + ] + }, + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_last_valid_pivot_low_price(len(df)-1, show_print=True, return_length=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "MAX_INVALID_NUM = 3\n", + "\n", + "\n", + "def get_last_valid_pivot_price(index, show_print=False, return_price=False, return_length=False):\n", + " if show_print:\n", + " print(index, df['datetime'][index])\n", + "\n", + " p = df['concrete_high'][index]\n", + " if show_print:\n", + " print(p)\n", + "\n", + " invalid_num = 0\n", + "\n", + " last_valid_pivot = None\n", + "\n", + " df_tmp = df[:index]\n", + "\n", + " first_valid_index = None\n", + "\n", + " high_index = df_tmp[ df_tmp['is_pivot_high'] ][['pivot_high_price', 'index']]['index'].to_list()\n", + " for i in reversed( list(range(len(high_index))) ):\n", + " i_raw = high_index[i]\n", + "\n", + " cur_price = df['pivot_high_price'][i_raw]\n", + "\n", + " if cur_price >= p:\n", + " first_valid_index = i_raw\n", + " if last_valid_pivot is None:\n", + " last_valid_pivot = df['pivot_high_price'][i_raw]\n", + " p = cur_price\n", + " if show_print:\n", + " print(i, i_raw, df['datetime'][i_raw], df['pivot_high_price'][i_raw])\n", + " invalid_num = 0\n", + " else:\n", + " if show_print:\n", + " print('-- invalid: ', i, i_raw, df['datetime'][i_raw], df['pivot_high_price'][i_raw])\n", + " invalid_num += 1\n", + " if invalid_num > MAX_INVALID_NUM:\n", + " break\n", + "\n", + " if show_print:\n", + " print('Last valid pivot price: ', last_valid_pivot)\n", + "\n", + " if return_price:\n", + " return last_valid_pivot\n", + " if return_length:\n", + " if first_valid_index is None:\n", + " return 0\n", + " return index - first_valid_index" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "143 2022-11-25 00:00:00\n", + "16.35\n", + "30 142 2022-11-18 00:00:00 16.47\n", + "29 134 2022-09-16 00:00:00 17.15\n", + "28 133 2022-09-09 00:00:00 17.15\n", + "-- invalid: 27 131 2022-08-26 00:00:00 16.46\n", + "-- invalid: 26 127 2022-07-29 00:00:00 15.41\n", + "25 121 2022-06-17 00:00:00 17.49\n", + "-- invalid: 24 118 2022-05-27 00:00:00 16.78\n", + "23 105 2022-02-25 00:00:00 19.29\n", + "22 96 2021-12-17 00:00:00 26.33\n", + "21 93 2021-11-26 00:00:00 26.99\n", + "-- invalid: 20 81 2021-09-03 00:00:00 19.65\n", + "-- invalid: 19 78 2021-08-13 00:00:00 20.05\n", + "-- invalid: 18 72 2021-07-02 00:00:00 16.64\n", + "-- invalid: 17 67 2021-05-28 00:00:00 17.17\n", + "Last valid pivot price: 16.47\n" + ] + }, + { + "data": { + "text/plain": [ + "50" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_last_valid_pivot_price(len(df)-7, show_print=True, return_length=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "df['last_valid_LH_pivot_price'] = df['index'].apply(lambda x: get_last_valid_pivot_price(index=x, return_price=True)).to_list()\n", + "df['valid_LH_length'] = df['index'].apply(lambda x: get_last_valid_pivot_price(index=x, return_length=True)).to_list()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "df['last_valid_HL_pivot_price'] = df['index'].apply(lambda x: get_last_valid_pivot_low_price(index=x, return_price=True)).to_list()\n", + "df['valid_HL_length'] = df['index'].apply(lambda x: get_last_valid_pivot_low_price(index=x, return_length=True)).to_list()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "shape: (5, 22)\n", + "┌───────┬───────┬───────┬───────┬─────┬───────┬───┬──────┬───┐\n", + "│ open ┆ close ┆ high ┆ low ┆ ... ┆ last_ ┆ v ┆ last ┆ v │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ valid ┆ a ┆ _val ┆ a │\n", + "│ f64 ┆ f64 ┆ f64 ┆ f64 ┆ ┆ _LH_p ┆ l ┆ id_H ┆ l │\n", + "│ ┆ ┆ ┆ ┆ ┆ ivot_ ┆ i ┆ L_pi ┆ i │\n", + "│ ┆ ┆ ┆ ┆ ┆ price ┆ d ┆ vot_ ┆ d │\n", + "│ ┆ ┆ ┆ ┆ ┆ --- ┆ _ ┆ pric ┆ _ │\n", + "│ ┆ ┆ ┆ ┆ ┆ f64 ┆ L ┆ e ┆ H │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ H ┆ --- ┆ L │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ _ ┆ f64 ┆ _ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ l ┆ ┆ l │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ e ┆ ┆ e │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ n ┆ ┆ n │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ g ┆ ┆ g │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ t ┆ ┆ t │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ h ┆ ┆ h │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ - ┆ ┆ - │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ - ┆ ┆ - │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ - ┆ ┆ - │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ i ┆ ┆ i │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ 6 ┆ ┆ 6 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ 4 ┆ ┆ 4 │\n", + "╞═══════╪═══════╪═══════╪═══════╪═════╪═══════╪═══╪══════╪═══╡\n", + "│ 21.41 ┆ 22.48 ┆ 23.95 ┆ 21.32 ┆ ... ┆ null ┆ 0 ┆ null ┆ 0 │\n", + "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌┼╌╌╌┤\n", + "│ 22.48 ┆ 23.92 ┆ 24.26 ┆ 22.46 ┆ ... ┆ null ┆ 0 ┆ null ┆ 0 │\n", + "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌┼╌╌╌┤\n", + "│ 24.05 ┆ 22.99 ┆ 25.17 ┆ 22.6 ┆ ... ┆ null ┆ 0 ┆ null ┆ 0 │\n", + "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌┼╌╌╌┤\n", + "│ 23.3 ┆ 23.99 ┆ 25.38 ┆ 23.3 ┆ ... ┆ 24.05 ┆ 1 ┆ null ┆ 0 │\n", + "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌┼╌╌╌┤\n", + "│ 23.4 ┆ 23.13 ┆ 25.95 ┆ 21.3 ┆ ... ┆ 24.05 ┆ 2 ┆ null ┆ 0 │\n", + "└───────┴───────┴───────┴───────┴─────┴───────┴───┴──────┴───┘" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "逻辑1\n", + "- 上一个周期 LH\n", + "- 当前价 broke LH\n", + " - last valid LH pivot price\n", + " - if cur > last valid LH pivot\n", + "- 当前价 有HL\n", + "\n", + "再拆解\n", + "- break 上个周期的 trend" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "shape: (10,)\n", + "Series: 'last_valid_LH_pivot_price' [f64]\n", + "[\n", + "\t17.15\n", + "\t17.15\n", + "\t17.15\n", + "\t16.47\n", + "\t17.15\n", + "\t17.15\n", + "\t16.76\n", + "\t16.76\n", + "\t16.76\n", + "\t16.76\n", + "]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['last_valid_LH_pivot_price'].tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "shape: (10,)\n", + "Series: 'high_broke_last_priod_LH' [bool]\n", + "[\n", + "\tfalse\n", + "\tfalse\n", + "\tfalse\n", + "\tfalse\n", + "\tfalse\n", + "\tfalse\n", + "\tfalse\n", + "\tfalse\n", + "\tfalse\n", + "\tfalse\n", + "]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 进仓条件\n", + "df['high_broke_last_priod_LH'] = ( \\\n", + " (df['concrete_high'] > df['last_valid_LH_pivot_price'].shift()) \\\n", + " & (df['valid_LH_length'].shift() > 12) \\\n", + " & (df['valid_HL_length'].shift() > 6) \\\n", + " ).fill_null(False)\n", + "df['high_broke_last_priod_LH'].tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# 导出 Visual 数据文件\n", + "df.to_parquet(f'../analyzer.view.server/dump/cn_weekly/compute/{symbol}_with_length_threshold.1W.parquet')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "策略测试\n", + "- 日k高点 broke 上个周期 LH\n", + "- Entry: 上个周期的 last_valid_LH_price\n", + "- Exit: Open of next mkt day" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "# 进仓条件列类型是 Bool, 和Float相乘会只保留 条件为True的行\n", + "# 进仓价是前日的 last_valid_LH_pivot_price 指标\n", + "df['entry'] = df['last_valid_LH_pivot_price'].shift() * df['high_broke_last_priod_LH'] * 1.005 # 设定了 0.005 的滑点\n", + "\n", + "# 以下一天的开盘作为出仓价，注意因为最后一天还不知道第二天的开盘价，这里设定了当日收盘价来计算\n", + "df['exit'] = df['open'].shift(-1).fill_null(df['close'][-1]) * 0.995 # 设定了 0.005 的滑点" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "df_pnl = ((df['exit'] - df['entry']) / df['entry'])[ df['high_broke_last_priod_LH'] ]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pd.Series( df_pnl ).plot(kind='bar')" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "32" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(df_pnl)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.005417386525550511, 0.03996492926747724)" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_pnl.mean(), df_pnl.std()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sharpe = 2.1518452951472486\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "print(f\"Sharpe = {np.sqrt(252) * df_pnl.mean() / df_pnl.std()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pd.Series(cumret).plot()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- 策略结合SR筹码比例" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "def get_support_rate(cur_price, date_str):\n", + " fname = f'cn_data_range_vol/period_20/002594/{date_str}.parquet'\n", + " if not os.path.exists(fname):\n", + " # print(fname)\n", + " return 0.0\n", + " df_joint = pl.read_parquet(fname)\n", + " # print(df_joint)\n", + "\n", + "\n", + " cur_price = int(cur_price * 100)\n", + "\n", + " df_above = df_joint.filter(pl.col('range') < cur_price + 20 * 100) \\\n", + " .filter(pl.col('range') > cur_price)\n", + " df_below = df_joint.filter(pl.col('range') > cur_price - 20 * 100) \\\n", + " .filter(pl.col('range') <= cur_price)\n", + "\n", + " rate = round(df_below['vol'].sum() / (df_below['vol'].sum() + df_above['vol'].sum()), 2)\n", + " # print(rate)\n", + " return rate" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(datetime.datetime(2021, 2, 2, 0, 0), 15.43)\n", + "(datetime.datetime(2021, 6, 8, 0, 0), 17.09)\n", + "(datetime.datetime(2021, 7, 16, 0, 0), 17.16)\n", + "(datetime.datetime(2021, 8, 24, 0, 0), 19.11)\n", + "(datetime.datetime(2021, 8, 26, 0, 0), 19.49)\n", + "(datetime.datetime(2021, 9, 8, 0, 0), 19.19)\n", + "(datetime.datetime(2021, 9, 9, 0, 0), 19.65)\n", + "(datetime.datetime(2021, 12, 15, 0, 0), 25.8)\n", + "(datetime.datetime(2021, 12, 17, 0, 0), 26.0)\n", + "(datetime.datetime(2022, 1, 6, 0, 0), 24.4)\n", + "(datetime.datetime(2022, 2, 23, 0, 0), 19.34)\n", + "(datetime.datetime(2022, 2, 24, 0, 0), 19.86)\n", + "(datetime.datetime(2022, 3, 3, 0, 0), 19.7)\n", + "(datetime.datetime(2022, 5, 12, 0, 0), 13.79)\n", + "(datetime.datetime(2022, 5, 19, 0, 0), 14.41)\n", + "(datetime.datetime(2022, 7, 14, 0, 0), 14.3)\n", + "(datetime.datetime(2022, 7, 21, 0, 0), 13.95)\n", + "(datetime.datetime(2022, 7, 28, 0, 0), 14.59)\n", + "(datetime.datetime(2022, 10, 18, 0, 0), 13.42)\n", + "(datetime.datetime(2022, 10, 26, 0, 0), 13.26)\n", + "(datetime.datetime(2022, 11, 30, 0, 0), 16.43)\n", + "(datetime.datetime(2023, 1, 5, 0, 0), 13.38)\n", + "(datetime.datetime(2023, 1, 6, 0, 0), 14.11)\n", + "(datetime.datetime(2023, 1, 11, 0, 0), 14.8)\n" + ] + } + ], + "source": [ + "def sr_rate(x):\n", + " try:\n", + " date_str = datetime.strftime(x[0] - timedelta(days=1), '%Y-%m-%d')\n", + " entry = x[1]\n", + " if entry is not None and entry > 0.0:\n", + " rate = get_support_rate(entry, date_str)\n", + " return rate\n", + " return 0.0\n", + " except Exception as e:\n", + " print(x)\n", + " return 0.0\n", + "\n", + "\n", + "sr_rate_list = df[['datetime', 'entry']].apply(lambda x: sr_rate(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "730" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "df['sr_rate'] = sr_rate_list['apply']" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "df['sr_rate_valid'] = (df['sr_rate'] > 0.75)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "df['trend_sr_valid'] = (df['high_broke_last_priod_LH'] & df['sr_rate_valid'])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# 进仓条件列类型是 Bool, 和Float相乘会只保留 条件为True的行\n", + "# 进仓价是前日的 last_valid_LH_pivot_price 指标\n", + "df['entry_sr'] = df['last_valid_LH_pivot_price'].shift() * df['trend_sr_valid'] * 1.01\n", + "\n", + "# 以下一天的开盘作为出仓价，注意因为最后一天还不知道第二天的开盘价，这里设定了当日收盘价来计算\n", + "df['exit_sr'] = df['open'].shift(-1).fill_null(df['close'][-1]) * 0.99" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "df_pnl = ((df['exit_sr'] - df['entry_sr']) / df['entry_sr'])[ df['trend_sr_valid'] ]\n", + "if len(df_pnl) > 0:\n", + " pd.Series( df_pnl ).plot(kind='bar')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "unsupported operand type(s) for *: 'float' and 'NoneType'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_13528/1937716509.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Sharpe = {np.sqrt(252) * df_pnl.mean() / df_pnl.std()}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mcumret\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcumprod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdf_pnl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcumret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'float' and 'NoneType'" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "print(f\"Sharpe = {np.sqrt(252) * df_pnl.mean() / df_pnl.std()}\")\n", + "cumret=(np.cumprod(1+df_pnl)-1)\n", + "pd.Series(cumret).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['open',\n", + " 'close',\n", + " 'high',\n", + " 'low',\n", + " 'vol',\n", + " 'amount',\n", + " 'datetime',\n", + " 'concrete_high',\n", + " 'concrete_low',\n", + " 'is_pivot_low',\n", + " 'pivot_low_price',\n", + " 'is_pivot_high',\n", + " 'pivot_high_price',\n", + " 'is_potential_low',\n", + " 'is_potential_high',\n", + " 'freq',\n", + " 'symbol',\n", + " 'index',\n", + " 'last_valid_LH_pivot_price',\n", + " 'valid_LH_length',\n", + " 'last_valid_HL_pivot_price',\n", + " 'valid_HL_length',\n", + " 'high_broke_last_priod_LH',\n", + " 'sr_rate',\n", + " 'sr_rate_valid',\n", + " 'trend_sr_valid',\n", + " 'entry_sr',\n", + " 'exit_sr']" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# df = df.drop('sr_rate_list')\n", + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "df = df.drop('exit')\n", + "df = df.drop('entry')\n", + "df = df.drop('exit_sr')\n", + "df = df.drop('entry_sr')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Vol\n", + "2. AVol\n", + "3. RVol" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import polars as pl\n", + "from tqdm import tqdm\n", + "from datetime import datetime" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1925/1925 [03:49<00:00, 8.37it/s]\n" + ] + } + ], + "source": [ + "for code in tqdm(os.listdir(f'cn_data/')):\n", + " dates = []\n", + " vols = []\n", + "\n", + " for fname in os.listdir(f'cn_data/{code}'):\n", + " date_str = fname.split('.')[0]\n", + "\n", + " df = pl.read_csv(f'cn_data/{code}/{fname}')\n", + "\n", + " rs = df.filter((pl.col('time_str') == '09:25') | (pl.col('time_str') == '09:30') | (pl.col('time_str') == '09:31') | (pl.col('time_str') == '09:32') | (pl.col('time_str') == '09:33') | (pl.col('time_str') == '09:34') | (pl.col('time_str') == '09:35'))\n", + "\n", + " vol = rs['vol'].sum()\n", + " \n", + " dates.append(date_str)\n", + " vols.append(vol)\n", + "\n", + " df = pl.DataFrame({\n", + " 'date_str': dates,\n", + " 'vol': vols\n", + " }).sort('date_str')\n", + "\n", + " avol_30 = df['vol'].shift(1).rolling_mean(30).round(3)\n", + " df['avol_30'] = avol_30.to_list()\n", + " df['rvol_30'] = (df['vol'] / df['avol_30']).round(3)\n", + "\n", + " #\n", + " df['avol_30'] = df['avol_30'].fill_nan(0.0).fill_null(0.0)\n", + " df['rvol_30'] = df['rvol_30'].fill_nan(0.0).fill_null(0.0)\n", + "\n", + " #\n", + " df['vol'] = df['vol'].apply(lambda x: int(x))\n", + "\n", + " #\n", + " df['freq'] = ['1d'] * len(df)\n", + "\n", + " #\n", + " dts = []\n", + " for tstr in df['date_str']:\n", + " dts.append(datetime.strptime(tstr, '%Y%m%d'))\n", + " df['time'] = dts\n", + " \n", + " df.to_csv(f'cn_data_rvol/data/{code}.csv')\n", + " df.to_parquet(f'cn_data_rvol/data_parquet/{code}.parquet')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.4 (default, Jan 10 2023, 13:17:10) \n[GCC 7.5.0]" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "82328c1ac27e33c7f9ad051b9117ee9a5989011e2ef55b48106b5a4895113660" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/rvol_LHBreak.ipynb b/rvol_LHBreak.ipynb new file mode 100644 index 00000000..e78c3012 --- /dev/null +++ b/rvol_LHBreak.ipynb @@ -0,0 +1,801 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import polars as pl" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "symbol = '002594'\n", + "df_rvol = pl.read_parquet(f'cn_data_rvol/data_parquet/{symbol}.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "shape: (5, 19)\n", + "┌───────┬───────┬───────┬───────┬─────┬───────┬───────┬───┬───────┐\n", + "│ open ┆ close ┆ high ┆ low ┆ ... ┆ index ┆ last_ ┆ v ┆ high_ │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ valid ┆ a ┆ broke │\n", + "│ f64 ┆ f64 ┆ f64 ┆ f64 ┆ ┆ i64 ┆ _LH_p ┆ l ┆ _last │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ivot_ ┆ i ┆ _prio │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ price ┆ d ┆ d_LH │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ --- ┆ _ ┆ --- │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ f64 ┆ L ┆ bool │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ H ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ _ ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ l ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ e ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ n ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ g ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ t ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ h ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ - ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ - ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ - ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ i ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 6 ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 4 ┆ │\n", + "╞═══════╪═══════╪═══════╪═══════╪═════╪═══════╪═══════╪═══╪═══════╡\n", + "│ 47.66 ┆ 46.92 ┆ 47.66 ┆ 46.61 ┆ ... ┆ 0 ┆ null ┆ 0 ┆ false │\n", + "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌┤\n", + "│ 51.61 ┆ 51.61 ┆ 51.61 ┆ 50.51 ┆ ... ┆ 1 ┆ null ┆ 0 ┆ false │\n", + "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌┤\n", + "│ 55.0 ┆ 53.44 ┆ 55.99 ┆ 53.0 ┆ ... ┆ 2 ┆ null ┆ 0 ┆ false │\n", + "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌┤\n", + "│ 54.01 ┆ 53.31 ┆ 54.75 ┆ 52.28 ┆ ... ┆ 3 ┆ 55.99 ┆ 1 ┆ false │\n", + "├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌┼╌╌╌╌╌╌╌┤\n", + "│ 53.49 ┆ 54.09 ┆ 54.54 ┆ 51.78 ┆ ... ┆ 4 ┆ 55.99 ┆ 2 ┆ false │\n", + "└───────┴───────┴───────┴───────┴─────┴───────┴───────┴───┴───────┘" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_lh_break = pl.read_parquet(f'../analyzer.view.server/dump/cn_pivot/compute/{symbol}_with_length_threshold.1d.parquet')\n", + "df_lh_break.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "df = df_lh_break.join(df_rvol, on='datetime', how='inner')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "df['rvol_lhbreak'] = df['high_broke_last_priod_LH'] & (df['rvol_30'] > 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "df = df.drop('time')" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "df.to_parquet(f'../analyzer.view.server/dump/cn_pivot_rvol/compute/{symbol}_rvol_hlbreak.1d.parquet')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.4 (default, Jan 10 2023, 13:17:10) \n[GCC 7.5.0]" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "82328c1ac27e33c7f9ad051b9117ee9a5989011e2ef55b48106b5a4895113660" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/stk_list/cn_list.csv b/stk_list/cn_list.csv new file mode 100644 index 00000000..bb465e57 --- /dev/null +++ b/stk_list/cn_list.csv @@ -0,0 +1,2025 @@ +symbol,name,tdx_api_mkt,code +SZ002875,安奈儿,0,002875 +SZ002873,新天药业,0,002873 +SZ002870,香山股份,0,002870 +SZ002869,金溢科技,0,002869 +SZ002868,绿康生化,0,002868 +SZ002867,周大生,0,002867 +SZ002866,传艺科技,0,002866 +SZ002865,钧达股份,0,002865 +SZ002864,盘龙药业,0,002864 +SZ002862,实丰文化,0,002862 +SZ002860,星帅尔,0,002860 +SZ002859,洁美科技,0,002859 +SZ002858,力盛体育,0,002858 +SZ002853,皮阿诺,0,002853 +SZ002852,道道全,0,002852 +SZ002851,麦格米特,0,002851 +SZ002850,科达利,0,002850 +SZ002848,高斯贝尔,0,002848 +SZ002847,盐津铺子,0,002847 +SZ002846,英联股份,0,002846 +SZ002845,同兴达,0,002845 +SZ002843,泰嘉股份,0,002843 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+SH600862,中航高科,1,600862 +SH600861,北京城乡,1,600861 +SH600860,京城股份,1,600860 +SH600859,王府井,1,600859 +SH600855,航天长峰,1,600855 +SH600850,电科数字,1,600850 +SH600846,同济科技,1,600846 +SH600845,宝信软件,1,600845 +SH600837,海通证券,1,600837 +SH600833,第一医药,1,600833 +SH600827,百联股份,1,600827 +SH600818,中路股份,1,600818 +SH600809,山西汾酒,1,600809 +SH600803,新奥股份,1,600803 +SH600801,华新水泥,1,600801 +SH600785,新华百货,1,600785 +SH600783,鲁信创投,1,600783 diff --git a/stk_list/cn_list.ipynb b/stk_list/cn_list.ipynb new file mode 100644 index 00000000..b29cdd81 --- /dev/null +++ b/stk_list/cn_list.ipynb @@ -0,0 +1,78 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "import polars as pl" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "items = []\n", + "for fname in os.listdir('data'):\n", + " with open(f'data/{fname}', 'r') as fr:\n", + " c = fr.read()\n", + " js = json.loads(c)['data']['list']\n", + " for j in js:\n", + " items.append({\n", + " 'symbol': j['symbol'],\n", + " 'name': j['name'],\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "df = pl.DataFrame(items)\n", + "\n", + "df['tdx_api_mkt'] = df['symbol'].apply(lambda x: 0 if x[:2] == 'SZ' else 1)\n", + "df['code'] = df['symbol'].apply(lambda x: x[2:])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv('cn_list.csv')" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "b09ccace7a263505cbe8d3abcd75bd5dbc38ab4b50bda5dfb585d8cccb8cf538" + }, + "kernelspec": { + "display_name": "Python 3.7.6 64-bit ('3.7.6')", + "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": "3.7.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/stk_list/data/cn_list_1.json b/stk_list/data/cn_list_1.json new file mode 100644 index 00000000..27ba905a --- /dev/null +++ b/stk_list/data/cn_list_1.json @@ -0,0 +1,2809 @@ +{ + "data": { + "count": 2024, + "list": [ + { + "pct": 9.99, + "symbol": "SZ301391", + "current": 109, + "amount": 250739503.84, + "name": "卡莱特", + "exchange": "sh_sz", + "type": 11, + "areacode": "440000", + "fmc": 1646467672, + "tick_size": 0.01, + "has_follow": false, + "indcode": "S7101" + }, + { + "pct": -1.15, + "symbol": "SZ301377", + "current": 22.38, + "amount": 139173544.7, + "name": "鼎泰高科", + "exchange": "sh_sz", + "type": 11, + "areacode": "440000", + "fmc": 1050684334, + "tick_size": 0.01, + "has_follow": false, + "indcode": "S6401" + }, + { + "pct": 0.8, + "symbol": "SZ301369", + "current": 96.15, + "amount": 75629622.71, + "name": "联动科技", + "exchange": "sh_sz", + "type": 11, + "areacode": "440000", + "fmc": 1115344327, + "tick_size": 0.01, + "has_follow": false, + "indcode": "S2701" + }, + { + "pct": 0, + "symbol": "SZ301367", + "current": 220.2, + "amount": 134101466.64, + "name": "怡和嘉业", + "exchange": "sh_sz", + "type": 11, + "areacode": "110000", + "fmc": 3341401779, + "tick_size": 0.01, + "has_follow": false, + "indcode": "S3705" + }, + { + "pct": 2.17, + "symbol": "SZ301363", + "current": 42.31, + "amount": 101161989, + "name": "美好医疗", + "exchange": "sh_sz", + "type": 11, + "areacode": "440000", + "fmc": 1598844678, + "tick_size": 0.01, + "has_follow": false, + "indcode": "S3705" + }, + { + "pct": -2.72, + "symbol": "SZ301349", + "current": 113.96, + "amount": 191319584.91, + "name": "信德新材", + "exchange": "sh_sz", + "type": 11, + "areacode": "210000", + "fmc": 1837218106, + "tick_size": 0.01, + "has_follow": false, + "indcode": "S6307" + }, + { + "pct": 1.41, + "symbol": "SZ301339", + "current": 17.95, + 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"SH600801", + "current": 15.28, + "amount": 69308871, + "name": "华新水泥", + "exchange": "sh_sz", + "type": 11, + "areacode": "420000", + "fmc": 20809524184, + "tick_size": 0.01, + "has_follow": false, + "indcode": "S6101" + }, + { + "pct": 7.24, + "symbol": "SH600785", + "current": 19.55, + "amount": 309910625, + "name": "新华百货", + "exchange": "sh_sz", + "type": 11, + "areacode": "640000", + "fmc": 4411091524, + "tick_size": 0.01, + "has_follow": false, + "indcode": "S4503" + }, + { + "pct": -0.83, + "symbol": "SH600783", + "current": 13.13, + "amount": 51132618, + "name": "鲁信创投", + "exchange": "sh_sz", + "type": 11, + "areacode": "370000", + "fmc": 9773437530, + "tick_size": 0.01, + "has_follow": false, + "indcode": "S6401" + } + ] + }, + "error_code": 0, + "error_description": "" +} diff --git a/support_resistance/README.md b/support_resistance/README.md new file mode 100644 index 00000000..e8764251 --- /dev/null +++ b/support_resistance/README.md @@ -0,0 +1 @@ +- Support / Resistance diff --git a/support_resistance/sr.ipynb b/support_resistance/sr.ipynb new file mode 100644 index 00000000..397e9a55 --- /dev/null +++ b/support_resistance/sr.ipynb @@ -0,0 +1,45 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Spreading\n", + "- ATR\n", + "- 集中度\n", + " - 百分比\n", + " - 几倍ATR\n", + "- 最终特征\n", + " - 当前价格对应的得分 区间+-100\n", + "- 组件特征\n", + " - 密集区间\n", + " - 价格范围\n", + " - 密集程度\n", + " - 稀疏区间\n", + " - 价格范围\n", + " - 稀疏程度\n", + " - 当前价格上下密集度对比\n", + "- 不同周期\n", + " - Fixed number\n", + " - Combined w/ trend\n", + " " + ] + } + ], + "metadata": { + "interpreter": { + "hash": "b09ccace7a263505cbe8d3abcd75bd5dbc38ab4b50bda5dfb585d8cccb8cf538" + }, + "kernelspec": { + "display_name": "Python 3.7.6 64-bit ('3.7.6')", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/tdx/cn_tick_intraday_data.1m.csv b/tdx/cn_tick_intraday_data.1m.csv new file mode 100644 index 00000000..f5810241 --- /dev/null +++ b/tdx/cn_tick_intraday_data.1m.csv @@ -0,0 +1,18 @@ +datetime,open,high,low,close,volume,symbol,freq +2023-01-06T09:25:00.000000000,5.78,5.85,5.78,5.85,10667,300507,1m +2023-01-06T09:26:00.000000000,5.85,5.87,5.84,5.86,10763,300507,1m +2023-01-06T09:27:00.000000000,5.86,5.9,5.85,5.89,10719,300507,1m +2023-01-06T09:28:00.000000000,5.9,5.91,5.88,5.88,10774,300507,1m +2023-01-06T09:29:00.000000000,5.89,5.89,5.83,5.85,10680,300507,1m +2023-01-06T09:30:00.000000000,5.84,5.89,5.82,5.89,10860,300507,1m +2023-01-06T09:31:00.000000000,5.89,5.95,5.89,5.94,12312,300507,1m +2023-01-06T09:32:00.000000000,5.96,6.02,5.96,6.0,11088,300507,1m +2023-01-06T09:33:00.000000000,6.01,6.01,5.97,5.99,11125,300507,1m +2023-01-06T09:34:00.000000000,6.0,6.02,5.99,6.02,10778,300507,1m +2023-01-06T09:35:00.000000000,6.02,6.02,5.98,5.98,10705,300507,1m +2023-01-06T09:36:00.000000000,5.98,5.99,5.94,5.94,11377,300507,1m +2023-01-06T09:37:00.000000000,5.95,5.97,5.94,5.94,10890,300507,1m +2023-01-06T09:38:00.000000000,5.94,5.95,5.93,5.95,10876,300507,1m +2023-01-06T09:39:00.000000000,5.96,5.97,5.94,5.95,10651,300507,1m +2023-01-06T09:40:00.000000000,5.95,5.98,5.95,5.98,11857,300507,1m +2023-01-06T09:41:00.000000000,5.98,5.98,5.98,5.98,12,300507,1m diff --git a/tdx/cn_tick_intraday_data.1m.parquet b/tdx/cn_tick_intraday_data.1m.parquet new file mode 100644 index 00000000..c2d8d891 Binary files /dev/null and b/tdx/cn_tick_intraday_data.1m.parquet differ diff --git a/tdx/get_tick_data.ipynb b/tdx/get_tick_data.ipynb new file mode 100644 index 00000000..06c73b1d --- /dev/null +++ b/tdx/get_tick_data.ipynb @@ -0,0 +1,3244 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pytdx.hq import TdxHq_API\n", + "from pytdx.params import TDXParams\n", + "from datetime import datetime, timedelta\n", + "import pandas as pd\n", + "import polars as pl\n", + "import os\n", + "from tqdm import tqdm\n", + "\n", + "HOST = '119.147.212.81'\n", + "PORT = 7709\n", + "\n", + "API_TICK_SIZE = 2000" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "api = TdxHq_API()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "api" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " time price vol buyorsell\n", + "0 11:24 6.78 1 1\n", + "1 11:24 6.78 2 1\n", + "2 11:25 6.79 41 0\n", + "3 11:25 6.78 10 0\n", + "4 11:25 6.77 1 1" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with api.connect(HOST, PORT):\n", + " data = api.get_history_transaction_data(TDXParams.MARKET_SZ, '300507', 0, API_TICK_SIZE, 20230111)\n", + " df_tmp = api.to_df(data)\n", + "\n", + "df_tmp.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def get_historical_transaction(api, mkt_code, symbol, d_int):\n", + " offset = 0\n", + " dfs = []\n", + " while True:\n", + " data = api.get_history_transaction_data(mkt_code, symbol, offset, API_TICK_SIZE, d_int)\n", + " if data is None or len(data) == 0:\n", + " break\n", + " df_tmp = api.to_df(data)\n", + " dfs.insert(0, df_tmp)\n", + " offset += API_TICK_SIZE\n", + "\n", + " if len(dfs) == 0:\n", + " return None\n", + "\n", + " df = pd.concat(dfs)\n", + " # print(df.head())\n", + " df_pl = pl.DataFrame({\n", + " 'index': df.index.to_list(),\n", + " 'time_str': df['time'].to_list(),\n", + " 'price': df['price'].to_list(),\n", + " 'vol': df['vol'].to_list(),\n", + " 'buyorsell': df['buyorsell'].to_list()\n", + " })\n", + " return df_pl" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def get_stk_tick_data(mkt_code=TDXParams.MARKET_SZ, symbol='300507', base_d=datetime(year=2022, month=10, day=9)):\n", + " with api.connect(HOST, PORT):\n", + " for i in list(range(60)):\n", + " d = base_d - timedelta(days=i)\n", + " d_str = datetime.strftime(d, '%Y%m%d')\n", + " d_int = int(d_str)\n", + "\n", + " df = get_historical_transaction(api, mkt_code, symbol, d_int)\n", + " if df is None:\n", + " continue\n", + " if not os.path.exists(f'../cn_data/{symbol}'):\n", + " os.mkdir(f'../cn_data/{symbol}')\n", + "\n", + " df.to_csv(f'../cn_data/{symbol}/{d_str}.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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name: rvol_30 + type: Splines + indexSrc: data + dataQuery: + type: parquet + source: __file__ + setting_schema: [cols, colors, scale] + setting_table: + - [rvol_30, "#ff1f6d", 1.0] diff --git a/visual_config/cn_pivot.tvjsconf.yml b/visual_config/cn_pivot.tvjsconf.yml new file mode 100644 index 00000000..0145b5b9 --- /dev/null +++ b/visual_config/cn_pivot.tvjsconf.yml @@ -0,0 +1,29 @@ +version: 3.0 +1d: + chartSettings: + infer_from_file: false + chart: + type: Candles + indexBased: true + dataQuery: + type: parquet + source: __file__ + offchart: + - name: pivot_high_price + type: Splines + indexSrc: data + dataQuery: + type: parquet + source: __file__ + setting_schema: [cols, colors, scale] + setting_table: + - [pivot_high_price, "#ff1f6d", 1.0] + - name: pivot_low_price + type: Splines + indexSrc: data + dataQuery: + type: parquet + source: __file__ + setting_schema: [cols, colors, scale] + setting_table: + - [pivot_low_price, "#ff1f6d", 1.0] diff --git a/visual_config/demo_ema.tvjsconf.yml b/visual_config/demo_ema.tvjsconf.yml new file mode 100644 index 00000000..a5c1d172 --- /dev/null +++ b/visual_config/demo_ema.tvjsconf.yml @@ -0,0 +1,67 @@ +version: 3.0 +1d: + chartSettings: + infer_from_file: false + chart: + type: Candles + indexBased: true + dataQuery: + type: parquet + source: __file__ + onchart: + - name: demo_ema + type: Splines + indexSrc: data + dataQuery: + type: parquet + source: __file__ + setting_schema: [cols, colors] + setting_table: + - [ema27, "#ff1f6d"] + - [ema50, "#dc00f5"] + - [ema200, "#5e00f5"] + - [std50, "#5C3317"] + - name: sig + type: VertLines + indexSrc: data + dataQuery: + type: parquet + source: __file__ + setting_schema: [cols] + setting_table: + - [sig_buy] + - [sig_sell] + - name: diff_sig + type: Trades + indexSrc: data + dataQuery: + type: parquet + source: __diff_file__ + setting_schema: [cols] + setting_table: + - [sig_buy] + - [sig_sell] + offchart: + - name: signals + type: Splines + indexSrc: data + dataQuery: + type: parquet + source: __file__ + setting_schema: [cols, colors, scale] + setting_table: + - [cond1, "#ff1f6d", 1.0] + - [cond2, "#dc00f5", 0.8] + - [cond3, "#5e00f5", 0.6] + # - [cond4, "#00c2cc", 0.4] + - [condout_1, "#00c2cc", -0.8] + - [condout_2, "#8db531", -1.0] + - name: score + type: Splines + indexSrc: data + dataQuery: + type: parquet + source: __file__ + setting_schema: [cols, colors] + setting_table: + - [score, "#ff00ff"] \ No newline at end of file diff --git a/visual_config/us.tvjsconf.yml b/visual_config/us.tvjsconf.yml new file mode 100644 index 00000000..bbcc2c16 --- /dev/null +++ b/visual_config/us.tvjsconf.yml @@ -0,0 +1,10 @@ +version: 3.0 +1m: + chartSettings: + infer_from_file: false + chart: + type: Candles + indexBased: true + dataQuery: + type: parquet + source: __file__