diff --git a/research/basic.ipynb b/research/basic.ipynb
index d0006de..bc03b18 100644
--- a/research/basic.ipynb
+++ b/research/basic.ipynb
@@ -2,133 +2,12 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 45,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "Activating profile profile1\n",
- "
\n"
- ],
- "text/plain": [
- "Activating profile profile1\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "Profile profile1 loaded successfully.\n",
- "
\n"
- ],
- "text/plain": [
- "Profile profile1 loaded successfully.\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "Current values:\n",
- "{\n",
- " 'AGG_EXCLUDED_TRADES': ['C', 'O', '4', 'B', '7', 'V', 'P', 'W', 'U', 'Z', 'F'],\n",
- " 'BT_DELAYS': {\n",
- " 'trigger_to_strat': 0.02,\n",
- " 'strat_to_sub': 0.023,\n",
- " 'sub_to_fill': 0.008,\n",
- " 'fill_to_not': 0.023,\n",
- " 'limit_order_offset': 0\n",
- " },\n",
- " 'BT_FILL_CONDITION_BUY_LIMIT': <FillCondition.SLOW: 'slow'>,\n",
- " 'BT_FILL_CONDITION_SELL_LIMIT': <FillCondition.SLOW: 'slow'>,\n",
- " 'BT_FILL_CONS_TRADES_REQUIRED': 2,\n",
- " 'BT_FILL_LOG_SURROUNDING_TRADES': 10,\n",
- " 'BT_FILL_PRICE_MARKET_ORDER_PREMIUM': 0.005,\n",
- " 'COUNT_API_REQUESTS': False,\n",
- " 'GROUP_TRADES_WITH_TIMESTAMP_LESS_THAN': 0.003,\n",
- " 'ILOG_SAVE_LEVEL_FROM': 1,\n",
- " 'LIVE_DATA_FEED': 'sip',\n",
- " 'NORMALIZED_TICK_BASE_PRICE': 30.0,\n",
- " 'OFFLINE_MODE': False,\n",
- " 'PROD_SERVER_HOSTNAMES': ['tradingeastcoast', 'David-MacBook-Pro.local'],\n",
- " 'QUIET_MODE': True,\n",
- " 'TEST_SERVER_HOSTNAMES': ['tradingtest']\n",
- "}\n",
- "\n"
- ],
- "text/plain": [
- "Current values:\n",
- "\u001b[1m{\u001b[0m\n",
- " \u001b[32m'AGG_EXCLUDED_TRADES'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'C'\u001b[0m, \u001b[32m'O'\u001b[0m, \u001b[32m'4'\u001b[0m, \u001b[32m'B'\u001b[0m, \u001b[32m'7'\u001b[0m, \u001b[32m'V'\u001b[0m, \u001b[32m'P'\u001b[0m, \u001b[32m'W'\u001b[0m, \u001b[32m'U'\u001b[0m, \u001b[32m'Z'\u001b[0m, \u001b[32m'F'\u001b[0m\u001b[1m]\u001b[0m,\n",
- " \u001b[32m'BT_DELAYS'\u001b[0m: \u001b[1m{\u001b[0m\n",
- " \u001b[32m'trigger_to_strat'\u001b[0m: \u001b[1;36m0.02\u001b[0m,\n",
- " \u001b[32m'strat_to_sub'\u001b[0m: \u001b[1;36m0.023\u001b[0m,\n",
- " \u001b[32m'sub_to_fill'\u001b[0m: \u001b[1;36m0.008\u001b[0m,\n",
- " \u001b[32m'fill_to_not'\u001b[0m: \u001b[1;36m0.023\u001b[0m,\n",
- " \u001b[32m'limit_order_offset'\u001b[0m: \u001b[1;36m0\u001b[0m\n",
- " \u001b[1m}\u001b[0m,\n",
- " \u001b[32m'BT_FILL_CONDITION_BUY_LIMIT'\u001b[0m: \u001b[1m<\u001b[0m\u001b[1;95mFillCondition.SLOW:\u001b[0m\u001b[39m \u001b[0m\u001b[32m'slow'\u001b[0m\u001b[39m>,\u001b[0m\n",
- "\u001b[39m \u001b[0m\u001b[32m'BT_FILL_CONDITION_SELL_LIMIT'\u001b[0m\u001b[39m: \u001b[0m,\n",
- " \u001b[32m'BT_FILL_CONS_TRADES_REQUIRED'\u001b[0m: \u001b[1;36m2\u001b[0m,\n",
- " \u001b[32m'BT_FILL_LOG_SURROUNDING_TRADES'\u001b[0m: \u001b[1;36m10\u001b[0m,\n",
- " \u001b[32m'BT_FILL_PRICE_MARKET_ORDER_PREMIUM'\u001b[0m: \u001b[1;36m0.005\u001b[0m,\n",
- " \u001b[32m'COUNT_API_REQUESTS'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
- " \u001b[32m'GROUP_TRADES_WITH_TIMESTAMP_LESS_THAN'\u001b[0m: \u001b[1;36m0.003\u001b[0m,\n",
- " \u001b[32m'ILOG_SAVE_LEVEL_FROM'\u001b[0m: \u001b[1;36m1\u001b[0m,\n",
- " \u001b[32m'LIVE_DATA_FEED'\u001b[0m: \u001b[32m'sip'\u001b[0m,\n",
- " \u001b[32m'NORMALIZED_TICK_BASE_PRICE'\u001b[0m: \u001b[1;36m30.0\u001b[0m,\n",
- " \u001b[32m'OFFLINE_MODE'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
- " \u001b[32m'PROD_SERVER_HOSTNAMES'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'tradingeastcoast'\u001b[0m, \u001b[32m'David-MacBook-Pro.local'\u001b[0m\u001b[1m]\u001b[0m,\n",
- " \u001b[32m'QUIET_MODE'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
- " \u001b[32m'TEST_SERVER_HOSTNAMES'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'tradingtest'\u001b[0m\u001b[1m]\u001b[0m\n",
- "\u001b[1m}\u001b[0m\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "config_handler.active_profile='profile1'\n",
- "
\n"
- ],
- "text/plain": [
- "config_handler.\u001b[33mactive_profile\u001b[0m=\u001b[32m'profile1'\u001b[0m\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "config handler initialized\n",
- "
\n"
- ],
- "text/plain": [
- "config handler initialized\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Realtime Websocket connection will use FEED: sip and credential of ACCOUNT1\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from v2realbot.tools.loadbatch import load_batch\n",
- "from v2realbot.utils.utils import zoneNY\n",
+ "from v2realbot.utils.utils import zoneNY, DATA_DIR\n",
"import pandas as pd\n",
"import numpy as np\n",
"import vectorbtpro as vbt\n",
@@ -143,69 +22,36 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "batch_id = \"e44a5075\"\n",
+ "# res, df = load_batch(batch_id=batch_id,\n",
+ "# space_resolution_evenly=False,\n",
+ "# indicators_columns=[\"Rsi14\"],\n",
+ "# main_session_only=True)\n",
+ "# if res < 0:\n",
+ "# print(\"Error\" + str(res) + str(df))\n",
+ "# df = df[\"bars\"]\n",
+ "# df.info()\n",
+ "# df.head()\n",
+ "# #df.ptable()\n",
+ "# df.to_pickle(DATA_DIR+\"/\"+f'{batch_id}.pickle')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## FILTERING"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
"metadata": {},
"outputs": [
- {
- "data": {
- "text/html": [
- "Resolution : 23\n",
- "
\n"
- ],
- "text/plain": [
- "Resolution : \u001b[1;36m23\u001b[0m\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "NOTE: DUPLICATES 100/3034 in bars. REMOVING.\n",
- "
\n"
- ],
- "text/plain": [
- "NOTE: DUPLICATES \u001b[1;36m100\u001b[0m/\u001b[1;36m3034\u001b[0m in bars. REMOVING.\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "Now there are 0/2934\n",
- "
\n"
- ],
- "text/plain": [
- "Now there are \u001b[1;36m0\u001b[0m/\u001b[1;36m2934\u001b[0m\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "DatetimeIndex: 2934 entries, 2024-03-11 09:30:00-04:00 to 2024-03-13 15:59:51-04:00\n",
- "Data columns (total 8 columns):\n",
- " # Column Non-Null Count Dtype \n",
- "--- ------ -------------- ----- \n",
- " 0 Open 2934 non-null float64 \n",
- " 1 High 2934 non-null float64 \n",
- " 2 Low 2934 non-null float64 \n",
- " 3 Close 2934 non-null float64 \n",
- " 4 Volume 2934 non-null int64 \n",
- " 5 Vwap 2934 non-null float64 \n",
- " 6 updated 2934 non-null datetime64[ns, US/Eastern]\n",
- " 7 Rsi14 2934 non-null float64 \n",
- "dtypes: datetime64[ns, US/Eastern](1), float64(6), int64(1)\n",
- "memory usage: 270.8 KB\n"
- ]
- },
{
"data": {
"text/html": [
@@ -421,35386 +267,179 @@
"[2934 rows x 8 columns]"
]
},
- "execution_count": 2,
+ "execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "res, df = load_batch(batch_id=\"e44a5075\",\n",
- " space_resolution_evenly=False,\n",
- " indicators_columns=[\"Rsi14\"],\n",
- " main_session_only=True)\n",
- "if res < 0:\n",
- " print(\"Error\" + str(res) + str(df))\n",
- "df = df[\"bars\"]\n",
- "df.info()\n",
- "df.head()\n",
- "#df.ptable()\n",
+ "df = pd.read_pickle(DATA_DIR+\"/\"+f'{batch_id}.pickle')\n",
"df"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## FILTERING"
- ]
- },
{
"cell_type": "code",
- "execution_count": 209,
+ "execution_count": 48,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "DatetimeIndex: 1961 entries, 2024-03-12 09:30:00-04:00 to 2024-03-13 15:59:51-04:00\n",
- "Data columns (total 8 columns):\n",
- " # Column Non-Null Count Dtype \n",
- "--- ------ -------------- ----- \n",
- " 0 Open 1961 non-null float64 \n",
- " 1 High 1961 non-null float64 \n",
- " 2 Low 1961 non-null float64 \n",
- " 3 Close 1961 non-null float64 \n",
- " 4 Volume 1961 non-null int64 \n",
- " 5 Vwap 1961 non-null float64 \n",
- " 6 updated 1961 non-null datetime64[ns, US/Eastern]\n",
- " 7 Rsi14 1961 non-null float64 \n",
- "dtypes: datetime64[ns, US/Eastern](1), float64(6), int64(1)\n",
- "memory usage: 137.9 KB\n",
- "\n",
- "DatetimeIndex: 1961 entries, 2024-03-12 09:30:00-04:00 to 2024-03-13 15:59:51-04:00\n",
- "Data columns (total 8 columns):\n",
- " # Column Non-Null Count Dtype \n",
- "--- ------ -------------- ----- \n",
- " 0 Open 1961 non-null float64 \n",
- " 1 High 1961 non-null float64 \n",
- " 2 Low 1961 non-null float64 \n",
- " 3 Close 1961 non-null float64 \n",
- " 4 Volume 1961 non-null int64 \n",
- " 5 Vwap 1961 non-null float64 \n",
- " 6 updated 1961 non-null datetime64[ns, US/Eastern]\n",
- " 7 Rsi14 1961 non-null float64 \n",
- "dtypes: datetime64[ns, US/Eastern](1), float64(6), int64(1)\n",
- "memory usage: 137.9 KB\n",
- "\n",
- "DatetimeIndex: 100 entries, 2024-03-12 10:09:00-04:00 to 2024-03-12 10:47:24-04:00\n",
- "Data columns (total 8 columns):\n",
- " # Column Non-Null Count Dtype \n",
- "--- ------ -------------- ----- \n",
- " 0 Open 100 non-null float64 \n",
- " 1 High 100 non-null float64 \n",
- " 2 Low 100 non-null float64 \n",
- " 3 Close 100 non-null float64 \n",
- " 4 Volume 100 non-null int64 \n",
- " 5 Vwap 100 non-null float64 \n",
- " 6 updated 100 non-null datetime64[ns, US/Eastern]\n",
- " 7 Rsi14 100 non-null float64 \n",
- "dtypes: datetime64[ns, US/Eastern](1), float64(6), int64(1)\n",
- "memory usage: 7.0 KB\n",
- "\n",
- "DatetimeIndex: 100 entries, 2024-03-12 10:09:00-04:00 to 2024-03-12 10:47:24-04:00\n",
- "Data columns (total 2 columns):\n",
- " # Column Non-Null Count Dtype \n",
- "--- ------ -------------- ----- \n",
- " 0 Open 100 non-null float64\n",
- " 1 High 100 non-null float64\n",
- "dtypes: float64(2)\n",
- "memory usage: 2.3 KB\n"
- ]
- },
- {
- "data": {
- "application/vnd.plotly.v1+json": {
- "config": {
- "plotlyServerURL": "https://plot.ly"
- },
- "data": [
- {
- "name": "Rsi14",
- "showlegend": true,
- "type": "scatter",
- "uid": "efdece63-8464-407b-87a7-6ee41d71a834",
- "x": [
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+ "outputs": [],
"source": [
+ "\n",
"#naloadujeme do vbt symbol as column\n",
"basic_data = vbt.Data.from_data({\"BAC\": df}, tz_convert=zoneNY)\n",
"start_date = pd.Timestamp('2024-03-12 09:30', tz=zoneNY)\n",
"end_date = pd.Timestamp('2024-03-13 16:00', tz=zoneNY)\n",
"\n",
+ "#filter date\n",
"#basic_data = basic_data.transform(lambda df: df[df.index.date == start_date.date()])\n",
+ "#filter range\n",
"basic_data = basic_data.transform(lambda df: df[(df.index >= start_date) & (df.index <= end_date)])\n",
- "basic_data.data[\"BAC\"].info()\n",
- "filtered_data = basic_data.transform(lambda df: df[(df.index >= start_date) & (df.index <= end_date)])\n",
+ "#filtered_data = basic_data.transform(lambda df: df[(df.index >= start_date) & (df.index <= end_date)])\n",
"# #range filtered_data = data[(data.index >= start_date) & (data.index <= end_date)\n",
- "\n",
- "filtered_data.data[\"BAC\"].info()\n",
- "b= filtered_data.get().iloc[100:200]\n",
- "b.info()\n",
- "c = b[[\"Open\",\"High\"]]\n",
- "c.info()\n",
- "rsi14 = filtered_data.data[\"BAC\"][\"Rsi14\"].rename(\"Rsi14\")\n",
- "\n",
- "rsi14.vbt.plot().show()\n",
- "filtered_data.data[\"BAC\"].vbt.ohlcv.plot().show()"
+ "#df.between_time('09:30', '16:00')\n",
+ "#(df.index.time >= pd.Timestamp('09:30').time()) & (df.index.time <= pd.Timestamp('16:00').time())"
]
},
{
"cell_type": "code",
- "execution_count": 210,
+ "execution_count": 49,
"metadata": {},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "time\n",
- "2024-03-12 09:30:00-04:00 0.000000\n",
- "2024-03-12 09:30:23-04:00 0.000000\n",
- "2024-03-12 09:30:47-04:00 0.000000\n",
- "2024-03-12 09:31:11-04:00 0.000000\n",
- "2024-03-12 09:31:34-04:00 0.000000\n",
- " ... \n",
- "2024-03-13 15:58:19-04:00 59.600097\n",
- "2024-03-13 15:58:42-04:00 58.207248\n",
- "2024-03-13 15:59:05-04:00 49.776325\n",
- "2024-03-13 15:59:28-04:00 37.387129\n",
- "2024-03-13 15:59:51-04:00 38.075336\n",
- "Name: Vwap, Length: 1961, dtype: float64\n",
- "['T', '_AXIS_LEN', '_AXIS_ORDERS', '_AXIS_TO_AXIS_NUMBER', '_HANDLED_TYPES', '__abs__', '__add__', '__and__', '__annotations__', '__array__', '__array_priority__', '__array_ufunc__', '__bool__', '__class__', '__column_consortium_standard__', '__contains__', '__copy__', '__deepcopy__', '__delattr__', '__delitem__', '__dict__', '__dir__', '__divmod__', '__doc__', '__eq__', '__finalize__', '__float__', '__floordiv__', '__format__', '__ge__', '__getattr__', '__getattribute__', '__getitem__', '__getstate__', '__gt__', '__hash__', '__iadd__', '__iand__', '__ifloordiv__', '__imod__', '__imul__', '__init__', '__init_subclass__', '__int__', '__invert__', '__ior__', '__ipow__', '__isub__', '__iter__', '__itruediv__', '__ixor__', '__le__', '__len__', '__lt__', '__matmul__', '__mod__', '__module__', '__mul__', '__ne__', '__neg__', '__new__', '__nonzero__', '__or__', '__pandas_priority__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rmatmul__', '__rmod__', '__rmul__', '__ror__', '__round__', '__rpow__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__setitem__', '__setstate__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__weakref__', '__xor__', '_accessors', '_accum_func', '_agg_examples_doc', '_agg_see_also_doc', '_align_for_op', '_align_frame', '_align_series', '_append', '_arith_method', '_as_manager', '_attrs', '_binop', '_can_hold_na', '_check_inplace_and_allows_duplicate_labels', '_check_is_chained_assignment_possible', '_check_label_or_level_ambiguity', '_check_setitem_copy', '_clear_item_cache', '_clip_with_one_bound', '_clip_with_scalar', '_cmp_method', '_consolidate', '_consolidate_inplace', '_construct_axes_dict', '_construct_result', '_constructor', '_constructor_expanddim', '_constructor_expanddim_from_mgr', '_constructor_from_mgr', '_data', '_deprecate_downcast', '_dir_additions', '_dir_deletions', '_drop_axis', '_drop_labels_or_levels', '_duplicated', '_expanddim_from_mgr', '_find_valid_index', '_flags', '_flex_method', '_from_mgr', '_get_axis', '_get_axis_name', '_get_axis_number', '_get_axis_resolvers', '_get_block_manager_axis', '_get_bool_data', '_get_cacher', '_get_cleaned_column_resolvers', '_get_index_resolvers', '_get_label_or_level_values', '_get_numeric_data', '_get_rows_with_mask', '_get_value', '_get_values_tuple', '_get_with', '_getitem_slice', '_gotitem', '_hidden_attrs', '_indexed_same', '_info_axis', '_info_axis_name', '_info_axis_number', '_init_dict', '_init_mgr', '_inplace_method', '_internal_names', '_internal_names_set', '_is_cached', '_is_copy', '_is_label_or_level_reference', '_is_label_reference', '_is_level_reference', '_is_mixed_type', '_is_view', '_is_view_after_cow_rules', '_item_cache', '_ixs', '_logical_func', '_logical_method', '_map_values', '_maybe_update_cacher', '_memory_usage', '_metadata', '_mgr', '_min_count_stat_function', '_name', '_needs_reindex_multi', '_pad_or_backfill', '_protect_consolidate', '_reduce', '_references', '_reindex_axes', '_reindex_indexer', '_reindex_multi', '_reindex_with_indexers', '_rename', '_replace_single', '_repr_data_resource_', '_repr_latex_', '_reset_cache', '_reset_cacher', '_set_as_cached', '_set_axis', '_set_axis_name', '_set_axis_nocheck', '_set_is_copy', '_set_labels', '_set_name', '_set_value', '_set_values', '_set_with', '_set_with_engine', '_shift_with_freq', '_slice', '_stat_function', '_stat_function_ddof', '_take_with_is_copy', '_to_latex_via_styler', '_typ', '_update_inplace', '_validate_dtype', '_values', '_where', 'abs', 'add', 'add_prefix', 'add_suffix', 'agg', 'aggregate', 'align', 'all', 'any', 'apply', 'argmax', 'argmin', 'argsort', 'array', 'asfreq', 'asof', 'astype', 'at', 'at_time', 'attrs', 'autocorr', 'axes', 'backfill', 'between', 'between_time', 'bfill', 'bool', 'case_when', 'clip', 'combine', 'combine_first', 'compare', 'convert_dtypes', 'copy', 'corr', 'count', 'cov', 'cummax', 'cummin', 'cumprod', 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'reorder_levels', 'repeat', 'replace', 'resample', 'reset_index', 'rfloordiv', 'rmod', 'rmul', 'rolling', 'round', 'rpow', 'rsub', 'rtruediv', 'sample', 'searchsorted', 'sem', 'set_axis', 'set_flags', 'shape', 'shift', 'size', 'skew', 'sort_index', 'sort_values', 'squeeze', 'std', 'struct', 'sub', 'subtract', 'sum', 'swapaxes', 'swaplevel', 'tail', 'take', 'to_clipboard', 'to_csv', 'to_dict', 'to_excel', 'to_frame', 'to_hdf', 'to_json', 'to_latex', 'to_list', 'to_markdown', 'to_numpy', 'to_period', 'to_pickle', 'to_sql', 'to_string', 'to_timestamp', 'to_xarray', 'transform', 'transpose', 'truediv', 'truncate', 'tz_convert', 'tz_localize', 'unique', 'unstack', 'update', 'value_counts', 'values', 'var', 'vbt', 'view', 'where', 'xs']\n",
- "time\n",
- "2024-03-12 09:30:00-04:00 70.6588\n",
- "2024-03-12 09:30:23-04:00 74.0781\n",
- "2024-03-12 09:30:47-04:00 76.6184\n",
- "2024-03-12 09:31:11-04:00 81.0538\n",
- "2024-03-12 09:31:34-04:00 86.0863\n",
- " ... \n",
- "2024-03-13 15:58:19-04:00 59.6001\n",
- "2024-03-13 15:58:42-04:00 58.2072\n",
- "2024-03-13 15:59:05-04:00 49.7763\n",
- "2024-03-13 15:59:28-04:00 37.3871\n",
- "2024-03-13 15:59:51-04:00 37.3871\n",
- "Name: Rsi14, Length: 1961, dtype: float64\n"
- ]
- },
+ "data": {
+ "text/plain": [
+ "time\n",
+ "2024-03-12 09:30:00-04:00 70.6588\n",
+ "2024-03-12 09:30:23-04:00 74.0781\n",
+ "2024-03-12 09:30:47-04:00 76.6184\n",
+ "2024-03-12 09:31:11-04:00 81.0538\n",
+ "2024-03-12 09:31:34-04:00 86.0863\n",
+ " ... \n",
+ "2024-03-13 15:58:19-04:00 59.6001\n",
+ "2024-03-13 15:58:42-04:00 58.2072\n",
+ "2024-03-13 15:59:05-04:00 49.7763\n",
+ "2024-03-13 15:59:28-04:00 37.3871\n",
+ "2024-03-13 15:59:51-04:00 37.3871\n",
+ "Name: Rsi14, Length: 1961, dtype: float64"
+ ]
+ },
+ "execution_count": 49,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# basic_data.data[\"BAC\"]\n",
+ "rsi14"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1c98336032174fb0858c20b1b83d4f1b",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "FigureWidget({\n",
+ " 'data': [{'name': 'Rsi14',\n",
+ " 'showlegend': True,\n",
+ " 'type': 'scatter',\n",
+ " 'uid': '5eb5e6f2-c71f-441c-9c2b-88a379762a9d',\n",
+ " 'x': array([datetime.datetime(2024, 3, 12, 9, 30, tzinfo=),\n",
+ " datetime.datetime(2024, 3, 12, 9, 30, 23, tzinfo=),\n",
+ " datetime.datetime(2024, 3, 12, 9, 30, 47, tzinfo=),\n",
+ " ...,\n",
+ " datetime.datetime(2024, 3, 13, 15, 59, 5, tzinfo=),\n",
+ " datetime.datetime(2024, 3, 13, 15, 59, 28, tzinfo=),\n",
+ " datetime.datetime(2024, 3, 13, 15, 59, 51, tzinfo=)],\n",
+ " dtype=object),\n",
+ " 'xaxis': 'x',\n",
+ " 'y': array([70.6588, 74.0781, 76.6184, ..., 49.7763, 37.3871, 37.3871]),\n",
+ " 'yaxis': 'y2'},\n",
+ " {'close': array([35.93 , 35.97 , 35.975, ..., 36.09 , 36.08 , 36.07 ]),\n",
+ " 'decreasing': {'fillcolor': '#ee534f', 'line': {'color': '#ee534f'}},\n",
+ " 'high': array([35.96 , 35.99 , 35.985, ..., 36.14 , 36.11 , 36.1 ]),\n",
+ " 'increasing': {'fillcolor': '#26a69a', 'line': {'color': '#26a69a'}},\n",
+ " 'low': array([35.85 , 35.92 , 35.935, ..., 36.09 , 36.05 , 36.065]),\n",
+ " 'name': 'OHLC',\n",
+ " 'opacity': 0.75,\n",
+ " 'open': array([35.9 , 35.925, 35.97 , ..., 36.13 , 36.095, 36.08 ]),\n",
+ " 'type': 'candlestick',\n",
+ " 'uid': 'e526fc44-8b7e-4948-b4d0-39ae4e7a2b91',\n",
+ " 'x': array([datetime.datetime(2024, 3, 12, 9, 30, tzinfo=),\n",
+ " datetime.datetime(2024, 3, 12, 9, 30, 23, tzinfo=),\n",
+ " datetime.datetime(2024, 3, 12, 9, 30, 47, tzinfo=),\n",
+ " ...,\n",
+ " datetime.datetime(2024, 3, 13, 15, 59, 5, tzinfo=),\n",
+ " datetime.datetime(2024, 3, 13, 15, 59, 28, tzinfo=),\n",
+ " datetime.datetime(2024, 3, 13, 15, 59, 51, tzinfo=)],\n",
+ " dtype=object),\n",
+ " 'xaxis': 'x',\n",
+ " 'yaxis': 'y'},\n",
+ " {'marker': {'color': array(['#26a69a', '#26a69a', '#26a69a', ..., '#ee534f', '#ee534f', '#ee534f'],\n",
+ " dtype=object),\n",
+ " 'line': {'width': 0}},\n",
+ " 'name': 'Volume',\n",
+ " 'opacity': 0.5,\n",
+ " 'type': 'bar',\n",
+ " 'uid': '213ef8f6-a7f7-46a3-a3b8-f203762289eb',\n",
+ " 'x': array([datetime.datetime(2024, 3, 12, 9, 30, tzinfo=),\n",
+ " datetime.datetime(2024, 3, 12, 9, 30, 23, tzinfo=),\n",
+ " datetime.datetime(2024, 3, 12, 9, 30, 47, tzinfo=),\n",
+ " ...,\n",
+ " datetime.datetime(2024, 3, 13, 15, 59, 5, tzinfo=),\n",
+ " datetime.datetime(2024, 3, 13, 15, 59, 28, tzinfo=),\n",
+ " datetime.datetime(2024, 3, 13, 15, 59, 51, tzinfo=)],\n",
+ " dtype=object),\n",
+ " 'xaxis': 'x2',\n",
+ " 'y': array([416236, 25178, 14986, ..., 545827, 734354, 321567]),\n",
+ " 'yaxis': 'y3'}],\n",
+ " 'layout': {'height': 350,\n",
+ " 'legend': {'orientation': 'h',\n",
+ " 'traceorder': 'normal',\n",
+ " 'x': 1,\n",
+ " 'xanchor': 'right',\n",
+ " 'y': 1.02,\n",
+ " 'yanchor': 'bottom'},\n",
+ " 'margin': {'b': 30, 'l': 30, 'r': 30, 't': 30},\n",
+ " 'template': '...',\n",
+ " 'width': 1280,\n",
+ " 'xaxis': {'anchor': 'y',\n",
+ " 'domain': [0.0, 0.94],\n",
+ " 'matches': 'x2',\n",
+ " 'rangeslider': {'visible': False},\n",
+ " 'showticklabels': False},\n",
+ " 'xaxis2': {'anchor': 'y3', 'domain': [0.0, 0.94]},\n",
+ " 'yaxis': {'anchor': 'x', 'domain': [0.575, 1.0]},\n",
+ " 'yaxis2': {'anchor': 'x', 'overlaying': 'y', 'side': 'right'},\n",
+ " 'yaxis3': {'anchor': 'x2', 'domain': [0.0, 0.425]}}\n",
+ "})"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#b = filtered_data.get().iloc[100:200] #b[[\"Open\",\"High\"]]\n",
+ "rsi14 = basic_data.data[\"BAC\"][\"Rsi14\"].rename(\"Rsi14\")\n",
+ "#create subploit\n",
+ "fig = vbt.make_subplots(rows=2, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}], [{\"secondary_y\": False}]])\n",
+ "rsi14.vbt.plot(add_trace_kwargs=dict(row=1, col=1, secondary_y=True),fig=fig)\n",
+ "basic_data.data[\"BAC\"].vbt.ohlcv.plot(add_trace_kwargs=dict(row=1, col=1, secondary_y=False), fig=fig)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
{
"data": {
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@@ -35813,7 +452,7 @@
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+ "uid": "8644a6fb-707b-40dc-a4d2-3e15535d00e2",
"x": [
"2024-03-12T09:30:00-04:00",
"2024-03-12T09:30:23-04:00",
@@ -39740,5469 +4379,13 @@
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- "bounds": [
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- ]
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- ]
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- ]
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- {
- "bounds": [
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- "2024-03-13T15:55:38-04:00"
- ]
- }
- ]
- }
- }
- }
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.plotly.v1+json": {
- "config": {
- "plotlyServerURL": "https://plot.ly"
- },
- "data": [
{
"mode": "lines",
"name": "real",
"showlegend": true,
"type": "scatter",
- "uid": "0c2b8ac8-2956-4bc8-8308-27f67bcdf881",
+ "uid": "b8228feb-b634-4391-b74b-0decd43c5800",
"x": [
"2024-03-12T09:30:00-04:00",
"2024-03-12T09:30:23-04:00",
@@ -49962,6 +9145,11 @@
},
"width": 1280,
"xaxis": {
+ "anchor": "y",
+ "domain": [
+ 0,
+ 1
+ ],
"rangebreaks": [
{
"bounds": [
@@ -54572,6 +13760,13 @@
]
}
]
+ },
+ "yaxis": {
+ "anchor": "x",
+ "domain": [
+ 0,
+ 1
+ ]
}
}
}
@@ -54584,22 +13779,27 @@
"run_rsi = vbt.talib_func(\"rsi\")\n",
"rsi_new = run_rsi(basic_data.vwap, timeperiod=15)\n",
"rsi_new = rsi_new.fillna(0)\n",
- "print(rsi_new)\n",
- "print(dir(rsi_new))\n",
+ "# print(rsi_new)\n",
+ "# print(dir(rsi_new))\n",
"rsi14 = basic_data.data[\"BAC\"][\"Rsi14\"]\n",
- "print(rsi14)\n",
+ "# print(rsi14)\n",
"\n",
- "combined_df = pd.concat([rsi_new, rsi14], axis=1)\n",
- "combined_df\n",
+ "#zkombinujeme do stejneho dataframe skrz sloupce (axis1)\n",
+ "# combined_df = pd.concat([rsi_new, rsi14], axis=1)\n",
+ "# combined_df\n",
+ "\n",
+ "#create subplot\n",
+ "fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True)\n",
"\n",
"plot_rsi = vbt.talib_plot_func(\"rsi\")\n",
- "plot_rsi(rsi_new).show()\n",
- "plot_rsi(rsi14).show()\n"
+ "plot_rsi(rsi_new, fig=fig)\n",
+ "plot_rsi(rsi14, fig=fig)\n",
+ "fig.show()"
]
},
{
"cell_type": "code",
- "execution_count": 211,
+ "execution_count": 52,
"metadata": {},
"outputs": [
{
@@ -54620,7 +13820,7 @@
"Name: Vwap, Length: 1961, dtype: bool"
]
},
- "execution_count": 211,
+ "execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
@@ -54632,7 +13832,7 @@
},
{
"cell_type": "code",
- "execution_count": 212,
+ "execution_count": 53,
"metadata": {},
"outputs": [
{
@@ -54653,7 +13853,7 @@
"Name: Vwap, Length: 1961, dtype: bool"
]
},
- "execution_count": 212,
+ "execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
@@ -54665,7 +13865,7 @@
},
{
"cell_type": "code",
- "execution_count": 213,
+ "execution_count": 54,
"metadata": {},
"outputs": [
{
@@ -62562,7 +21762,7 @@
36.08
],
"type": "candlestick",
- "uid": "d4ca0ab8-1e54-4e68-8d6b-e2c18d2b243f",
+ "uid": "a57bdc26-86f1-4d93-8a8e-8126412c48ae",
"x": [
"2024-03-12T09:30:00-04:00",
"2024-03-12T09:30:23-04:00",
@@ -66501,7 +25701,7 @@
"name": "Volume",
"opacity": 0.5,
"type": "bar",
- "uid": "1e37740d-9ee2-449d-a328-90a429c85b3a",
+ "uid": "18d000e3-3df4-49ea-8f67-ef2ce2a865dc",
"x": [
"2024-03-12T09:30:00-04:00",
"2024-03-12T09:30:23-04:00",
@@ -70439,7 +29639,7 @@
"name": "Vwap",
"showlegend": true,
"type": "scatter",
- "uid": "e102314a-487c-4d58-b8e5-db2a3720990a",
+ "uid": "f8bfc1af-8eb6-49e5-ab1a-3328eb81452b",
"x": [
"2024-03-12T09:30:00-04:00",
"2024-03-12T09:30:23-04:00",
@@ -74383,7 +33583,7 @@
"name": "Entries",
"showlegend": true,
"type": "scatter",
- "uid": "16de330c-817d-45e1-8fd2-b8b7addb8a44",
+ "uid": "da071d42-462c-47a6-ac48-3ca52a67a351",
"x": [
"2024-03-12T11:27:11-04:00",
"2024-03-12T11:46:26-04:00",
@@ -74475,7 +33675,7 @@
"name": "Exits",
"showlegend": true,
"type": "scatter",
- "uid": "ad7b9ceb-b1de-4c9d-ad44-514eb8abc73a",
+ "uid": "66fa8b32-c3c3-4159-a0c1-d8599e9c8540",
"x": [
"2024-03-12T09:56:09-04:00",
"2024-03-12T09:57:42-04:00",
@@ -92533,7 +51733,7 @@
36.08
],
"type": "candlestick",
- "uid": "95062a7f-bddf-4ba8-89df-690d1fb7bf78",
+ "uid": "aef022d5-a1bc-4091-8b16-f11b21c1ec20",
"x": [
"2024-03-12T09:30:00-04:00",
"2024-03-12T09:30:23-04:00",
@@ -96472,7 +55672,7 @@
"name": "Volume",
"opacity": 0.5,
"type": "bar",
- "uid": "f75fd578-357e-4279-8a5c-71b710573a80",
+ "uid": "bc64e35a-e77d-4b26-b45c-5042c072b4e4",
"x": [
"2024-03-12T09:30:00-04:00",
"2024-03-12T09:30:23-04:00",
@@ -100410,7 +59610,7 @@
"name": "Vwap",
"showlegend": true,
"type": "scatter",
- "uid": "ac249064-e86c-4fd6-b577-a8a0e4553584",
+ "uid": "06553329-871d-4681-891e-eb0f858fa943",
"x": [
"2024-03-12T09:30:00-04:00",
"2024-03-12T09:30:23-04:00",
@@ -104354,7 +63554,7 @@
"name": "Entries",
"showlegend": true,
"type": "scatter",
- "uid": "d89b4fe1-b7e8-4291-a5a2-35303bf3ee0a",
+ "uid": "3b5d0eaa-513a-4915-bda5-af895723036c",
"x": [
"2024-03-12T11:27:11-04:00",
"2024-03-12T11:46:26-04:00",
@@ -104402,7 +63602,7 @@
"name": "Exits",
"showlegend": true,
"type": "scatter",
- "uid": "21a89431-dd37-4e59-a581-aa704917a3c1",
+ "uid": "7dfe77e4-a827-4a89-a06a-7eea06903adf",
"x": [
"2024-03-12T11:36:40-04:00",
"2024-03-12T12:55:25-04:00",
@@ -114553,7 +73753,7 @@
},
{
"cell_type": "code",
- "execution_count": 214,
+ "execution_count": 55,
"metadata": {},
"outputs": [
{
@@ -114562,7 +73762,7 @@
"12"
]
},
- "execution_count": 214,
+ "execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
@@ -114574,7 +73774,7 @@
},
{
"cell_type": "code",
- "execution_count": 205,
+ "execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
@@ -114600,7 +73800,7 @@
},
{
"cell_type": "code",
- "execution_count": 206,
+ "execution_count": 57,
"metadata": {},
"outputs": [
{
@@ -122509,7 +81709,7 @@
36.08
],
"type": "candlestick",
- "uid": "ce098689-2595-462d-9a38-64692add2ab7",
+ "uid": "e714e4b7-c4e8-412a-8526-ccc56a323b98",
"x": [
"2024-03-12T09:30:00-04:00",
"2024-03-12T09:30:23-04:00",
@@ -126448,7 +85648,7 @@
"name": "Volume",
"opacity": 0.5,
"type": "bar",
- "uid": "4893a1c1-8ebd-4e8c-aa9c-88f88f15ba1d",
+ "uid": "0d725cb6-86e6-4078-953e-e484e2523ef9",
"x": [
"2024-03-12T09:30:00-04:00",
"2024-03-12T09:30:23-04:00",
@@ -130386,7 +89586,7 @@
"name": "Vwap",
"showlegend": true,
"type": "scatter",
- "uid": "a37182b1-73b7-4257-8ea7-e04c549017f1",
+ "uid": "d4b2ab2f-4afb-4ab6-b281-c003e028c15f",
"x": [
"2024-03-12T09:30:00-04:00",
"2024-03-12T09:30:23-04:00",
@@ -134330,15 +93530,41 @@
"name": "Entries",
"showlegend": true,
"type": "scatter",
- "uid": "f2dbdc05-04ef-4a78-80bf-80bc9a8dd969",
+ "uid": "39a40550-f0be-47e2-bb82-7f2c5869e804",
"x": [
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- "2024-03-13T09:41:18-04:00",
+ "2024-03-12T10:36:30-04:00",
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@@ -134346,17 +93572,57 @@
"2024-03-13T09:51:23-04:00",
"2024-03-13T09:54:51-04:00",
"2024-03-13T09:59:54-04:00",
- "2024-03-13T10:02:13-04:00"
+ "2024-03-13T10:02:13-04:00",
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+ "2024-03-13T13:08:16-04:00",
+ "2024-03-13T13:14:15-04:00",
+ "2024-03-13T13:23:04-04:00",
+ "2024-03-13T13:47:57-04:00",
+ "2024-03-13T15:21:46-04:00",
+ "2024-03-13T15:38:54-04:00"
],
"xaxis": "x",
"y": [
+ 36.07,
+ 36.04,
35.875,
35.946,
35.975,
35.99,
35.97,
36.0192,
- 36.24,
+ 36.0399,
+ 36.05,
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+ 36.095,
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+ 36,
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+ 36.055,
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+ 36.21,
36.26,
36.33,
36.295,
@@ -134364,7 +93630,21 @@
36.336,
36.39,
36.43,
- 36.41
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+ 36.155
],
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},
@@ -134382,14 +93662,40 @@
"name": "Exits",
"showlegend": true,
"type": "scatter",
- "uid": "c6fec1ed-d4de-4749-ab93-8479547061b3",
+ "uid": "e666a6e7-8e29-4921-90f2-8f13f60c7b81",
"x": [
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+ "2024-03-12T09:34:45-04:00",
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+ "2024-03-12T10:40:44-04:00",
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+ "2024-03-12T15:15:03-04:00",
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+ "2024-03-13T09:32:45-04:00",
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+ "2024-03-13T09:40:08-04:00",
"2024-03-13T09:42:54-04:00",
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@@ -134398,16 +93704,56 @@
"2024-03-13T09:54:28-04:00",
"2024-03-13T09:59:30-04:00",
"2024-03-13T10:01:04-04:00",
- "2024-03-13T10:02:59-04:00"
+ "2024-03-13T10:02:59-04:00",
+ "2024-03-13T11:04:21-04:00",
+ "2024-03-13T11:07:31-04:00",
+ "2024-03-13T11:26:30-04:00",
+ "2024-03-13T11:39:00-04:00",
+ "2024-03-13T11:40:09-04:00",
+ "2024-03-13T11:43:59-04:00",
+ "2024-03-13T11:47:26-04:00",
+ "2024-03-13T12:49:55-04:00",
+ "2024-03-13T13:13:51-04:00",
+ "2024-03-13T13:17:44-04:00",
+ "2024-03-13T13:30:06-04:00",
+ "2024-03-13T14:42:44-04:00",
+ "2024-03-13T15:27:36-04:00",
+ "2024-03-13T15:44:46-04:00"
],
"xaxis": "x",
"y": [
+ 36.015,
+ 35.99,
35.926,
35.982,
36,
35.95,
36.01,
35.98,
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36.2814,
36.305,
36.2807,
@@ -134416,7 +93762,21 @@
36.375,
36.426,
36.39,
- 36.365
+ 36.365,
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],
"yaxis": "y2"
}
@@ -144521,13 +103881,13 @@
"\n",
"@vbt.njit\n",
"def elapsed_minutes_from_open_nb(time_in_ns):\n",
- " market_open_minutes = 570 # 9 hours * 60 minutes + 30 minutes\n",
- " current_minutes = vbt.dt_nb.hour_nb(time_in_ns) * 60 + vbt.dt_nb.minute_nb(time_in_ns)\n",
+ " market_opens_in_minute = 570 # 9 hours * 60 minutes + 30 minutes\n",
+ " current_minute = vbt.dt_nb.hour_nb(time_in_ns) * 60 + vbt.dt_nb.minute_nb(time_in_ns)\n",
" #print(\"current_minutes\", current_minutes)\n",
" # Calculate elapsed minutes since market open at 9:30 AM\n",
- " elapsed_minutes = current_minutes - market_open_minutes\n",
- " print( \"elapsed_from_open\", elapsed_minutes)\n",
- " return elapsed_minutes if elapsed_minutes >= 0 else 0\n",
+ " minutes_from_open = current_minute - market_opens_in_minute\n",
+ " print( \"elapsed_from_open\", minutes_from_open)\n",
+ " return minutes_from_open if minutes_from_open >= 0 else 0\n",
"\n",
"@vbt.njit\n",
"def entry_place_func_nb(c, low, close, time_in_ns, rsi14, window_open, window_close):\n",
@@ -144618,8 +103978,6 @@
"print(rsi_entries_array)\n",
"\n",
"\n",
- "\n",
- "\n",
"entries, exits = vbt.pd_acc.signals.generate_both( # (6)!\n",
" symbol_wrapper.shape,\n",
" entry_place_func_nb=entry_place_func_nb,\n",
diff --git a/research/indcross_parametrized.ipynb b/research/indcross_parametrized.ipynb
new file mode 100644
index 0000000..d0e1c37
--- /dev/null
+++ b/research/indcross_parametrized.ipynb
@@ -0,0 +1,1526 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "celkovy optimalizacni backtest na vetsim oknu 1 - 300\n",
+ "a možná take to udělat jako parametr\n",
+ "zkusit CV\n",
+ "zobrazit nejak robustnost parametru"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Activating profile profile1\n",
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+ "Activating profile profile1\n"
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+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Realtime Websocket connection will use FEED: sip and credential of ACCOUNT1\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n"
+ ],
+ "text/plain": [
+ ""
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+ "text/plain": [
+ "Start 2024-01-02 09:30:00-05:00\n",
+ "End 2024-02-28 15:59:59-05:00\n",
+ "Period 5 days 18:15:46\n",
+ "Start Value 100.0\n",
+ "Min Value 99.766637\n",
+ " ... \n",
+ "Expectancy -0.058341\n",
+ "Sharpe Ratio -3.597608\n",
+ "Calmar Ratio -2.69309\n",
+ "Omega Ratio 0.867176\n",
+ "Sortino Ratio -4.934545\n",
+ "Length: 29, dtype: object"
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+ "execution_count": 18,
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+ "source": [
+ "##na toto udelat crosssvalidationu nebo alespon na testovacim ci jinem obdobi\n",
+ "#take udelat long leg - tato je shortovaci\n",
+ "\n",
+ "#8\t-0.06\t-0.2\t0.0028\t0.0048\t4.156254\n",
+ "\n",
+ "#short combination ok for train(4)/test(0.1) (window 1-90, fe 95-100)\n",
+ "#2,\t-0.02,\t-0.25,\t0.0018,\t0.0068\n",
+ "\n",
+ "#dalsi ok hodnota shortu for train/test 4/1\n",
+ "#70,\t8,\t-0.06,\t-0.2,\t0.0013,\t0.0053\t\n",
+ "\n",
+ "\n",
+ "#kombinace bez roc_th, train/test 7/-1.5\n",
+ "#70\t7\t-0.07\t0.0033\t0.0063\n",
+ "\n",
+ "#opet bez roc_th, train(5.77)/test 0.9 - spolus tsl_stop + tsl_th\n",
+ "#29\t7\t-0.09\t0.0033\t0.0068\n",
+ "\n",
+ "#bez roc_th a s trailing sl train/test 8.1/-0.8 \n",
+ "#70\t2\t-0.05\t0.0018\t0.0068\n",
+ "\n",
+ "\n",
+ "# TODO:\n",
+ "#- vyzkouset zda nejvyhodnejsi kombinace krom train/testu funguje i na nasledujicich dnech po trainu\n",
+ "# -zkusit najit v short datasetu neco vyhodneho co funguji i na testu\n",
+ "# - dodelat kombinace pro long signaly\n",
+ "# - zkusit walk forward\n",
+ "# - vytvorit vysledkove totoznou na v2realbot\n",
+ "# - podivat se jak detailne funguji tsl_stop a tsl_th\n",
+ "\n",
+ "#70,\t4,\t-0.07,\t0.0048,\t0.0068\t\n",
+ "\n",
+ "\n",
+ "entry_window_closes, mom_timeperiod, mom_th, sl_stop, tp_stop = 8,\t3,\t0.07,\t0.0028,\t0.0033\t\n",
+ "roc_th = 0\n",
+ "momshort = vbt.indicator(\"talib:MOM\").run(basic_data.close, timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
+ "rocp = vbt.indicator(\"talib:ROC\").run(basic_data.close, short_name = \"rocp\")\n",
+ "#rate of change + momentum\n",
+ "\n",
+ "#momshort.plot rocp.real_crossed_below(roc_th) & \n",
+ "short_signal = momshort.real_crossed_below(mom_th)\n",
+ "\n",
+ "long_signal = momshort.real_crossed_above(mom_th)\n",
+ "\n",
+ "# print(\"short signal\")\n",
+ "# print(short_signal.value_counts())\n",
+ "\n",
+ "#forced_exit = pd.Series(False, index=close.index)\n",
+ "forced_exit = basic_data.symbol_wrapper.fill(False)\n",
+ "#entry_window_open = pd.Series(False, index=close.index)\n",
+ "entry_window_open= basic_data.symbol_wrapper.fill(False)\n",
+ "\n",
+ "# Calculate the time difference in minutes from market open for each timestamp\n",
+ "elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
+ "\n",
+ "entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
+ "\n",
+ "#print(entry_window_open.value_counts())\n",
+ "\n",
+ "forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
+ "short_entries = (short_signal & entry_window_open)\n",
+ "short_exits = forced_exit\n",
+ "\n",
+ "entries = (long_signal & entry_window_open)\n",
+ "exits = forced_exit\n",
+ "#long_entries.info()\n",
+ "#number of trues and falses in long_entries\n",
+ "# print(short_exits.value_counts())\n",
+ "# print(short_entries.value_counts())\n",
+ "\n",
+ "#fig = plot_2y_close([],[momshort, rocp], close)\n",
+ "#short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
+ "#print(sl_stop)\n",
+ "#short_entries=short_entries, short_exits=short_exits,\n",
+ "pf = vbt.Portfolio.from_signals(close=basic_data, entries=entries, exits=exits, tsl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
+ "\n",
+ "pf.stats()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
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diff --git a/research/loading_trades_aggregation.ipynb b/research/loading_trades_aggregation.ipynb
new file mode 100644
index 0000000..fe8d653
--- /dev/null
+++ b/research/loading_trades_aggregation.ipynb
@@ -0,0 +1,316 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Loading trades and vectorized aggregation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from numba import jit\n",
+ "from alpaca.data.historical import StockHistoricalDataClient\n",
+ "from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
+ "from alpaca.data.requests import StockTradesRequest\n",
+ "from v2realbot.enums.enums import BarType\n",
+ "import time\n",
+ "\n",
+ "from datetime import datetime\n",
+ "from v2realbot.utils.utils import parse_alpaca_timestamp, ltp, zoneNY, send_to_telegram, fetch_calendar_data\n",
+ "import pyarrow\n",
+ "from v2realbot.loader.aggregator_vectorized import fetch_daily_stock_trades, fetch_trades_parallel, generate_time_bars_nb, aggregate_trades\n",
+ "import vectorbtpro as vbt\n",
+ "\n",
+ "vbt.settings.set_theme(\"dark\")\n",
+ "vbt.settings['plotting']['layout']['width'] = 1280\n",
+ "vbt.settings.plotting.auto_rangebreaks = True\n",
+ "# Set the option to display with pagination\n",
+ "pd.set_option('display.notebook_repr_html', True)\n",
+ "pd.set_option('display.max_rows', 10) # Number of rows per page"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "symbol = \"SPY\"\n",
+ "#datetime in zoneNY \n",
+ "day_start = datetime(2024, 5, 15, 9, 30, 0)\n",
+ "day_stop = datetime(2024, 5, 16, 16, 00, 0)\n",
+ "day_start = zoneNY.localize(day_start)\n",
+ "day_stop = zoneNY.localize(day_stop)\n",
+ "#neslo by zrychlit, kdyz se zobrazuje pomalu Searching cache - nejaky bottle neck?\n",
+ "df = fetch_trades_parallel(symbol, day_start, day_stop, minsize=50) #exclude_conditions=['C','O','4','B','7','V','P','W','U','Z','F'])\n",
+ "ohlcv_df = aggregate_trades(symbol=symbol, trades_df=df, resolution=1, type=BarType.TIME)\n",
+ "#df.info()\n",
+ "ohlcv_df\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "basic_data = vbt.Data.from_data(vbt.symbol_dict({symbol: ohlcv_df}), tz_convert=zoneNY)\n",
+ "vbt.settings['plotting']['auto_rangebreaks'] = True\n",
+ "basic_data.ohlcv.plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pickle\n",
+ "from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
+ "import gzip\n",
+ "\n",
+ "file_path = f\"{DATA_DIR}/tradecache/BAC-1709044200-1709067600.cache.gz\"\n",
+ "\n",
+ "with gzip.open(file_path, 'rb') as fp:\n",
+ " tradesResponse = pickle.load(fp)\n",
+ "\n",
+ "tradesResponse"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def convert_dict_to_multiindex_df(tradesResponse):\n",
+ " # Create a DataFrame for each key and add the key as part of the MultiIndex\n",
+ " dfs = []\n",
+ " for key, values in tradesResponse.items():\n",
+ " df = pd.DataFrame(values)\n",
+ " # Rename columns\n",
+ " # Select and order columns explicitly\n",
+ " #print(df)\n",
+ " df = df[['t', 'x', 'p', 's', 'i', 'c','z']]\n",
+ " df.rename(columns={'t': 'timestamp', 'c': 'conditions', 'p': 'price', 's': 'size', 'x': 'exchange', 'z':'tape', 'i':'id'}, inplace=True)\n",
+ " df['symbol'] = key # Add ticker as a column\n",
+ " df['timestamp'] = pd.to_datetime(df['timestamp']) # Convert 't' from string to datetime before setting it as an index\n",
+ " df.set_index(['symbol', 'timestamp'], inplace=True) # Set the multi-level index using both 'ticker' and 't'\n",
+ " df = df.tz_convert(zoneNY, level='timestamp')\n",
+ " dfs.append(df)\n",
+ "\n",
+ " # Concatenate all DataFrames into a single DataFrame with MultiIndex\n",
+ " final_df = pd.concat(dfs)\n",
+ "\n",
+ " return final_df\n",
+ "\n",
+ "# Convert and print the DataFrame\n",
+ "df = convert_dict_to_multiindex_df(tradesResponse)\n",
+ "df\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ohlcv_df.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ohlcv_df.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ohlcv_df = aggregate_trades(symbol=symbol, trades_df=df, resolution=1000, type=\"dollar\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ohlcv_df.index.strftime('%Y-%m-%d %H').unique()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#ohlcv_df.groupby(ohlcv_df.index.date).size()\n",
+ "ohlcv_df.head(100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#access just BCA\n",
+ "df_filtered = df.loc[\"BAC\"]\n",
+ "\n",
+ "df_filtered.info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_filtered= df_filtered.reset_index()\n",
+ "ticks = df_filtered[['timestamp', 'price', 'size']].to_numpy()\n",
+ "ticks\n",
+ "timestamps = ticks[:, 0]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_filtered= df_filtered.reset_index()\n",
+ "ticks = df_filtered[['timestamp', 'price', 'size']].to_numpy()\n",
+ "\n",
+ "#timestamp to integer\n",
+ "# Extract the timestamps column (assuming it's the first column)\n",
+ "timestamps = ticks[:, 0]\n",
+ "\n",
+ "# Convert the timestamps to Unix timestamps in seconds with microsecond precision\n",
+ "unix_timestamps_s = np.array([ts.timestamp() for ts in timestamps], dtype='float64')\n",
+ "\n",
+ "# Replace the original timestamps in the NumPy array with the converted Unix timestamps\n",
+ "ticks[:, 0] = unix_timestamps_s\n",
+ "\n",
+ "#ticks[:, 0] = pd.to_datetime(ticks[:, 0]).astype('int64') // 1_000_000_000 # Convert to Unix timestamp\n",
+ "ticks\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ticks = ticks.astype(np.float64)\n",
+ "ticks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "resolution = 1 # Example resolution of 60 seconds\n",
+ "ohlcv_bars = generate_time_bars_nb(ticks, resolution)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ohlcv_bars"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Convert the resulting array back to a DataFrame\n",
+ "columns = ['time', 'open', 'high', 'low', 'close', 'volume', 'trades']\n",
+ "ohlcv_df = pd.DataFrame(ohlcv_bars, columns=columns)\n",
+ "ohlcv_df['time'] = pd.to_datetime(ohlcv_df['time'], unit='s')\n",
+ "ohlcv_df.set_index('time', inplace=True)\n",
+ "ohlcv_df.index = ohlcv_df.index.tz_localize('UTC').tz_convert(zoneNY)\n",
+ "#ohlcv_df = ohlcv_df.loc[\"2024-03-1 15:50:00\":\"2024-03-28 13:40:00\"]\n",
+ "#ohlcv_df.index.strftime('%Y-%m-%d %H').unique()\n",
+ "\n",
+ "ohlcv_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "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": 2
+}
diff --git a/research/rsi_alpaca.ipynb b/research/rsi_alpaca.ipynb
new file mode 100644
index 0000000..94d4da3
--- /dev/null
+++ b/research/rsi_alpaca.ipynb
@@ -0,0 +1,26673 @@
+{
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+ "execution_count": 79,
+ "metadata": {},
+ "outputs": [
+ {
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+ "
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+ " | \n",
+ " Open | \n",
+ " High | \n",
+ " Low | \n",
+ " Close | \n",
+ " Volume | \n",
+ " Trade count | \n",
+ " VWAP | \n",
+ "
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+ " \n",
+ " | Open time | \n",
+ " | \n",
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+ " | \n",
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+ " Open High Low Close Volume \\\n",
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+ },
+ "execution_count": 81,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#filter na dny\n",
+ "# dates_of_interest = pd.to_datetime(['2024-04-22', '2024-04-23']).tz_localize('US/Eastern')\n",
+ "# filtered_df = df.loc[df.index.normalize().isin(dates_of_interest)]\n",
+ "\n",
+ "# df = filtered_df\n",
+ "# df.info()\n",
+ "\n",
+ "basic_data.data[\"BAC\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {},
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+ " Open High Low Close Volume\n",
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+ "\n",
+ "[191 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 82,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
+ "\n",
+ "m1_data.data[\"BAC\"]\n",
+ "#m5_data = m1_data.resample(\"5T\")\n",
+ "\n",
+ "#m5_data.data[\"BAC\"].head(10)\n",
+ "\n",
+ "m15_data = m1_data.resample(\"15T\")\n",
+ "\n",
+ "m15_data = m15_data.transform(lambda x: x.between_time(\"9:30\",\"15:59\"))\n",
+ "\n",
+ "m15 = m15_data.data[\"BAC\"]\n",
+ "\n",
+ "m15.vbt.ohlcv.plot()\n",
+ "\n",
+ "m15\n",
+ "\n",
+ "# m1_data.wrapper.index\n",
+ "\n",
+ "# m1_resampler = m1_data.wrapper.get_resampler(\"1T\")\n",
+ "# m1_resampler.index_difference(reverse=True)\n",
+ "\n",
+ "\n",
+ "# m5_resampler.prettify()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Calculate VWAP"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "VWAP.run(\n",
+ " high,\n",
+ " low,\n",
+ " close,\n",
+ " volume,\n",
+ " anchor=Default(value='D'),\n",
+ " short_name='vwap',\n",
+ " hide_params=None,\n",
+ " hide_default=True,\n",
+ " **kwargs\n",
+ "):\n",
+ " Run `VWAP` indicator.\n",
+ " \n",
+ " * Inputs: `high`, `low`, `close`, `volume`\n",
+ " * Parameters: `anchor`\n",
+ " * Outputs: `vwap`\n",
+ " \n",
+ " Pass a list of parameter names as `hide_params` to hide their column levels, or True to hide all.\n",
+ " Set `hide_default` to False to show the column levels of the parameters with a default value.\n",
+ " \n",
+ " Other keyword arguments are passed to `VWAP.run_pipeline`.\n"
+ ]
+ },
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+ " | vwap_anchor | \n",
+ " array | \n",
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+ " | symbol | \n",
+ " BAC | \n",
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\n",
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+ "source": [
+ "#WWAP\n",
+ "vbt.phelp(vbt.VWAP.run)\n",
+ "close = m1_data.close\n",
+ "high = m1_data.high\n",
+ "low = m1_data.low\n",
+ "volume = m1_data.volume\n",
+ "vwapD = vbt.VWAP.run(high, low, close, volume, anchor=\"D\")\n",
+ "# vwapT = vbt.VWAP.run(high, low, close, volume, anchor=\"T\")\n",
+ "\n",
+ "#vwap.vwap\n",
+ "\n",
+ "fig = m1_data.data[\"BAC\"].vbt.ohlcv.plot()\n",
+ "vwapD.vwap.vbt.plot(fig=fig)\n",
+ "#vwapT.vwap.vbt.plot(fig=fig)\n",
+ "fig.show()\n",
+ "\n",
+ "vwapD.vwap\n",
+ "\n",
+ "#vwap = vbt.VWAP.run()"
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+ " | Open time | \n",
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+ "source": [
+ "m1_data.data[\"BAC\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# defining ENTRY WINDOW and forced EXIT window"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#m1_data.data[\"BAC\"].info()\n",
+ "import datetime\n",
+ "# Define the market open and close times\n",
+ "market_open = datetime.time(9, 30)\n",
+ "market_close = datetime.time(16, 0)\n",
+ "entry_window_opens = 1\n",
+ "entry_window_closes = 350\n",
+ "\n",
+ "forced_exit_start = 360\n",
+ "forced_exit_end = 390\n",
+ "\n",
+ "forced_exit = m1_data.symbol_wrapper.fill(False)\n",
+ "entry_window_open= m1_data.symbol_wrapper.fill(False)\n",
+ "\n",
+ "# Calculate the time difference in minutes from market open for each timestamp\n",
+ "elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
+ "\n",
+ "entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
+ "forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
+ "\n",
+ "#entry_window_open.info()\n",
+ "# forced_exit.tail(100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ " \n",
+ " | \n",
+ " count | \n",
+ "
\n",
+ " \n",
+ " | (14, BAC) | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ " \n",
+ "Loading ITables v2.0.1 from the init_notebook_mode cell...\n",
+ "(need help?) | \n",
+ "\n",
+ "
\n",
+ "\n",
+ "
\n",
+ "\n"
+ ],
+ "text/plain": [
+ "(14, BAC)\n",
+ "False 127\n",
+ "True 14\n",
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+ "execution_count": 64,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "close = m1_data.close\n",
+ "\n",
+ "rsi = vbt.RSI.run(close, window=14)\n",
+ "\n",
+ "long_entries = (rsi.rsi.vbt.crossed_below(28) & entry_window_open)\n",
+ "long_exits = (rsi.rsi.vbt.crossed_above(70) | forced_exit)\n",
+ "#long_entries.info()\n",
+ "#number of trues and falses in long_entries\n",
+ "#long_entries.value_counts()\n",
+ "long_exits.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ " \n",
+ " | symbol | \n",
+ " BAC | \n",
+ "
\n",
+ " \n",
+ " | Open time | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ "\n",
+ "\n",
+ " \n",
+ "\n",
+ " \n",
+ "Loading ITables v2.0.1 from the init_notebook_mode cell...\n",
+ "(need help?) | \n",
+ "\n",
+ "
\n",
+ "\n",
+ "
\n",
+ "\n"
+ ],
+ "text/plain": [
+ "symbol BAC\n",
+ "Open time \n",
+ "2024-04-29 09:30:00-04:00 37.8800\n",
+ "2024-04-29 09:45:00-04:00 37.9850\n",
+ "2024-04-29 10:00:00-04:00 38.0145\n",
+ "2024-04-29 10:15:00-04:00 37.9650\n",
+ "2024-04-29 10:30:00-04:00 37.9600\n",
+ "... ...\n",
+ "2024-05-06 09:45:00-04:00 37.4900\n",
+ "2024-05-06 10:00:00-04:00 37.6350\n",
+ "2024-05-06 10:15:00-04:00 37.6250\n",
+ "2024-05-06 10:30:00-04:00 37.5993\n",
+ "2024-05-06 10:45:00-04:00 37.5600\n",
+ "\n",
+ "[141 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 65,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "close"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "4c11ec2e1b754bf597d0856020d41755",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "FigureWidget({\n",
+ " 'data': [{'name': 'BAC',\n",
+ " 'showlegend': True,\n",
+ " 'type': 'scatter',\n",
+ " 'uid': '8eef4694-305a-4f38-9916-7a6176babe2e',\n",
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