diff --git a/Untitled1.ipynb b/Untitled1.ipynb
index 038ee5c..ee4519b 100644
--- a/Untitled1.ipynb
+++ b/Untitled1.ipynb
@@ -14,28 +14,67 @@
},
{
"cell_type": "code",
- "execution_count": 108,
+ "execution_count": 19,
"id": "8d39819f-2cac-437f-b5fc-633ca7443f8a",
"metadata": {},
"outputs": [],
"source": [
- "rs = DataFrame({0: range(10, 101, 10), 1: range(10, 20), 2: range(20, 40, 2)})"
+ "rs = DataFrame({0: range(10, 101, 10), 1: range(10, 20), 2: range(20, 40, 2), \"symbols\": [chr(i) for i in [65]*5 + [68]*5]})"
]
},
{
"cell_type": "code",
- "execution_count": 109,
+ "execution_count": 32,
+ "id": "d7976bb8-05cb-4924-a6e2-90ea8af85d9d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[75, 75, 75, 75, 75, 75, 75, 75, 75, 75]"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "[75]*10"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
"id": "eb69c292-79e3-4105-b10c-6f4f5c22074f",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "KeyError",
+ "evalue": "None",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[71], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m ind \u001b[38;5;241m=\u001b[39m rs[rs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msymbols\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mA\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mname\n\u001b[1;32m----> 2\u001b[0m \u001b[43mrs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mind\u001b[49m\u001b[43m]\u001b[49m\n",
+ "File \u001b[1;32m~\\OneDrive - UBA\\Documents\\Personal\\VS\\aiomql\\venv\\Lib\\site-packages\\pandas\\core\\indexing.py:1191\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1189\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[0;32m 1190\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_deprecated_callable_usage(key, maybe_callable)\n\u001b[1;32m-> 1191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[1;32m~\\OneDrive - UBA\\Documents\\Personal\\VS\\aiomql\\venv\\Lib\\site-packages\\pandas\\core\\indexing.py:1431\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1429\u001b[0m \u001b[38;5;66;03m# fall thru to straight lookup\u001b[39;00m\n\u001b[0;32m 1430\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[1;32m-> 1431\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_label\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[1;32m~\\OneDrive - UBA\\Documents\\Personal\\VS\\aiomql\\venv\\Lib\\site-packages\\pandas\\core\\indexing.py:1381\u001b[0m, in \u001b[0;36m_LocIndexer._get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m 1379\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_label\u001b[39m(\u001b[38;5;28mself\u001b[39m, label, axis: AxisInt):\n\u001b[0;32m 1380\u001b[0m \u001b[38;5;66;03m# GH#5567 this will fail if the label is not present in the axis.\u001b[39;00m\n\u001b[1;32m-> 1381\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[1;32m~\\OneDrive - UBA\\Documents\\Personal\\VS\\aiomql\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:4301\u001b[0m, in \u001b[0;36mNDFrame.xs\u001b[1;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[0;32m 4299\u001b[0m new_index \u001b[38;5;241m=\u001b[39m index[loc]\n\u001b[0;32m 4300\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4301\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(loc, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[0;32m 4304\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m loc\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39mbool_:\n",
+ "File \u001b[1;32m~\\OneDrive - UBA\\Documents\\Personal\\VS\\aiomql\\venv\\Lib\\site-packages\\pandas\\core\\indexes\\range.py:417\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 415\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 416\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Hashable):\n\u001b[1;32m--> 417\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n\u001b[0;32m 418\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n\u001b[0;32m 419\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n",
+ "\u001b[1;31mKeyError\u001b[0m: None"
+ ]
+ }
+ ],
"source": [
- "rs.set_index(2, drop=False, inplace=True)"
+ "ind = rs[rs['symbols'] != 'A'].iloc[0].index.name\n",
+ "rs.loc[ind]"
]
},
{
"cell_type": "code",
- "execution_count": 110,
- "id": "d7976bb8-05cb-4924-a6e2-90ea8af85d9d",
+ "execution_count": 44,
+ "id": "cce9fa9e-d841-4f45-8813-313f17e7b12f",
"metadata": {},
"outputs": [
{
@@ -62,95 +101,99 @@
" | 0 | \n",
" 1 | \n",
" 2 | \n",
- " \n",
- " \n",
- " | 2 | \n",
- " | \n",
- " | \n",
- " | \n",
+ " symbols | \n",
"
\n",
" \n",
" \n",
" \n",
- " | 20 | \n",
+ " 0 | \n",
" 10 | \n",
" 10 | \n",
" 20 | \n",
+ " A | \n",
"
\n",
" \n",
- " | 22 | \n",
+ " 1 | \n",
" 20 | \n",
" 11 | \n",
" 22 | \n",
+ " A | \n",
"
\n",
" \n",
- " | 24 | \n",
+ " 2 | \n",
" 30 | \n",
" 12 | \n",
" 24 | \n",
+ " A | \n",
"
\n",
" \n",
- " | 26 | \n",
+ " 3 | \n",
" 40 | \n",
" 13 | \n",
" 26 | \n",
+ " A | \n",
"
\n",
" \n",
- " | 28 | \n",
+ " 4 | \n",
" 50 | \n",
" 14 | \n",
" 28 | \n",
+ " A | \n",
"
\n",
" \n",
- " | 30 | \n",
+ " 5 | \n",
" 60 | \n",
" 15 | \n",
" 30 | \n",
+ " D | \n",
"
\n",
" \n",
- " | 32 | \n",
+ " 6 | \n",
" 70 | \n",
" 16 | \n",
" 32 | \n",
+ " D | \n",
"
\n",
" \n",
- " | 34 | \n",
+ " 7 | \n",
" 80 | \n",
" 17 | \n",
" 34 | \n",
+ " D | \n",
"
\n",
" \n",
- " | 36 | \n",
+ " 8 | \n",
" 90 | \n",
" 18 | \n",
" 36 | \n",
+ " D | \n",
"
\n",
" \n",
- " | 38 | \n",
+ " 9 | \n",
" 100 | \n",
" 19 | \n",
" 38 | \n",
+ " D | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " 0 1 2\n",
- "2 \n",
- "20 10 10 20\n",
- "22 20 11 22\n",
- "24 30 12 24\n",
- "26 40 13 26\n",
- "28 50 14 28\n",
- "30 60 15 30\n",
- "32 70 16 32\n",
- "34 80 17 34\n",
- "36 90 18 36\n",
- "38 100 19 38"
+ " 0 1 2 symbols\n",
+ "0 10 10 20 A\n",
+ "1 20 11 22 A\n",
+ "2 30 12 24 A\n",
+ "3 40 13 26 A\n",
+ "4 50 14 28 A\n",
+ "5 60 15 30 D\n",
+ "6 70 16 32 D\n",
+ "7 80 17 34 D\n",
+ "8 90 18 36 D\n",
+ "9 100 19 38 D"
]
},
- "execution_count": 110,
+ "execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
@@ -161,23 +204,26 @@
},
{
"cell_type": "code",
- "execution_count": 112,
+ "execution_count": 31,
"id": "3c456100-4931-4fbd-a63d-531eaf735e70",
"metadata": {},
"outputs": [
{
- "data": {
- "text/plain": [
- "5"
- ]
- },
- "execution_count": 112,
- "metadata": {},
- "output_type": "execute_result"
+ "ename": "InvalidIndexError",
+ "evalue": "RangeIndex(start=0, stop=10, step=1)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mInvalidIndexError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[31], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mrs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrs\u001b[49m\u001b[43m[\u001b[49m\u001b[43mrs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m<\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m31\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[1;32m~\\OneDrive - UBA\\Documents\\Personal\\VS\\aiomql\\venv\\Lib\\site-packages\\pandas\\core\\indexes\\range.py:418\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 416\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Hashable):\n\u001b[0;32m 417\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n\u001b[1;32m--> 418\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_indexing_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 419\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n",
+ "File \u001b[1;32m~\\OneDrive - UBA\\Documents\\Personal\\VS\\aiomql\\venv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6059\u001b[0m, in \u001b[0;36mIndex._check_indexing_error\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 6055\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_check_indexing_error\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[0;32m 6056\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scalar(key):\n\u001b[0;32m 6057\u001b[0m \u001b[38;5;66;03m# if key is not a scalar, directly raise an error (the code below\u001b[39;00m\n\u001b[0;32m 6058\u001b[0m \u001b[38;5;66;03m# would convert to numpy arrays and raise later any way) - GH29926\u001b[39;00m\n\u001b[1;32m-> 6059\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n",
+ "\u001b[1;31mInvalidIndexError\u001b[0m: RangeIndex(start=0, stop=10, step=1)"
+ ]
}
],
"source": [
- "rs.index.get_loc(rs[rs.index <= 31].index[-1])"
+ "rs.index.get_loc(rs[rs.index <= 31].index)"
]
},
{
@@ -203,7 +249,7 @@
},
{
"cell_type": "code",
- "execution_count": 79,
+ "execution_count": 28,
"id": "2d53ccef-72e1-4378-818f-b60682bbd8b7",
"metadata": {},
"outputs": [],
diff --git a/src/aiomql/core/backtester/test_data.py b/src/aiomql/core/backtester/test_data.py
index e7da4c2..9bfa3a3 100644
--- a/src/aiomql/core/backtester/test_data.py
+++ b/src/aiomql/core/backtester/test_data.py
@@ -4,7 +4,8 @@ import pytz
import numpy as np
import pandas as pd
from pandas import DataFrame
-from MetaTrader5 import Tick, SymbolInfo, AccountInfo
+from MetaTrader5 import Tick, SymbolInfo, AccountInfo, TradeOrder, TradePosition, TradeDeal
+import MetaTrader5
from ..constants import TimeFrame, CopyTicks
from .get_data import Data, GetData
@@ -14,6 +15,9 @@ tz = pytz.timezone('Etc/UTC')
class TestData:
+ history_orders: DataFrame
+ history_deals: DataFrame
+
def __init__(self, data: Data):
self._data = data
self.account = data['account']
@@ -24,7 +28,12 @@ class TestData:
self.interval = data['interval']
self.cursor = 0
self.iter = iter(self.interval)
-
+ self.orders: dict[str, dict[int, TradeOrder]] = {}
+ self.open_orders: dict[int, TradeOrder] = {}
+ self.positions: dict[str, dict[int, TradePosition]] = {}
+ self.open_positions = dict[int, TradePosition] = {}
+ self.history_deals = dict[str, dict[int, TradeDeal]] = {}
+
def __next__(self):
self.cursor = next(self.iter)
return self.cursor
@@ -58,7 +67,7 @@ class TestData:
rates = self.rates[symbol][timeframe.name]
start = int(datetime.timestamp(date_from)) if isinstance(date_from, datetime) else int(date_from)
start = round_down(start, timeframe.time)
- start = rates[rates.index <= start].index
+ start = rates[rates.index <= start].iloc[-1].name
start = rates.index.get_loc(start)
end = start + count
return rates.iloc[start:end].to_numpy()
@@ -69,17 +78,51 @@ class TestData:
end = end or None
return rates.iloc[-start_pos:end].to_numpy()
- def get_rates_range(self, symbol: str, timeframe: TimeFrame, date_from: datetime, date_to: datetime) -> np.ndarray:
+ def get_rates_range(self, symbol: str, timeframe: TimeFrame, date_from: datetime | float, date_to: datetime | float) -> np.ndarray:
rates = self.rates[symbol][timeframe.name]
- start = round_down(int(datetime.timestamp(date_from)), timeframe.time)
- start = rates[rates.index <= start].iloc[-1].index
- end = round_up(int(datetime.timestamp(date_to)), timeframe.time)
- end = rates[rates.index >= end].index
+ start = int(datetime.timestamp(date_from)) if isinstance(date_from, datetime) else int(date_from)
+ start = round_down(start, timeframe.time)
+ start = rates[rates.index <= start].iloc[-1].name
+ end = int(datetime.timestamp(date_to)) if isinstance(date_to, datetime) else int(date_to)
+ end = round_up(end, timeframe.time)
+ end = rates[rates.index >= end].iloc[-1].name
return rates.loc[start:end].to_numpy()
- def get_ticks_from(self, symbol: str, date_from: datetime | float, count: int, flags: CopyTicks) -> DataFrame:
+ def get_ticks_from(self, symbol: str, date_from: datetime | float, count: int, flags: CopyTicks) -> np.ndarray:
ticks = self.ticks[symbol]
start = int(datetime.timestamp(date_from)) if isinstance(date_from, datetime) else int(date_from)
- start = round_down(start, 1)
+ start = ticks[ticks.index <= start].iloc[-1].name
+ start = ticks.index.get_loc(start)
end = start + count
- return ticks.loc[start:end]
+ return ticks.iloc[start:end]
+
+ def get_ticks_range(self, symbol: str, date_from: datetime | float, date_to: datetime | float, flags) -> np.ndarray:
+ ticks = self.ticks[symbol]
+ start = int(datetime.timestamp(date_from)) if isinstance(date_from, datetime) else int(date_from)
+ start = ticks[ticks.index <= start].iloc[-1].index
+ end = int(datetime.timestamp(date_to)) if isinstance(date_to, datetime) else int(date_to)
+ end = ticks[ticks.index >= end].iloc[-1].index
+ return ticks.loc[start:end].to_numpy()
+
+ def get_orders_total(self) -> int:
+ return len(self.live_orders)
+
+ def get_orders(self, symbol: str = '', group: str = '', ticket: int = None) -> tuple[TradeOrder, ...]:
+ if ticket:
+ return self.live_orders.get(ticket, ())
+
+ elif symbol:
+ return tuple(order for order in self.orders.get(symbol, ()) if order.ticket in self.live_orders)
+
+ elif group:
+ return tuple(self.live_orders.values())
+
+ else:
+ return tuple(self.live_orders.values())
+
+ def history_orders_total(self, date_from: datetime | float, date_to: datetime | float):
+ start =
+
+
+
+