Better support for recent pandas

.github/workflows/integration-test.yml

@@ -15,10 +15,10 @@ jobs:
 
     - uses: actions/checkout@v4
 
-    - name: Set up Python 3.8
+    - name: Set up Python 3.10
       uses: actions/setup-python@v5
       with:
-        python-version: 3.8
+        python-version: 3.10
 
     - name: Install Poetry
       uses: abatilo/actions-poetry@v2

.github/workflows/unit-test.yml

@@ -13,7 +13,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ "3.8", "3.9", "3.10" ]
+        python-version: [ "3.9", "3.10", "3.11" ]
 
     steps:
 

pyproject.toml

@@ -18,25 +18,24 @@ classifiers = [
     "Operating System :: OS Independent",
     "Programming Language :: Cython",
     "Programming Language :: Python",
-    "Programming Language :: Python :: 3.7",
-    "Programming Language :: Python :: 3.8",
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
     "Topic :: Scientific/Engineering",
     "Topic :: Software Development :: Libraries",
     "License :: OSI Approved :: BSD License",
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.7,<4.0"
+python = ">=3.9,<4.0"
 requests = "^2.24"
 pycryptodome = "^3.9"
 requests-cache = "^0.5"
 six = "^1.15"
 lightstreamer-client-lib = "^1.0.3"
 
-pandas = { version = "^1", optional = true }
-munch = { version = "^2.5", optional = true }
+pandas = {version = "^2", optional = true}
+munch = {version = "^2.5", optional = true}
 tenacity = {version = "^8", optional = true}
 
 [tool.poetry.extras]

sample/rest.ipynb

@@ -0,0 +1,198 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ec8c83b74991ebc7",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# Imports, objects, and starting a session\n",
+    "\n",
+    "Using v2 sessions by default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6236766fd0340597",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from trading_ig.rest import IGService\n",
+    "from trading_ig.config import config\n",
+    "\n",
+    "service = IGService(\n",
+    "    config.username,\n",
+    "    config.password,\n",
+    "    config.api_key,\n",
+    "    config.acc_type,\n",
+    "    acc_number=config.acc_number,\n",
+    ")\n",
+    "# creating a v2 session\n",
+    "service.create_session()\n",
+    "\n",
+    "# creating a v3 session\n",
+    "#service.create_session(version=\"3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa41443cec0fb2d2",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# Fetching account information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19f74547fb3174e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "service.fetch_accounts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3e01ba486501770",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# Fetching historic prices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b72874aaba7a665",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = service.fetch_historical_prices_by_epic(\n",
+    "    epic=\"IX.D.FTSE.DAILY.IP\",\n",
+    "    resolution=\"D\",\n",
+    "    start_date=\"2024-01-01\",\n",
+    "    end_date=\"2024-01-22\",\n",
+    "    format=service.flat_prices\n",
+    ")\n",
+    "\n",
+    "data[\"prices\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e34db8ecfc649f6e",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# Save prices to file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9cd0c0854f0a17c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "save_path = Path('~').expanduser() / \"prices.csv\"\n",
+    "print(f\"saving historic prices to {save_path}\")\n",
+    "\n",
+    "data[\"prices\"].to_csv(save_path, date_format=\"%Y-%m-%dT%H:%M:%S%z\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5ecd4aa7bc33dea",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# Fetch history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79c0c6ed2ca08a1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime, timedelta\n",
+    "to_date = datetime.now()\n",
+    "from_date = to_date - timedelta(days=7)\n",
+    "\n",
+    "service.fetch_transaction_history(from_date=from_date, to_date=to_date)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52e40ae5ef37d3eb",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# Fetch activity (simple)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a089d1babd98781",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "service.fetch_account_activity(from_date=from_date, to_date=to_date)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5411321a662441be",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# Fetch activity (detailed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c31119d129505286",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "service.fetch_account_activity(from_date=from_date, to_date=to_date, detailed=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

trading_ig/rest.py

@@ -33,7 +33,7 @@
     from .utils import munchify
 
 if _HAS_PANDAS:
-    from .utils import pd, np
+    from .utils import pd
     from pandas import json_normalize
 
 from threading import Thread
@@ -1613,6 +1613,11 @@ def cols(typ):
             keys.append("last")
 
         df2 = pd.concat(data, axis=1, keys=keys)
+
+        # force all object columns to be numeric, NaN if error
+        for col in df2.select_dtypes(include=["object"]).columns:
+            df2[col] = pd.to_numeric(df2[col], errors="coerce")
+
         return df2
 
     def flat_prices(self, prices, version):
@@ -1637,9 +1642,11 @@ def flat_prices(self, prices, version):
         if version == "3":
             df = df.set_index("snapshotTimeUTC")
             df = df.drop(columns=["snapshotTime"])
+            date_format = "%Y-%m-%dT%H:%M:%S"
         else:
             df = df.set_index("snapshotTime")
-        df.index = pd.to_datetime(df.index, format=DATE_FORMATS[int(version)])
+            date_format = DATE_FORMATS[int(version)]
+        df.index = pd.to_datetime(df.index, format=date_format)
         df.index.name = "DateTime"
         df = df.drop(
             columns=[
@@ -1798,7 +1805,6 @@ def fetch_historical_prices_by_epic(
             format = self.format_prices
         if self.return_dataframe:
             data["prices"] = format(data["prices"], version)
-            data["prices"] = data["prices"].fillna(value=np.nan)
         self.log_allowance(data["metadata"])
         return data
 
@@ -1820,7 +1826,6 @@ def fetch_historical_prices_by_epic_and_num_points(
             format = self.format_prices
         if self.return_dataframe:
             data["prices"] = format(data["prices"], version)
-            data["prices"] = data["prices"].fillna(value=np.nan)
         return data
 
     def fetch_historical_prices_by_epic_and_date_range(
@@ -1884,7 +1889,6 @@ def fetch_historical_prices_by_epic_and_date_range(
             format = self.format_prices
         if self.return_dataframe:
             data["prices"] = format(data["prices"], version)
-            data["prices"] = data["prices"].fillna(value=np.nan)
         return data
 
     def log_allowance(self, data):

trading_ig/utils.py

@@ -46,13 +46,13 @@ def conv_resol(resolution):
             to_offset("10Min"): "MINUTE_10",
             to_offset("15Min"): "MINUTE_15",
             to_offset("30Min"): "MINUTE_30",
-            to_offset("1H"): "HOUR",
-            to_offset("2H"): "HOUR_2",
-            to_offset("3H"): "HOUR_3",
-            to_offset("4H"): "HOUR_4",
+            to_offset("1h"): "HOUR",
+            to_offset("2h"): "HOUR_2",
+            to_offset("3h"): "HOUR_3",
+            to_offset("4h"): "HOUR_4",
             to_offset("D"): "DAY",
             to_offset("W"): "WEEK",
-            to_offset("M"): "MONTH",
+            to_offset("ME"): "MONTH",
         }
         offset = to_offset(resolution)
         if offset in d: