Merge pull request #196 from amor71/optimizer

bug fixes

.travis.yml

@@ -1,6 +1,6 @@
 language: python
 python:
-- '3.8'
+- '3.9'
 os: linux
 services:
 - postgresql

README.md

@@ -1,5 +1,5 @@
 # LiuAlgoTrader
-[![Build Status](https://travis-ci.org/amor71/LiuAlgoTrader.svg?branch=master)](https://travis-ci.org/amor71/LiuAlgoTrader)
+[![Build Status](https://travis-ci.com/amor71/LiuAlgoTrader.svg?branch=master)](https://travis-ci.org/amor71/LiuAlgoTrader)
 ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/liualgotrader)
 [![Python 3](https://pyup.io/repos/github/amor71/LiuAlgoTrader/python-3-shield.svg)](https://pyup.io/repos/github/amor71/LiuAlgoTrader/)
 [![Updates](https://pyup.io/repos/github/amor71/LiuAlgoTrader/shield.svg)](https://pyup.io/repos/github/amor71/LiuAlgoTrader/)
@@ -11,7 +11,7 @@
 
 **LiuAlgoTrader** is a scalable, multi-process ML-ready framework
 for effective algorithmic trading. The framework simplify development, testing,
-deployment, analysis and training algo trading strategies. The framework **automatically analyzes** trading sessions, and the analysis may be used to train predictive models.  
+deployment, analysis and training algo trading strategies. The framework **automatically analyzes** trading sessions, hyper-parameters optimization, and the analysis may be used to train predictive models.  
 
 LiuAlgoTrader can run on a laptop and 
 *hedge-on-the-go*, or run on a multi-core hosted Linux server 

analysis/notebooks/hyperparameters_analysis.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Take Action: Select portfolio for analysis"
+    "## Take Action: Select Optimizer run for analysis"
    ]
   },
   {
@@ -13,7 +13,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "optimizer_run_id = \"c76c97cb-d8cd-4498-8112-d75b2b1f1443\""
+    "optimizer_run_id = \"28fb93cc-489d-48b3-950b-ee207d7317ed\""
    ]
   },
   {
@@ -63,36 +63,33 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "analysis = pd.DataFrame(columns=['profile_id'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "analysis = pd.DataFrame(columns=['profile_id'])\n",
     "analysis['profile_id'] = hyperparameters.index.levels[0]\n",
+    "analysis['configurations'] = analysis['profile_id'].apply(\n",
+    "    lambda x: hyperparameters[\n",
+    "            hyperparameters.index.get_level_values(\"portfolio_id\") == x\n",
+    "        ].configurations[-1]\n",
+    ")\n",
     "analysis['sharpe'] = analysis['profile_id'].apply(\n",
     "    lambda x: qs.stats.sharpe(\n",
     "        returns=hyperparameters[\n",
     "            hyperparameters.index.get_level_values(\"portfolio_id\") == x\n",
-    "        ]\n",
-    "    ).totals\n",
+    "        ].totals\n",
+    "    )\n",
     ")\n",
     "analysis['volatility'] = analysis['profile_id'].apply(\n",
     "    lambda x: qs.stats.volatility(\n",
     "        returns=hyperparameters[\n",
     "            hyperparameters.index.get_level_values(\"portfolio_id\") == x\n",
-    "        ]\n",
-    "    ).totals\n",
+    "        ].totals\n",
+    "    )\n",
     ")\n",
     "analysis['total'] = analysis['profile_id'].apply(\n",
     "    lambda x: hyperparameters[\n",
     "            hyperparameters.index.get_level_values(\"portfolio_id\") == x\n",
-    "        ]\n",
-    "    .totals[-1]\n",
-    ")\n"
+    "        ].totals[-1]\n",
+    ")\n",
+    "\n"
    ]
   },
   {

analysis/notebooks/portfolio_analysis.ipynb

@@ -13,7 +13,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "portfolio_id = \"765c2f7d-658f-4e29-a276-11fc904cdff9\"\n",
+    "portfolio_id = \"13c0303f-814b-4402-a68c-6de9369b87af\"\n",
     "report_title = \"Trade JNUG/JDST (based on GDXJ SMA)volatility,  Jun-20 to Jun-21 \""
    ]
   },
@@ -73,8 +73,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "qs.extend_pandas()\n",
-    "qs.reports.html(portfolio_returns.totals.pct_change(1), \"SPY\", title=report_title, output=\"/tmp/tearsheet.html\")"
+    "if len(portfolio_returns):\n",
+    "    qs.extend_pandas()\n",
+    "    qs.reports.html(portfolio_returns.totals.pct_change(1), \"SPY\", title=report_title, output=\"/tmp/tearsheet.html\")"
    ]
   },
   {

liualgotrader/analytics/analysis.py

@@ -483,9 +483,14 @@ def calc_portfolio_returns(portfolio_id: str) -> pd.DataFrame:
     loop = asyncio.get_event_loop()
     _ = loop.run_until_complete(Portfolio.load_by_portfolio_id(portfolio_id))
 
-    account_id, initial_account_size = loop.run_until_complete(
-        Portfolio.load_details(portfolio_id)
-    )
+    try:
+        account_id, initial_account_size = loop.run_until_complete(
+            Portfolio.load_details(portfolio_id)
+        )
+    except Exception:
+        print("ERROR loading portfolio-id, please verify id and re-run.")
+        return pd.DataFrame()
+
     data_loader = DataLoader()
     trades = load_trades_by_portfolio(portfolio_id)
     start_date = trades.client_time.min().date()
@@ -515,15 +520,16 @@ def calc_portfolio_returns(portfolio_id: str) -> pd.DataFrame:
 def calc_hyperparameters_analysis(optimizer_run_id: str) -> pd.DataFrame:
     loop = asyncio.get_event_loop()
 
-    portfolio_ids = loop.run_until_complete(
-        OptimizerRun.get_portfolio_ids(optimizer_run_id)
+    portfolio_ids_parameters = loop.run_until_complete(
+        OptimizerRun.get_portfolio_ids_parameters(optimizer_run_id)
     )
-
+    portfolio_ids, hypers = list(map(list, zip(*portfolio_ids_parameters)))
     df = None
     if len(portfolio_ids):
         for i in tqdm(range(len(portfolio_ids)), desc="Loading Portfolios"):
             _df = calc_portfolio_returns(portfolio_ids[i])
             _df["portfolio_id"] = portfolio_ids[i]
+            _df["configurations"] = hypers[i]
             _df.reset_index(inplace=True)
             _df = _df.set_index(["portfolio_id", "date"])
             df = pd.concat([df, _df], axis=0) if df is not None else _df