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notebooks/01-use-cases/finance/README.md

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+<p align="center">
+  <img src="https://data.atoti.io/notebooks/banners/logo.png" alt="atoti logo">
+</p>
+
+<p align="center">
+  <a href="https://github.com/atoti/atoti/discussions"><img src="https://img.shields.io/github/discussions/atoti/atoti" alt="GitHub Discussion"></a>
+  <a href="https://hits.seeyoufarm.com"><img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Fatoti%2Fnotebooks&count_bg=%23FF7375&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false" alt="Hits"></a>
+  <a href="https://stackoverflow.com/questions/tagged/atoti"><img src="https://img.shields.io/badge/StackOverflow-atoti-f58024.svg" alt="stackoverflow"></a>
+</p>
+
+
+## Financial use cases
+
+<ol><li>
+<details open>
+<summary><b>Front Office</b></summary><blockquote>
+<br/>
+
+<table>
+
+<img src="https://data.atoti.io/notebooks/banners/Atoti_plus_RVB_Icon.png" width="15px"/><b> indicates requires Atoti+</b>
+
+<tr>
+    <td><b>Notebooks</b></td>
+    <td><b>Related media</b></td>
+</tr>
+<tr>
+<td rowspan="1"><a href="notebooks/01-use-cases/finance/front-office/cumulative-pnl/main.ipynb">&nbsp;cumulative-pnl&nbsp;</a></td>
+<td><a href="https://www.atoti.io/articles/dynamic-performance-analytics-handling-of-non-linear-aggregation/?utm_source=github">📰&nbsp;Dynamic Performance Analytics: Handling of non-linear aggregation</a></td>
+</tr>
+<tr>
+<td rowspan="1"><a href="notebooks/01-use-cases/finance/front-office/pnl-explained/main.ipynb">&nbsp;pnl-explained&nbsp;</a></td>
+<td><a href="https://www.atoti.io/articles/pnl-explained-with-atoti/?utm_source=github">📰&nbsp;PnL Explained with atoti</a></td>
+</tr>
+<tr>
+<td rowspan="2"><a href="notebooks/01-use-cases/finance/front-office/real-time-risk/main.ipynb">&nbsp;real-time-risk&nbsp;</a></td>
+<td><a href="https://www.atoti.io/guides/implementing-real-time-sources-with-atoti/?utm_source=github">📰&nbsp;Implementing real-time sources with atoti</a></td>
+</tr>
+<tr>
+<td valign="center"><a href="https://www.atoti.io/guides/implementing-real-time-sources-with-atoti/?utm_source=github">📰&nbsp;Real-time risk analytics with atoti</a></td>
+</tr>
+</table>
+<br/>
+</blockquote></details>
+
+</li><li>
+<details open>
+<summary><b>Risk Management</b></summary><blockquote>
+<br/>
+
+<ul><li>
+<details open>
+<summary><b>Credit Risk</b></summary><blockquote>
+<br/>
+
+<table>
+
+<img src="https://data.atoti.io/notebooks/banners/Atoti_plus_RVB_Icon.png" width="15px"/><b> indicates requires Atoti+</b>
+
+<tr>
+    <td><b>Notebooks</b></td>
+    <td><b>Related media</b></td>
+</tr>
+<tr>
+<td rowspan="2"><a href="notebooks/01-use-cases/finance/risk-management/credit-risk/ifrs9/main.ipynb">&nbsp;ifrs9&nbsp;</a></td>
+<td><a href="https://www.atoti.io/articles/ifrs-9-data-viz-ecl-pd-analytics-and-the-vintage-matrix/?utm_source=github">📰&nbsp;IFRS 9 Data Viz: ECL, PD Analytics and the Vintage Matrix</a></td>
+</tr>
+<tr>
+<td valign="center"><a href="https://www.atoti.io/articles/tutorial-how-to-build-an-ifrs-9-solution-with-python-and-atoti/?utm_source=github">📰&nbsp;Tutorial: How to build an IFRS 9 solution with Python and atoti</a></td>
+</tr>
+<tr>
+<td rowspan="1"><a href="notebooks/01-use-cases/finance/risk-management/credit-risk/sa-ccr/main.ipynb">&nbsp;sa-ccr&nbsp;</a></td>
+<td><a href="https://www.atoti.io/articles/sa-ccr-with-python-and-atoti/?utm_source=github">📰&nbsp;SA-CCR with python and atoti</a></td>
+</tr>
+<tr>
+<td rowspan="1"><a href="notebooks/01-use-cases/finance/risk-management/credit-risk/xva/main.ipynb">&nbsp;xva&nbsp;</a></td>
+<td><a href="https://www.atoti.io/articles/interactive-xva-analytics-with-python-and-atoti/?utm_source=github">📰&nbsp;Interactive xVA analytics with Python and atoti</a></td>
+</tr>
+<tr>
+<td rowspan="1"><a href="/notebooks/01-use-cases/finance/treasury/collateral-shortfall-forecast/notebooks/main.ipynb">&nbsp;collateral-shortfall-forecast&nbsp;</a></td>
+<td><a href="https://www.atoti.io/articles/collateral-modelling-and-simulation-using-atoti-and-machine-learning/?utm_source=github">📰&nbsp;Collateral Modelling and Simulation Using atoti and Machine Learning</a></td>
+</tr>
+</table>
+<br/>
+</blockquote></details>
+</li><li>
+<details open>
+<summary><b>Market Risk</b></summary><blockquote>
+<br/>
+
+<table>
+
+<img src="https://data.atoti.io/notebooks/banners/Atoti_plus_RVB_Icon.png" width="15px"/><b> indicates requires Atoti+</b>
+
+<tr>
+    <td><b>Notebooks</b></td>
+    <td><b>Related media</b></td>
+</tr>
+<tr>
+<td rowspan="1"><a href="notebooks/01-use-cases/finance/risk-management/market-risk/sbm/main.ipynb">&nbsp;sbm&nbsp;</a></td>
+<td><a href="https://www.atoti.io/articles/faster-algorithm-for-frtb-sbm-risk-aggregation/?utm_source=github">📰&nbsp;Faster Algorithm for FRTB SBM Risk Aggregation</a></td>
+</tr>
+<tr>
+<td rowspan="1"><a href="notebooks/01-use-cases/finance/risk-management/market-risk/stressed-var-calibration/main.ipynb">&nbsp;stressed-var-calibration&nbsp;</a></td>
+<td><a href="https://www.atoti.io/articles/market-risk-analytics-in-python-interactive-rolling-var/?utm_source=github">📰&nbsp;Market risk analytics in python: Interactive rolling VaR</a></td>
+</tr>
+<tr>
+<td rowspan="1"><a href="notebooks/01-use-cases/finance/front-offices/main.ipynb">&nbsp;pnl-explained&nbsp;</a></td>
+<td><a href="https://www.atoti.io/articles/pnl-explained-with-atoti/?utm_source=github">📰&nbsp;PnL Explained with atoti</a></td>
+</tr>
+<tr>
+<td rowspan="1"><a href="notebooks/01-use-cases/finance/risk-management/market-risk/value-at-risk/main.ipynb">&nbsp;value-at-risk&nbsp;</a></td>
+<td>&nbsp</td>
+</tr>
+</table>
+<br/>
+</blockquote></details>
+</li><li>
+<details open>
+<summary><b>Operational Risk</b></summary><blockquote>
+<br/>
+
+<table>
+
+<img src="https://data.atoti.io/notebooks/banners/Atoti_plus_RVB_Icon.png" width="15px"/><b> indicates requires Atoti+</b>
+
+<tr>
+    <td><b>Notebooks</b></td>
+    <td><b>Related media</b></td>
+</tr>
+<tr>
+<td rowspan="2"><a href="notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/main.ipynb">&nbsp;credit-card-fraud-detection&nbsp;</a></td>
+<td><a href="https://www.atoti.io/articles/credit-card-fraud-detection-part1/?utm_source=github">📰&nbsp;Part 1: Investigating fraudulent transactions real-time with atoti</a></td>
+</tr>
+<tr>
+<td valign="center"><a href="https://www.atoti.io/articles/credit-card-fraud-detection-part2//?utm_source=github">📰&nbsp;Part 2: Fraud detection with Faker and PyCaret</a></td>
+</tr>
+</table>
+<br/>
+</blockquote></details>
+</li>
+</ul>
+<br/>
+</blockquote></details>
+
+</li><li>
+<details open>
+<summary><b>Treasury</b></summary><blockquote>
+<br/>
+
+<table>
+
+<img src="https://data.atoti.io/notebooks/banners/Atoti_plus_RVB_Icon.png" width="15px"/><b> indicates requires Atoti+</b>
+
+<tr>
+    <td><b>Notebooks</b></td>
+    <td><b>Related media</b></td>
+</tr>
+<tr>
+<td rowspan="1"><a href="notebooks/01-use-cases/finance/treasury/collateral-shortfall-forecast/main.ipynb">&nbsp;collateral-shortfall-forecast&nbsp;</a></td>
+<td><a href="https://www.atoti.io/articles/collateral-modelling-and-simulation-using-atoti-and-machine-learning/?utm_source=github">📰&nbsp;Collateral Modelling and Simulation Using atoti and Machine Learning</a></td>
+</tr>
+<tr>
+<td rowspan="1"><a href="notebooks/01-use-cases/finance/treasury/collateral-shortfall-monitoring/main.ipynb">&nbsp;collateral-shortfall-monitoring&nbsp;</a></td>
+<td><a href="https://www.atoti.io/articles/rapid-collateral-modelling-and-simulation-with-atoti/?utm_source=github">📰&nbsp;Rapid collateral modelling and simulation with atoti</a></td>
+</tr>
+</table>
+<br/>
+</blockquote></details>
+
+</li><li>
+<details open>
+<summary><b>Insurance</b></summary><blockquote>
+<br/>
+
+<table>
+
+<img src="https://data.atoti.io/notebooks/banners/Atoti_plus_RVB_Icon.png" width="15px"/><b> indicates requires Atoti+</b>
+
+<tr>
+    <td><b>Notebooks</b></td>
+    <td><b>Related media</b></td>
+</tr>
+<tr>
+<td rowspan="1"><a href="notebooks/01-use-cases/finance/insurance/customer 360/03-main-csv.ipynb">&nbsp;customer 360&nbsp;</a></td>
+<td><a href="https://www.atoti.io/articles/customer-360-how-it-can-be-achieved-with-atoti/?utm_source=github">📰&nbsp;Customer 360: How it can be achieved with atoti</a></td>
+</tr>
+<tr>
+<td rowspan="1"><a href="notebooks/01-use-cases/finance/insurance/price-elasticity/main.ipynb">&nbsp;price-elasticity&nbsp;</a></td>
+<td><a href="https://www.atoti.io/articles/unravel-price-elasticity-of-demand-with-atoti/?utm_source=github">📰&nbsp;Unravel price elasticity of demand with atoti</a></td>
+</tr>
+</table>
+<br/>
+</blockquote></details>
+
+</li></ol>
+
+
+<br/>
+<p align="center">
+  Reach out to <a href="https://www.activeviam.com/contact-us/"><img src="https://data.atoti.io/notebooks/banners/ActiveViam-RVB-150dpi.png" alt="atoti logo" width="150px" /></a> for more information.
+</p>

notebooks/01-use-cases/finance/front-office/README.md

@@ -0,0 +1,44 @@
+<p align="center">
+  <img src="https://data.atoti.io/notebooks/banners/logo.png" alt="atoti logo">
+</p>
+
+<p align="center">
+  <a href="https://github.com/atoti/atoti/discussions"><img src="https://img.shields.io/github/discussions/atoti/atoti" alt="GitHub Discussion"></a>
+  <a href="https://hits.seeyoufarm.com"><img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Fatoti%2Fnotebooks&count_bg=%23FF7375&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false" alt="Hits"></a>
+  <a href="https://stackoverflow.com/questions/tagged/atoti"><img src="https://img.shields.io/badge/StackOverflow-atoti-f58024.svg" alt="stackoverflow"></a>
+</p>
+
+
+## Financial use cases for Front Office
+
+
+<table>
+
+<img src="https://data.atoti.io/notebooks/banners/Atoti_plus_RVB_Icon.png" width="15px"/><b> indicates requires Atoti+</b>
+
+<tr>
+    <td><b>Notebooks</b></td>
+    <td><b>Related media</b></td>
+</tr>
+<tr>
+<td rowspan="1"><a href="notebooks/01-use-cases/finance/front-office/cumulative-pnl/main.ipynb">&nbsp;cumulative-pnl&nbsp;</a></td>
+<td><a href="https://www.atoti.io/articles/dynamic-performance-analytics-handling-of-non-linear-aggregation/?utm_source=github">📰&nbsp;Dynamic Performance Analytics: Handling of non-linear aggregation</a></td>
+</tr>
+<tr>
+<td rowspan="1"><a href="notebooks/01-use-cases/finance/front-office/pnl-explained/main.ipynb">&nbsp;pnl-explained&nbsp;</a></td>
+<td><a href="https://www.atoti.io/articles/pnl-explained-with-atoti/?utm_source=github">📰&nbsp;PnL Explained with atoti</a></td>
+</tr>
+<tr>
+<td rowspan="2"><a href="notebooks/01-use-cases/finance/front-office/real-time-risk/main.ipynb">&nbsp;real-time-risk&nbsp;</a></td>
+<td><a href="https://www.atoti.io/guides/implementing-real-time-sources-with-atoti/?utm_source=github">📰&nbsp;Implementing real-time sources with atoti</a></td>
+</tr>
+<tr>
+<td valign="center"><a href="https://www.atoti.io/guides/implementing-real-time-sources-with-atoti/?utm_source=github">📰&nbsp;Real-time risk analytics with atoti</a></td>
+</tr>
+</table>
+
+
+<br/>
+<p align="center">
+  Reach out to <a href="https://www.activeviam.com/contact-us/"><img src="https://data.atoti.io/notebooks/banners/ActiveViam-RVB-150dpi.png" alt="atoti logo" width="150px" /></a> for more information.
+</p>

notebooks/real-time-risk/pricing_engine_emulator.py → notebooks/01-use-cases/finance/front-office/real-time-risk/pricing_engine_emulator.py

@@ -1,65 +1,65 @@
-import numpy as np
-import QuantLib as ql
-import pandas as pd
-
-
-def reprice_trade(spot_price, trade, calc_date):
-    # This function takes option trade object and spot prices and computes valuation and risk metrics.
-    # Volatility and interest rates are set to some constants for simplicity.
-
-    calculation_date = ql.Date(calc_date.day, calc_date.month, calc_date.year)
-    ql.Settings.instance().evaluationDate = calculation_date
-
-    # conventions
-    day_count = ql.Actual365Fixed()
-    calendar = ql.UnitedStates()
-    volatility = 0.3
-    dividend_rate = 0.0163
-    risk_free_rate = 0.001
-
-    maturity = trade["Maturity"]
-
-    maturity_date = ql.Date(maturity.day, maturity.month, maturity.year)
-    option_type = ql.Option.Call if trade.OptionType == "Call" else ql.Option.Put
-    strike_price = trade.Strike
-
-    # construct the European Option
-    payoff = ql.PlainVanillaPayoff(option_type, strike_price)
-    exercise = ql.EuropeanExercise(maturity_date)
-    european_option = ql.VanillaOption(payoff, exercise)
-
-    # The Black-Scholes-Merto process is constructed here.
-    spot_handle = ql.QuoteHandle(ql.SimpleQuote(spot_price))
-    flat_ts = ql.YieldTermStructureHandle(
-        ql.FlatForward(calculation_date, risk_free_rate, day_count)
-    )
-    dividend_yield = ql.YieldTermStructureHandle(
-        ql.FlatForward(calculation_date, dividend_rate, day_count)
-    )
-    flat_vol_ts = ql.BlackVolTermStructureHandle(
-        ql.BlackConstantVol(calculation_date, calendar, volatility, day_count)
-    )
-    bsm_process = ql.BlackScholesMertonProcess(
-        spot_handle, dividend_yield, flat_ts, flat_vol_ts
-    )
-
-    european_option.setPricingEngine(ql.AnalyticEuropeanEngine(bsm_process))
-
-    return {
-        "AsOfDate": calc_date.strftime("%Y-%m-%d"),
-        "TradeId": trade["TradeId"],
-        "RiskFactor": trade["Ticker"],
-        "MarketValue": european_option.NPV(),
-        "Delta": european_option.delta() * spot_price * trade["Quantity"],
-    }
-
-
-def reprice_portfolio(market_data, positions, calc_date):
-    # This function reprices trades and greeks if market data is available,
-    # and returns new risk numbers as a dataframe
-    risk = []
-    for i, trade in positions.iterrows():
-        spot_price_update = market_data[trade["Ticker"]]
-        if not np.isnan(spot_price_update):
-            risk = risk + [reprice_trade(spot_price_update, trade, calc_date)]
-    return pd.DataFrame(data=risk)
+import numpy as np
+import QuantLib as ql
+import pandas as pd
+
+
+def reprice_trade(spot_price, trade, calc_date):
+    # This function takes option trade object and spot prices and computes valuation and risk metrics.
+    # Volatility and interest rates are set to some constants for simplicity.
+
+    calculation_date = ql.Date(calc_date.day, calc_date.month, calc_date.year)
+    ql.Settings.instance().evaluationDate = calculation_date
+
+    # conventions
+    day_count = ql.Actual365Fixed()
+    calendar = ql.UnitedStates()
+    volatility = 0.3
+    dividend_rate = 0.0163
+    risk_free_rate = 0.001
+
+    maturity = trade["Maturity"]
+
+    maturity_date = ql.Date(maturity.day, maturity.month, maturity.year)
+    option_type = ql.Option.Call if trade.OptionType == "Call" else ql.Option.Put
+    strike_price = trade.Strike
+
+    # construct the European Option
+    payoff = ql.PlainVanillaPayoff(option_type, strike_price)
+    exercise = ql.EuropeanExercise(maturity_date)
+    european_option = ql.VanillaOption(payoff, exercise)
+
+    # The Black-Scholes-Merto process is constructed here.
+    spot_handle = ql.QuoteHandle(ql.SimpleQuote(spot_price))
+    flat_ts = ql.YieldTermStructureHandle(
+        ql.FlatForward(calculation_date, risk_free_rate, day_count)
+    )
+    dividend_yield = ql.YieldTermStructureHandle(
+        ql.FlatForward(calculation_date, dividend_rate, day_count)
+    )
+    flat_vol_ts = ql.BlackVolTermStructureHandle(
+        ql.BlackConstantVol(calculation_date, calendar, volatility, day_count)
+    )
+    bsm_process = ql.BlackScholesMertonProcess(
+        spot_handle, dividend_yield, flat_ts, flat_vol_ts
+    )
+
+    european_option.setPricingEngine(ql.AnalyticEuropeanEngine(bsm_process))
+
+    return {
+        "AsOfDate": calc_date.strftime("%Y-%m-%d"),
+        "TradeId": trade["TradeId"],
+        "RiskFactor": trade["Ticker"],
+        "MarketValue": european_option.NPV(),
+        "Delta": european_option.delta() * spot_price * trade["Quantity"],
+    }
+
+
+def reprice_portfolio(market_data, positions, calc_date):
+    # This function reprices trades and greeks if market data is available,
+    # and returns new risk numbers as a dataframe
+    risk = []
+    for i, trade in positions.iterrows():
+        spot_price_update = market_data[trade["Ticker"]]
+        if not np.isnan(spot_price_update):
+            risk = risk + [reprice_trade(spot_price_update, trade, calc_date)]
+    return pd.DataFrame(data=risk)