The fund’s portfolio managers were charged with boosting risk-adjusted investment returns by increasing their use of derivatives overlays. They needed to model and backtest new strategies at the instrument and portfolio levels before putting “real money” to work. They also wanted to calculate intraday P&L for their portfolio.
They were limited by their existing valuation platform, which didn’t cover many of the derivatives they needed, and couldn’t manage portfolios that combined cash and derivative instruments. Excel spreadsheets closed some of the valuation gaps, but suffered from their usual limitations around scalability, documentation, and modeling.
Given their requirements and the constraints of the existing environment, the team decided on a wholesale upgrade of their pricing and valuation system. Team members were comfortable coding in Python and wanted to leverage its flexibility and power to adjust the modeling of individual instruments.
At the same time, as portfolio managers their core task was generating alpha for their clients. They did not want to be distracted by the need to program extensively in Python. Therefore, they required pre-built pricers and accurate representations of the market “out of the box”, i.e., without requiring extensive calibration and adjustment to cover their investment universe.