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      "## Source material\n",
      "\n",
      "- [http://www.hilpisch.com/YH_PyData_Eurex_Tutorial.html](http://www.hilpisch.com/YH_PyData_Eurex_Tutorial.html)\n",
      "- [http://www.hilpisch.com/YH_PyData_Eurex_Tutorial.ipynb](http://www.hilpisch.com/YH_PyData_Eurex_Tutorial.ipynb)"
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      "## Background\n",
      "\n",
      "- Not just data munging as primary problem, but it is a big problem.\n",
      "    - Sources, formats, cleaning missing data.\n",
      "- Performance too\n",
      "- Organisational problems\n",
      "    - Teams are silos. People who need answers can't ask questions. People who can give answers can't express them.\n",
      "- Continuum Analytics vision\n",
      "    - Simple, interactive, collaborative, but still scalable performance"
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      "## Notes\n",
      "\n",
      "- pandas offers access to free financial sources, but beware! Not clean, not reliable, but good for playing around.\n",
      "- *Volatility clustering*: if you plot log difference in close (log returns) you notice volatility clusters, isn't randomly distributed.\n",
      "- Investors want volalitity, offers short-term trading profit chances\n",
      "- Do you have to shift Returns by 1 (back 1) before multiplying? No.\n",
      "- This model doesn't take portfolio rebalancing (?) into account.\n",
      "    - Rebalancing means restoring e.g. 70% in X, 30% in Y balance of your portfolio\n",
      "- Should use discounting, rather than simple sum of Earnings\n",
      "- Err on the side of readability. Don't put too many operations, particular in Pandas, onto one line.\n",
      "    - Unless performance, when measured, is an issue.\n",
      "- VSTOXX vs EUROSTOXX\n",
      "    - EUROSTOXX is mean reverting, standard theory of stocks apply.\n",
      "    - VSTOXX is kind of like an interest rate. Percentage points, aggregate, implies volatility of puts and calls.\n",
      "- Log returns helps comparing two different time series in a mathematical way. Seems a common pattern.\n",
      "- Good link: http://scipy-lectures.github.io/advanced/mathematical_optimization/\n",
      "\n",
      "###\u00a0High frequency trading data\n",
      "\n",
      "- High frequency data not well covered by textbooks, even just the data sizes changes the game.\n",
      "- Worse, heterogenous time intervals! Tick data comes when it comes, not fixed.\n",
      "- !!AI Can you use numexpr to df.apply(...) some optimized function?\n",
      "\n",
      "## Why Python?\n",
      "\n",
      "- Nothing compares to Python's sheer breadth.\n",
      "    - What, in Ruby, comes close to NumPy, SciPy, and Pandas?\n",
      "- R?\n",
      "    - Systems development, actual production code, web development, ..., Python can do it.\n",
      "- Performance?\n",
      "    - Python has overcome this stigma.\n",
      "    - Python is less a glue between system components or libraries, and more a glue between high performance methods.\n",
      "        - LLVM, multi-core, GPUs, clusters."
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