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Very nice. I might replace my own personal framework with this - it's certainly cleaner. A suggestion I'd make. Rather than simply taking a price series (which

Very nice. I might replace my own personal framework with this - it's certainly cleaner. A suggestion I'd make. Rather than simply taking a price series (which seems to be based on daily data), it might be useful to build based on the open/close/high/low series. Then allow the trades to happen at a random price between high/low rather than simply the close. I find this more reflective of the uncertainty in trading since there is no reliable way to actually trade at the close price.

jcrubino 12y ago
Handling tick data even could help and be helped in the case you outlines above.
- Emsu 12y ago
  I actually really want to support it. If only I could get my hands on some tick data... any ideas?
  - radikalus 12y ago
    I'll ping you on gh
  - findjashua 12y ago
    You can get 1-minute price bars from Yahoo Finance: http://finance.yahoo.com/_td_charts_api/resource/charts;comparisonTickers=;events=div%7Csplit%7Cearn;gmtz=-8;indicators=quote%7Csma~20%7Csma~40%7Csma~60%7Csma~80%7Csma~5;period1=1414803576;period2=1416701176;queryString=%7B%22s%22%3A%22yhoo%22%7D;range=1d;rangeSelected=undefined;ticker=YHOO;useMock=false?crumb=riKDDTwiAj5 http://finance.yahoo.com/_td_charts_api/resource/charts;comp...
minimax 12y ago
Why do you say you can't reliably get the closing price? You should be able to send an MOC order to the listing exchange to get the official closing price.
- yummyfajitas 12y ago
  To be more precise, you can't reliably get the closing price and also know what it is before you get it. If I understand the code right, it looks at the closing price and then decides if it wants to buy. https://github.com/Emsu/prophet/blob/master/prophet/backtest.py#L72 https://github.com/Emsu/prophet/blob/master/prophet/backtest... Look at lines 72 and 79. The data in the examples comes from YahooCloseData in data.py.
Emsu 12y ago
If you look at: https://github.com/Emsu/prophet/blob/master/prophet/data.py https://github.com/Emsu/prophet/blob/master/prophet/data.py There's a PandasDataGenerator which is a wrapper around the pandas libary's DataReader. For the Yahoo source, you can have the following data options: "Open", "High", "Low", "Close", "Volume", "Adj Close". The YahooCloseData generator actually uses "Adj Close". You can implement high, low or open by copying the YahooCloseData generator and getting the respective key instead of "Adj Close". Feel free to contribute these generators upstream too :) If you're looking for modeling out the uncertainty, see the slippage section in: http://prophet.michaelsu.io/en/latest/advanced.html#slippage-commissions http://prophet.michaelsu.io/en/latest/advanced.html#slippage... If you have a dataset that provides more frequent than daily data, you can store the sell order on your order generator and process it the next tick. That combined with slippage and commission will probably give you the most accurate trading model. Let me know if you have any other thoughts on how it can be better modeled.
- lrm242 12y ago
  Please don't use Adjusted Close for backtesting execution. Adjusted prices are not real. They never happened, and using them will add unknown error to your backtest results. Back adjusted prices are fine for building a model, but when back testing the model and simulating executions, you should always use real prices.
  - Emsu 12y ago
    You're right. I really need to bite the bullet and generate stock split data. That was the main reason I used adjusted because splits were skewing my tests a lot.

jcrubino 12y ago

Handling tick data even could help and be helped in the case you outlines above.

Emsu 12y ago
I actually really want to support it. If only I could get my hands on some tick data... any ideas?
- radikalus 12y ago
  I'll ping you on gh
- findjashua 12y ago
  You can get 1-minute price bars from Yahoo Finance: http://finance.yahoo.com/_td_charts_api/resource/charts;comparisonTickers=;events=div%7Csplit%7Cearn;gmtz=-8;indicators=quote%7Csma~20%7Csma~40%7Csma~60%7Csma~80%7Csma~5;period1=1414803576;period2=1416701176;queryString=%7B%22s%22%3A%22yhoo%22%7D;range=1d;rangeSelected=undefined;ticker=YHOO;useMock=false?crumb=riKDDTwiAj5 http://finance.yahoo.com/_td_charts_api/resource/charts;comp...

minimax 12y ago

Why do you say you can't reliably get the closing price? You should be able to send an MOC order to the listing exchange to get the official closing price.

yummyfajitas 12y ago
To be more precise, you can't reliably get the closing price and also know what it is before you get it. If I understand the code right, it looks at the closing price and then decides if it wants to buy. https://github.com/Emsu/prophet/blob/master/prophet/backtest.py#L72 https://github.com/Emsu/prophet/blob/master/prophet/backtest... Look at lines 72 and 79. The data in the examples comes from YahooCloseData in data.py.

Emsu 12y ago

If you look at: https://github.com/Emsu/prophet/blob/master/prophet/data.py https://github.com/Emsu/prophet/blob/master/prophet/data.py There's a PandasDataGenerator which is a wrapper around the pandas libary's DataReader. For the Yahoo source, you can have the following data options: "Open", "High", "Low", "Close", "Volume", "Adj Close". The YahooCloseData generator actually uses "Adj Close". You can implement high, low or open by copying the YahooCloseData generator and getting the respective key instead of "Adj Close". Feel free to contribute these generators upstream too :) If you're looking for modeling out the uncertainty, see the slippage section in: http://prophet.michaelsu.io/en/latest/advanced.html#slippage-commissions http://prophet.michaelsu.io/en/latest/advanced.html#slippage... If you have a dataset that provides more frequent than daily data, you can store the sell order on your order generator and process it the next tick. That combined with slippage and commission will probably give you the most accurate trading model. Let me know if you have any other thoughts on how it can be better modeled.

lrm242 12y ago
Please don't use Adjusted Close for backtesting execution. Adjusted prices are not real. They never happened, and using them will add unknown error to your backtest results. Back adjusted prices are fine for building a model, but when back testing the model and simulating executions, you should always use real prices.
- Emsu 12y ago
  You're right. I really need to bite the bullet and generate stock split data. That was the main reason I used adjusted because splits were skewing my tests a lot.