# Functional Stock Analysis (FunStock)

## Introduction

The target is to evaluate if a language (domain specific) for stock market analysis can be designed to make it both powerful and simple to use for most users.

Inspiration comes from:

I had a few ideas when I started evaluating this topic:

- Make the language mathematical/functional rather than imperative
- Do operations on time series (as a whole) not on individual data points at a specific time
- Harmonize between indicators (line diagrams) and signals (buy/sell events)

## Status

At present it's a simple proof of concept. A set of data structures and functions has been defined in Haskell which allow simple analysis to be performed.

The following features are supported.

- A time series datatype has been defined to hold stock data, indicators and signals (
`TimeSeries.hs`

) - A set of general functions operating on time series has been defined (
`TimeSeries.hs`

) - A set of example indicators has been defined which is based on basic functionality of time series (
`StockIndicators.hs`

) - Stock data can be downloaded from google finance (
`StockIo.hs`

) - Simple diagrams can be generated as SVG (
`StockView.hs`

)

There are still a lot more to do to make this useful or complete. For instance.

- Scripts are plain Haskell code which need to be compiled (not usable by most users)
- Diagramming features are very limited
- It's a command line tool which generates a single SVG file (no UI)

But it has somewhat helped me to further understand how to design a stock analysis language.

## An example

An example diagram is generated by `FunStock.hs`

.

It does the following.

- Load share stock data from a file (
`stocks/aapl.csv`

) as time series - Calculates a 10 step moving average as an indicator
- Defines a signal which trigger when MA10 indicator passes below stock indicator
- Draws a diagram with the two indicators and the signal

The code is shown below.

```
main =
do
s <- loadShareFile "aapl"
let aapl = select Io.Close s
let ma10 = movingAvarage 10 aapl
let cross = crossesBelow ma10 aapl
let indicators = [ ("AAPL", opaque blue, aapl)
, ("MA10", opaque red, ma10)
]
let signals = [ ("MA10 cross below", cross) ]
draw "aapl.svg" indicators signals
```

The Haskell code is quite straight forward. It could be made even easier to read if a domain specific language were used, which more or less directly could map towards the Haskell implementation.

In such case the syntax could look like the following.

```
aapl = load("aapl").close
ma10 = movingAvarage(10, aapl)
cross = crossesBelow(ma10, aapl)
indicators = [ ("AAPL", blue, aapl), ("MA10", red, ma10) ]
signals = [ ("MA10 cross below", cross) ]
draw "aapl.svg" indicators signals
```

The idea is that it shall be relatively simple to extend with new functions like `movingAvarage`

and `crossesBelow`

.

For instance `movingAvarage`

(in `StockIndicators.hs`

) is quite simple.

```
mean :: (Floating a) => [a] -> a
mean xs = sum xs / len xs
-- Calculate moving average for a time series over n number of data points
movingAvarage :: (Floating a) => Int -> TimeSeries a -> TimeSeries a
movingAvarage n ts = TS.map mean $ slidingWindow n ts
```

It makes use of `slidingWindow`

(in `TimeSeries.hs`

) which convert a single valued time series to a multi valued time series of a specified window length.

`movingAvarage`

is done two steps.

First, the time series is fed through the `slidingWindow`

function.

```
slidingWindow n ts
```

Then the `mean`

function is "mapped over" the "windowed" time series.

```
TS.map mean $ ...
```

It turns out that most simple indicators (i.e. indicators that only depend on "current" value) can be implemented through a simple `map`

function.

```
-- Map for time series
map :: (a -> b) -> (TimeSeries a) -> (TimeSeries b)
map f ts@(TS _ _ as) = ts { series = P.map f as } where
```

For instance `aapl + 10`

would be written as.

```
aaplPlus10 = TS.map (+10) aapl
```

Many indicators are however stateful i.e. they are dependent on previous data in the time series.

For instance in `slidingWindow`

the "current" value is a list of the previous `N`

values. This is a typical stateful operation.

```
-- Make sliding window from time series. Use a fifo as state
slidingWindow :: Int -> TimeSeries a -> TimeSeries [a]
slidingWindow n ts = smap window s0 ts where
s0 = F.mkEmpty n
window s e = let s' = push s e in (s', F.toList s')
```

To do stateful map operation there is a special `smap`

(in `TimeSeries.hs`

) which keeps a custom state through the map.

```
-- Stateful map functions for time series. It runs through series from chronologically, while
-- keeping a state through the process. Map function :: state -> elem -> (state', elem')
smap :: (s -> a -> (s, b)) -> s -> TimeSeries a -> TimeSeries b
smap f s0 ts@(TS _ _ as) = ts { series = as' } where
as' = reverse $ snd $ foldl smap' (s0, []) as
smap' (s, es') e = let (s', e') = f s e in (s', e':es')
```

`smap`

is similar to `map`

but it also permit a state to "travel" through the map.

To implement `slidingWindow`

we make use of a FIFO queue, to keep a list of the most N recent values of the input, as the state in the stateful map operation.

`crossesBelow`

(in `StockIndicators.hs`

) use a different strategy.

```
-- Determine when time series a cross from below of b from previous value to current
crossesBelow :: (Num a, Ord a) => TimeSeries a -> TimeSeries a -> Signal
crossesBelow a b = mkSignal id s where
a' = combine (,) (TS.delay 1 a) a
b' = combine (,) (TS.delay 1 b) b
s = combine crossBelow a' b'
-- Does (a_prev,a) pass (b_prev,b) from below i.e. is a_prev < b_prev && a > b ?
crossBelow :: (Num a, Ord a) => (a,a) -> (a,a) -> Bool
crossBelow (a',a) (b',b) | a' < b' && a > b = True
| otherwise = False
```

To determine if one time series passes another time series at a specified time instant (`t`

) you need to look at the current value as well as the previous value. You could do this by keeping the previous value as state in a stateful map operation, but here we use a different strategy.

Instead, we take each time series and "combines" it with a delayed (one time step) version of itself. `combine`

(in `TimeSeries.hs`

) takes two time series and combines its values by a supplied function. In this case we make a "tuple" from the two time series which creates a new time series with both current and previous value `(a_prev,a)`

as its tuple values.

`crossBelow`

is a pure function that takes two tuples (from series `a`

and `b`

) at current and previous time (`(a_prev,a)`

and `(b_prev,b)`

) to determine if `a`

passed `b`

from previous time step.

Finally `crossBelow`

is combined with the "tupled" time series to create a time series with `Bool`

values. `mkSignal`

convert a time series to a signal using a conversion function. The conversion function is in this case the identity function `id`

as the time series alread have the final `Bool`

values.

The resulting SVG diagram is shown below.

## The complete code

The whole code for the proof of concept can be found here.

funstock.tgz - All source code

## Evaluate `auto`

package as alternative

Evaluate auto package.TBD.

## References

- TradingView - a quite advanced trading platform
- d3.js - javascript diagramming framework
- TechanJS - TA built on d3
- The Essence of Dataflow Programming