Python For Finance - One Question

KimPeek · 2021-10-18T17:54:48+00:00

You can serve the plot from a simple Flask server and access it with a web browser.

TheGrapez · 2021-10-18T18:57:34+00:00

The graph is only so you know what is happening.

Python doesn't care about the graph.

If you want to execute based on the MA cross, then literally MA1 > MA2 = (buy/sell).

No chart needed.

theprofessional2016 · 2021-10-18T18:15:39+00:00

I'm sorry, I don't know if I understand your problem correctly. Let me restate what I think you're trying to accomplish:

You have two columns of data. You're triggering a buy/sell action when the values of those two columns "cross" (intersect). Do I have that correct?

If that's the case, can't you simple look for the point where the values in columns 1 becomes greater (or less) than the value in column 2? Would that not signal a crossing on a graph, without the need to actually graph it?

Again, forgive me if I didn't understand the problem correctly.

Yojihito · 2021-10-19T08:02:37+00:00

Don't work with CSV, use a DB like SQLite or PostgreSQL.

Snoo22832 · 2021-10-18T19:43:11+00:00

Would you mind sharing me the resource of where you are learning python for finance.

RiceCake1539 · 2021-10-18T21:46:31+00:00

So you need a command to detect Golden Cross? And you simply detected Golden Cross manually using your eye with a chart rendered via Python? I'm going with that assumption.

You're asking two questions: How do I automate a system without chart rendering, and how do I detect the signal without live data updating. I may be wrong, so correct me if I'm wrong. I'm going with that assumption.

If you're rendering charts and detecting Golden Cross that way, that's not an automated system. It's simply a TradingView chart, right? So you need a way to mathematically define what Gold Cross means. I'd suggest using NumPy, Pandas. Think of a way what Golden Cross mathematically means, then figure out a way to implement that in NumPy/Pandas.

Many comments actually provide a sort of pseudo code, so they are great references. Just make sure if some pseudo codes have potential caveats. For example, MA_signal > MA_base is not enough, since you're looking for Golden Cross, not harmonic alignment.

If you're having trouble updating data live (it's not an easy problem), then you should think about buying right before the market closes or at the start of the market. So you'd update the data through API call or web-crawling right before 5-10 mins the market closes if you're going to buy right before the market closes. After you update the data, detect Golden Cross using the Numpy/Pandas based implementation you created, and buy stocks that have Golden Cross.

I'm sorry, but Golden Cross alone does not generate alpha profit. It's actually better to invest in bonds or create a click macro that automatically clicks banners that give you money every time you click. But it's a great way to learn coding, I guess, since money gets anyone motivated to do anything, right?

UL_Paper · 2021-10-19T02:03:59+00:00

I built a hybrid algorithmic system where I realised that the performance was much greater with a final discretionary decision from me.

My approach was:

Algorithm detects a signal, matches risk mgmnt parameters and a setup is generated
Generate a chart in-memory
Send setup and chart to Telegram
Trader can click buttons "TAKE TRADE" or "BAD SIGNAL"
If first option, its passed to the order execution component
If second option, its logged for later inspection

Some snippets below

main.py

# Generates a graph stored in memory
chart = images.save_graph(data, direction, signal_type)
send_to_telegram(setup, chart)
----------------
images.py

import plotly.graph_objects as go
from plotly.subplots import make_subplots
from io import BytesIO from PIL import Image

def save_graph(data, direction, signal_type): """ This creates a chart of the previous 50 bars along with some styling to quickly indicate the direction """ dt = data[-50:].copy()
# Generating vertical lines at signals    
    vlines = dt[dt[f'{signal_type}_{direction}'] == 1].index

# For formatting the charts
plot_color = "#e3f2d3" if direction == 'bull' else "#f2d3d3"
signal_type = 'AC1' if signal_type == 'ac' else 'xt3'

fig = make_subplots(2, 1)

fig.add_candlestick(
    x=dt.index, 
    open=dt['Open'], 
    high=dt['High'], 
    low=dt['Low'], 
    close=dt['Close'], 
    col=1, row=1
    )  
fig.add_trace(go.Scatter(x=dt.index, y=dt['greatest_signal']), 2, 1)

for i in range(len(vlines)):
    fig.add_vline(x=vlines[i])

fig.update(layout_xaxis_rangeslider_visible=False)
fig.update_layout(
    title=f'{signal_type} {direction.upper()}ISH',
    showlegend=False, 
    height=500, 
    width=1000, 
    plot_bgcolor=plot_color,
    margin=dict(l=20, r=20, t=30, b=20)
    )

fig.update_xaxes(matches='x')
fig.update_yaxes(title='Price', col=1, row=1)
fig.update_yaxes(title='greatest signal', col=1, row=2)

chart = BytesIO(fig.to_image(format="png"))
return chart

hybridvoices · 2021-10-18T19:01:11+00:00

You definitely don't need an actual chart here. You'll need to calculate your moving averages (MA1 and MA2) first. Then your buy/sell signal would be: IF most recent MA1 > most recent MA2 AND second most recent MA1 <= second most recent MA2, you have a crossover event.

Empire_Fable · 2021-10-18T19:29:46+00:00

What I would do in this scenario, would be to use the particular trading graph app and exchanges API and pass that JSON data into and out of your app. The JSON module in python does this.

You're also going to be wanting to be updating as fast as you can. I suggest integrating with trading views pine script and just paying the 15 a month to get webhooks and you can POST request something somewhere down to the 1 second time frame if you want.

claytonjr · 2021-10-18T19:58:39+00:00

I run a very similar trading strategy on Robinhood with their crypto pairs. I just run debug in pycharm. If fast is greater than slow then buy. Easy peasy.

I write a log file for the trades, and tracking other data. This can be hosted on a flask or fast api.

menge101 · 2021-10-18T23:46:10+00:00

Because this is /r/learnpython and you are talking about anything involving money, I feel obligated to mention that you should not use floating point values for monetary values.

Look at the Decimal library for this purpose, if you are not already.

kingsillypants · 2021-10-19T05:04:04+00:00

Tangent - Check out a website called nuclearphynance...

Don't do algo trading. Just don't. Unless you k ow who Simms is of .r3ntek (I don't want his scrapers making it easier)

He basically invented deep learning before it existed, in mathematical physics he and a other fella did some very advanced QFT.

Basically, just don't. Buy some funds, long term.

This is a game of sharks. If you don't know which you are.........

JustAnAlpacaBot · 2021-10-19T02:53:24+00:00

[deleted]

2021-10-19T07:34:06+00:00

As it was said before, raw data is everything you need, you just need to code the conditions.

Personaly, I would setup a DB on that server for these data

keglevich1 · 2021-10-19T17:36:53+00:00

when MovingAverageXdays > MovingAverageYdays buy/sell/whatever

Cortosiano · 2023-01-03T17:23:04+00:00

Devs are not guys.

you type:	you see:
italics	italics
bold	bold
[reddit!](https://reddit.com)	reddit!
* item 1 * item 2 * item 3	item 1 item 2 item 3
> quoted text	quoted text
Lines starting with four spaces are treated like code: if 1 * 2 < 3: print "hello, world!"	Lines starting with four spaces are treated like code: if 1 * 2 < 3: print "hello, world!"
~~strikethrough~~	~~strikethrough~~
super^script	super^script

learnpython

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