https://preview.redd.it/p3gbsu4bgdw61.png?width=1902&format=png&auto=webp&s=75afe9c98b7f4f5b295cde9fd9f4c26be118de73 Final Edit: WAtch in real time as this post gets down voted by SchillBots...Unbelieveable! I've been watching this like it was a ticker for 16 hours! https://www.youtube.com/watch?v=Uui3Pe8Fshc&t=62s&ab_channel=Docta%27StockTrading

Edit 9: APES =dont let them downvote this...it is under heavy attack and all it is, is information... I have been curious to see what the Premium version of TV would get me, so I bought it. Mainly because I've been wanting to see my charts at the 1 second interval. First stock I've looked at on an interval less than 1 minute in over 15 years, and BOOM, one look at AMC's last hour shows everything about the machine trading going on.

The question is when US, the retail investors begin holding these institutions fully accountable and forcing them to end the robbery that is Trade Date + 2. The DTCC is responsible for allowing this, and they should be corrected immediately. This is the biggest financial entity in the world. It is going to take every. single. ape. to make the change we need to have safe markets to invest in. This is a call to action apes. Every Ape should be PISSED!

Edit: TLDR = Not a Dead Cat!!

Edit 2: Some Apes requested comparisons. I'll give several... One's highly shorted, then ones that are more bluechips...

Blue Chips requested:

AAPL

Not so much here on a normal moving stock

Chevron:

https://preview.redd.it/jiopx69ardw61.png?width=1920&format=png&auto=webp&s=ddc9311a7b03c0510ac7f6e74e41f692d56dbc57

Now for some "MEME" stonks:

$CLOV

https://preview.redd.it/8z8fnbwerdw61.png?width=1920&format=png&auto=webp&s=6c2243b9299ef9071afd11c94312dfd27a54cf44

MVIS:

Not so much with Mavis, due to so much volume

EBON

https://preview.redd.it/5mll30virdw61.png?width=1920&format=png&auto=webp&s=760ac7f30ead0ec90f34d0ba804ce5ed2806ba58

SENS

https://preview.redd.it/59dnfmakrdw61.png?width=1920&format=png&auto=webp&s=0e2b443f508327267571d28f6f511b0a2c63d296

SOS:

https://preview.redd.it/5enuytmlrdw61.png?width=1920&format=png&auto=webp&s=64d65180c69

Hey r/wallstreetbets, I've been working on a stock trading algorithm these past couple months. My interest in trading began this January and since I'm lazy as shit and I know how to code, I decided to code myself something that would trade for me.

For this project, I used Python and the TD Ameritrade API. I will begin by saying that the TD Ameritrade API is absolute garbage and you should use something else if you want to try something like this.

The code for TradeAlgo can be found here: https://github.com/4pz/TradeAlgo

TradeAlgo uses web scraping to pull a list of stocks which are predicted to rise already. After the list is scraped, each symbol is then checked to validate if they match the parameters set in the code. (These parameters are created by me after extensive research on how to predict a rising stock)

After this, the total balance of your TD Ameritrade account is pulled using the TD Ameritrade API and your total balance is split among the stocks which matched the set parameters. You can change how much money from your account is allocated to be used with the algorithm by changing the balance variable to the desired amount.

Finally, the buy function is called to execute all orders with a trailing stop loss to ensure minimal losses.

I've also included a way to only see a list of recommended stocks without actually buying them so if you want to make your own educated decisions after seeing what TradeAlgo advises, you can do that.

Make sure to check out the repositories ReadMe for detailed setup and usage instructions!

If you have a GitHub account and can star the repository, I'd appreciate it.

Repository Link

How TradeAlgo Should Look if All is Done Properly

Hey everyone, I have extra copies of my book "Algorithmic Trading with Python" lying around. I am going to give 5 of them away at random to 5 people that comment on this post.

At 5pm New York time Monday, April 26th, 2021 I'll run the following script to select the winners. All you have to do is leave one comment to be entered to win. Everyone that leaves at least one comment will have an equal chance of winning.

If you win, I'll ask you for your mailing address to send you a physical copy of the book. I can't give away any digital copies. I can only mail to addresses within the U.S. So, if you can't receive the book at a U.S. address, please refrain from entering.

Here's an Amazon link to the book: https://www.amazon.com/Algorithmic-Trading-Python-Quantitative-Development/dp/B086Y6H6YG/#ace-2342880709

I did this back in September on this sub and it was a big success. Publishers tend to send you lots of free copies of your book, so I am happy that I have this method for getting rid of them.

Here's the Python script I will run to select the winners.

# Selecting the winners ...
import praw
import random
random.seed(1234)
reddit_credentials = {
    'client_id': 'xxxxxx',
    'client_secret': 'xxxxxx',
    'user_agent': 'xxxxxx',
}
reddit_client = praw.Reddit(**reddit_credentials)

submission_id = 'xxxxxx'
submission = reddit_client.submission(id=submission_id)
submitters = [comment.author for comment in submission.comments.list()]
submitters = [author.name for author in submitters if author]
submitters = list(set(submitters))
submitters.sort()

winners = random.choices(submitters, k=5)
print(winners)

BTW, if this post is removed for any reason, the giveaway will be canceled, since I would have no way to select the winners.

For the past year, I have been building and testing crypto trading bots, pulling reports and analysing their performance. During my testing, I have trialled different strategies, with various degrees of success and I think that can provide a brief overview of which strategies tends to work and which ones don’t (in my experience anyway).

Machine Learning Bots

These are bots that typically ingest vast amounts of historical data on coin, in order to calculate a predicted price. I used an LSTM model in order to try and predict the price of the top 10 coins. After multiple tweaks and optimisation, I decided that a prediction 4 hours in the future is the most accurate one. Even so, the machine learning bot only got 50% of the trades right, meaning that it performed as well deciding how to trade based on a coin toss.

The issue with machine learning algorithms in finance is that, if they are only ingesting financial information, they remain ultimately blind to all the external factors which contribute to sharp fluctuations in price. Yes, machine learning can detect patterns in the price change with great accuracy if you feed it a big enough dataset, but it cannot account for anything outside these numbers: news, regulations, holidays, behavioural patterns etc..

In essence, machine learning bot that only works on financial data will always be at the mercy of whatever else is happening outside the historical dataset. A more robust algorithm would take into account news, major crypto events and behavioural patterns, as well as the historical data.

Technical Indicator Bots

This category is looking at financial indicators and technical analysis in order to make a short or long term decision on a cryptocurrency. Some of the technical indicators that I tried are: MFI, RSI and Awesome Oscillator. My conclusion is that technical indicator bots have a higher rate of success than a simple machine learning bot, but they are not without faults.

Taking the example of the RSI indicator - or the Relative Strength Index, this is supposed to show you much much momentum is there on a market from a scale of 0 to 100. A value over 70 is considered a strong bullish movement while a value under 30 is typically a bearish sign. The use I used this indicator is once the value goes over 70 and then back under again, let the bot place a sell position. If the value goes under 30 and back over, buy instead.

As you might expect this isn't exact and many times the bot registered fals

Its native token, ADA, is now the seventh-largest cryptocurrency by market capitalization. The launch will be the first time a stablecoin launches on the top-blockchain network.

The stablecoin is the result of a collaboration between Input-Output Global, Cardano’s parent company, blockchain-solutions provider Emurgo, and the Ergo Foundation, the stabelcoin’s principle developers.

Soure: https://beincrypto.com/cardano-sees-first-ever-stablecoin-launched-on-network/

Welcome to the Matrix, bitch!

THE WEDGE (January ,March's and April peaks downtrend + February 26th, March 24th, April 13th and 23rd, May 4th 's dips uptrend)

Price-outdated-simplified-Macro-chart that I'm too lazy to recreate (trends are valid)

My system as of early march (outdated, I didn't even see the MEGA WEDGE at that point, it was too big for my imagination) :

\"THE CODE\"

Uptrends:

THE DFV, first spike of January's madness, perfect parallel to february's 39$ uptrend

The Launchpad

One of the later ones, didn't find a cool name for it ¯\_(ツ)_/¯

The most critical uptrend I call "the Rocket" (too lazy to screenshot it), it spawns from time to time, you can find it from March 5th to 10th, March 24th to 26th. Smaller occurences are April 14th, 16th to 19th (w-e), and 26th.

DOWNTRENDS :

The Melvin, one of the major downtrends, this one is literally everywhere since January 15th (and before)

The Shitadel. Critical everyday-downtrend, you can find it connecting the peaks of January and February 24th, for example.

Yep. Visual translation of the short-ladder algorithm. It's easy, just set it where the price is at market open and you're good to go. Macro below from my previous post :

[April 14th to 23rd](https://preview.redd.it/ahx0mx9akpx61.png

I think that's all that needs to be said. Happy to answer any questions below!

We did it guys :)

How to create an algorithmic trading bot with Python

1. Overview - An overview of the project.
2. Design - Requirements and how the trader operates.
3. Getting financial data into Python - Pulling financial data into Python from MetaTrader5.
4. Open a trade using the MT5 API with Python - How to open a trade programmatically via MetaTrader 5.
5. Close a trade with MT5 using Python - How to close an open trade with MetaTrader 5.
6. Creating an algotrader/trading bot with Python – Part 1 - Creating the trading bot loop and opening trades with an entry strategy.
7. Creating an algotrader/trading bot with Python – Part 2 - Implementing a strategy reader.
8. Creating an algotrader/trading bot with Python – Part 3 - Closing a trade with an exit strategy.
9. Creating a strategy for your algorithmic trading bot – Part 1 - Creating a dynamic strategy with JSON for trading part 1.
10. Creating a strategy for your algorithmic trading bot – Part 2 - Creating a dynamic strategy with JSON for trading part 2.
11. Dynamically calculate lot size for your algorithmic trading bot - Dynamically calculate your position size based on account size and risk.
12. Send messages from Python to Slack - Sending open trade/close trade alerts to slack.
13. Send an email from Python - Sending open trade/close trade alerts via email.
14. Trade management for the [algorithmic trading bot](https://www.conorjohanlon.com/trade-management-

https://hub.docker.com/r/shobuprime/dab

Because why not? Self-host all the things.

Original Author: Vitling --> http://www.vitling.com/toys/acid-banger/

I am a manual trader - not entirely discretionary, as my strategy remains systematic but contains a degree of discretion. While not yet a successful trader for a variety of reasons, I don't think discretionary trading is flat out unprofitable because I have seen enough track records from people to suggest it is doable. However, as you probably know already if you actively trade, being in the phase where you're still trying to figure out what and how to trade comes with a lot of uncertainty and frequent bouncing around (different assets, different strategies, and so on).

I do think you can help me solve one of the doubts I have always had, though, so I'd appreciate to hear your opinion on a well-accepted (it seems by conversation with algo traders) fact - which is that you cannot create an algorithmic strategy that will work forever, and that these kind of strategies are often replaced. The work of an algo trader is one of constant research and development of new strategies, and the monitoring of those in their portfolio of algos to quickly remove what no longer works.

This is something I accepted as a fact, and the agreed upon principle is that the main advantage of not being an algo trader is that you can adapt to changing market conditions and trade the same strategy forever. This is what discretionary (or even semi-discretionary) traders say and believe, and it is what brought me to this style in the first place.

However, I have never understood why. What makes it so that a price action strategy is evergreen, while algos just stop working after a while?

I did some research, I spoke with some algo traders, and I think I have some ideas as for why this is and I'd like to hear what you think. Or, perhaps you believe discretionary strategies suffer from the same issue and those like me are delusional - I want to hear that side too.

The main answer I found for myself is that the driver of algo strategies is fundamentally different from what drives a price action strategy. An example would be a strategy I saw around here, where a person tested the correlation between the price of graphic cards and cryptocurriencies - in this case, it is easy to see why this would not work forever. It is not scalable and it will probably not continue forever.

Thanks to all who have helped with my Scheme questions in the past as I've worked on Scheme for Max. It's finally making music! here's a video of an algorithmic and stochastic composition study using Scheme for Max and modular synthesizer. There will be a "making of" video shortly, just check the channel (it's rendering...)

https://youtu.be/rcLWTjN4qBI

Edit: demo of how it's made up now too here: https://youtu.be/pg7B8h4yHkU

WSB fund alongside the market

I’ve spent a large part of the last year working with data from r/wallstreetbets and want to show how to create and backtest a simple strategy using the data. With a few extra steps, the code shown here could be modified to algorithmically trade based on discussion by retail investors.

Because I figured that some of you would only be interested in the results of this strategy, you can find a few visualizations summarizing the output here.

If you're interested in hearing about the process of implementing the strategy, read on.

The Strategy

Simplicity is the goal here, as I just want to provide a framework which can be built upon as desired:

1. Get data on the previous week’s WallStreetBets discussion.
2. Identify the five most mentioned stocks.
3. Buy those stocks at the start of the trading week, sizing positions based on how much they were talked about in proportion to each other.
4. Sell positions at the end of the trading week.
5. Repeat

One thing which I do not incorporate into this strategy is any information on the sentiment of wallstreetbets towards individual stocks. While the subreddit generally tends towards long positions (“stocks only go up” is a common saying) this is something that might be worth implementing in a more sophisticated strategy.

Implementation

Getting wallstreetbets discussion data

I used the quiverquant package in Python to easily access wallstreetbets discussion data through Quiver Quantitative’s API.

I use the Institution plan of this API in order to get live comment-level data but, with a few changes to my code, you should be able to implement a similar strategy using the Trader plan.

import quiverquant
import pandas as pd

#Replace <token> with your personal token
quiver = quiverquant.quiver(<token>)

df = quiver.wallstreetbetsComments(date_from=”20180901")

Wallstreetbets discussion data

Using the above code, I am able to get a Pandas DataFrame of approximately 3.3 million rows with data on ticker mentions on wallst

Hi everyone I am a recent graduate with a B. Sc in comp sci and M. Sc in Quantitative Finance (quant). I am unsure whether to pursue a creer in algorithm trading or blockchain development (backend) I have a very short experience (internship) as a algo trader and experience with systematic strategies on cryptocurrencies. I have no practical blockchain dev experience.

Here my 2 cents so far:

B. Chain dev pros:

1. lots of career opportunities, lot of demand
2. Learning new cutting edge technologies
3. Learning thoroughly a good system programming language (eg Rust, C++), and also learning to manage large codebases
4. Plenty of startups with young and motivated people and flexible working hours and entrepreneurial culture.
5. Prerequisites (eg you can have a brilliant career even if you hold a BSc in comp sci since there isn't any formal degree on blockchain technology)
6. Have a positive impact on the world by solving real problems
7. You actually learn how to build things by coding (useful eg if you want to start your own Saas business later on)

B. Chain dev cons :

1. Cant think of any, actually. Except that I have to learn a great deal of software development : git integration, unit testing etc...

Algotrading career pros

1. Salary slightly higher than Bchain dev
2. More data-science like, more maths involved and I kinda like maths when it's not too abstract.
3. I have already brief experience, easier to get a position

Algotrading career cons:

1. Supply and demands are somewhat balanced: less career opportunities
2. Skillset is less more specific than blockchain dev. (yes you actually learn a bit of data science but you get master only a determined range of techniques such as Bayesian filtering, stationarity tests, etc...) and you don't learn how to setup ideas from inception to execution. That is the implementation is left to software developers, once you have a strategy backtested.
3. Top research positions require PhD-level education in math, stats, physics etc..
4. You don't solve any actual problem, you are just trying to move as much money as possible in your pockets
5. Very formal working environment (usually) , roles are very well defined. No entrepreneurial culture and usually no flexible working hours.
6. Job interviews tends to be slightly harder than Bchain dev

With that said I am just a rookie in the job market game, so I was looking for someone that can criticise my analysis and provide his/her experience.

Thanks in advance

Hey all,

I'm VERY brand new at this. I've been taking a Udemy 100 Days of Code course, and I'm on day 3. I'm really enjoying myself and the problemsolving aspect of this hobby, but I need a little reassurance.

As I go through and practice the coding challenges, I can always get them to work - if not the first time, then I'm able to debug effectively. However, when I compare my code with the instructor's code (Dr. Angela Yu, in this case), her code is so much simpler, more elegant, and has far fewer steps than mine. I assume that this is something that comes from practice and experience, but I'm hoping you all can share your thoughts.

TLDR: Will it ever get easier, thinking algorithmically and simplifying code from many tiny steps into fewer complex steps? Head says yes, duh; heart says but what if not?

I've been contemplating algorithmic trading, but now that I've looked into short-term capital gains tax, it seems like it can only be profitable/viable at an institutional level. I'm probably incorrect about this, and would like to ask a few questions:

1. For solo algo traders, how do you manage short-term capital gains taxation? How much does it reduce your earnings/amplify your losses?
2. If short-term algo trading (perhaps selling programmatically about 10-100+ times a day) is not viable for retail trading, do you build strategies for long term investment (how do you do so; what time-frame are they applied to)? Tangentially: do you leverage fundamental or technical analysis more for longer term algo?

I may have some more questions later on, but still doing research. If you have any resources related to taxation, please send some. Thanks.

Edit: I believe capital gains was not the proper term to use

For some time, I've been putting together code that encapsulates objective rules around stock selection and risk as well as quantifiable phenomena as it relates to price action and auction theory. I'm happy to say that I believe I'm closing in on something profitable and perhaps even useful to the community in the form of swing trade ideas. One of the hardest things about trading, even if you know how to trade, is figuring out what to trade.

Right now my bot monitors large caps between $25-$85 which seems like a good starting point for both options traders and those who trade commons. It then scores bullish and bearish aggression, applies a few conditional checks, and initiates a trade. Backtests over the past year of playing long and short side show an aggregate win Pctg of about 41%, max drawdown: $-143.96, max winner: $631.40, avg hold time: 14.69 hrs, avg profit per winner: $22.41, avg profit per loser: $-4.42

Today it took starter long positions in $BP, $AJG, $ICE, $FTV , $CTAS and a starter short position in $CE.

https://preview.redd.it/yripoluc3uv61.png?width=1512&format=png&auto=webp&s=e8584538b6b4bedbdbb055dadda6564548072112

Not sure if the sub has an appetite for trade ideas that it generates....I guess we'll see how these pan-out over the next several days lol. I've been closely watching u/tony_letigre 's posts over in r/Daytrading and thought this might be a proper home for a swing-equivalent. Let me know what you think!

Inputs (full notebook linked at bottom)

Single color perspectives

Friend color pair perspectives

Enemy color pair perspectives

Full notebook:

https://github.com/jasonsackett/color-pie/blob/main/MTG-5color-alignments.ipynb

Any corrections or improvements welcome.

Thanks

I believe that if we create too many backlinks on exact match keywords, it may bring ranking down instead of boosting up irrespective of how relevant and authoritative your backlink is.

Am I right?

And also please let me know what is the definition of too many backlinks. Can you define in number?

Here is something interesting that y'all might be interested in. Im am using volume and sentiment data from Sentiment Investor (sentimentinvestor.com) and I've been working on a few different trading strategies, and there is one that I need feedback on.

To identify long term holds after a stock has a violent increased. I've noticed that after stocks get pumped and then pop and fall down they often drift back up slower than before but still fairly fast.

I built a model which computes the second derivative of volume which I'll call V and time. It tries to look for d^2V/dt^2 = 0; and it does this by figuring out the variance in the second derivative and as this stabilises to a certain level it places a trade. The reason I am looking at the second derivative (and why it "should" work) is because I am trying to catch the change before it happens at all.

This has actually worked fairly well and I have made a lot of successful trades but also a lot of duds.

I am not sure how to eliminate duds. One idea I have had is to look at higher order derivative but I am not sure why this would work. I have also thought about increasing the tolerance to variance but I am also losing out on a lot of good options here.

I also am trying to do a similar thing with second derivative on reddit stocks before they shoot up by looking at second derivatives. This again leaves with the same problems. I am trying to apply to the principle of tolerance to the volume data they provide but this does not always work i.e. below with MVIS there are spikes before the 26th which is when the stock shot up but I can't find a good way to pair the right spike with the right increase.

Again tolerance doesn't really work because the spikes are too large

MVIS Volume Data from sentimentinvestor.com

Having said this the algorithms aren't terrible but I's be a lot happier if I could find a way to cut out the noise.

Any comments or suggestions/help is much appreciated.

Hi there,

Not american and not a right-winger, but genuinely interested in engaging with right-wing ideas at their face value. Even before the Trump twitter ban, the american right seems to have included big tech corporations owning social media platforms to the list of "biased liberal media". I am writing a paper on algorhitmic bias where I aim to evaluate cases of algorithms discriminating against certain groups - and I want to evaluate the conservative claim. I am trying to make the best case for it, but am struggling to find any peer-reviewed research or empircal data that would support the right's thesis that you guys are getting censored any more than anyone else. In fact, I'm mostly finding research like this: https://static1.squarespace.com/static/5b6df958f8370af3217d4178/t/6011e68dec2c7013d3caf3cb/1611785871154/NYU+False+Accusation+report_FINAL.pdf concluding that "the claim of anti-conservative animus on the part of social media companies is itself a form of disinformation: a falsehood with no reliable evidence to support it".

In case my algorithm is fooling me and ironically not showing me evidence that does exist, I ask you to point me to any grounds on which you could base the claim on? Thank you!

It says to try again after 05:00... does it say that for everybody regardless of your location and it means 05:00 UTC, or is it specifically 05:00 in my time zone? I was glued to my screen so I wouldn't miss it, but it didn't appear to reset for even a second so I thought perhaps I misunderstood what time it actually happens. Either that or demand is so crazy high that people are somehow using bots to max out the pool the very second it resets haha

19 and recently started to learn about algorithmic trading and wanted to get a bit serious about it.

I wanted to know is there any api I could use to get data about certain stocks. I know there is heavy fee for real time data but what about previous days data ?

I have tried alpha vantage, it doesnt seem to work for Indian stockmarket (atleast not for me).

Currently I dont plan on using real money.

Any suggestion/question is welcomed.

Hello @ all,

I have been experimenting with new algorithmic composition techniques in the last two months in my spare time as a hobby, and I am happy to release my first album:

https://musescore1983.bandcamp.com/album/algorithmic-composition

The score of the first composition can be found here:

https://musescore.com/user/37663311/scores/6713263

(As a side note: The default synthesizer in the browser produces a terrible sound, so please if you use the link of musescore change the audio, but I suggest for listening to use the bandcamp version.)

On request, here are the other compositions:

track 2: https://musescore.com/user/37663311/scores/6711446

track 3: https://musescore.com/user/37663311/scores/6709819

track 4: https://musescore.com/user/37663311/scores/6710208

track 5: https://musescore.com/user/37663311/scores/6713562

Thanks for your help so far!

Kind regards from Limburg in Germany!

1. Create smart contract # 1 that accepts IOTA and issues IOTA Dollar at a price, for example, $ 1.02,

IOTA price is determined by the price taken from the api iota price FireFly.

Thus, anyone can exchange IOTA (in any volume) for IOTA Dollar, without using a centralized
exchange.

2. Create smart contract # 2. This smart contract works in cycles (for example, 1 minute, so that the
   price does not change much).

Determines the price taken from the api iota price FireFly and sets the exchange rate, for example 1
IOTA Dollar = 0.52 IOTA.

This price is valid for 1 cycle (1 minute), during this time anyone (both seller and buyer)

can send either IOTA Dollar or IOTA to a given smart contract and make an exchange.

The IOTA Dollar price is always $ 1.00. (for both the seller and the buyer).

The price is $ 0.02 lower than that of smart contract # 1 to stimulate the exchange through smart
contract # 2.

3. Smart contract upon receipt of IOTA Dollar or IOTA assigns queue numbers to sellers and buyers.

   Within 1 cycle (1 minute) smart contract will aggregate all sellers and all buyers,

   assigns a queue number and makes an exchange (based on liquidity) by sending an IOTA Dollar or
   IOTA to the addresses of sellers and buyers.

   If there is not enough liquidity, or excess liquidity remains, after the end of the cycle, all IOTA Dollars
   or IOTAs are returned to the owners.

4. New cycle.

5. IOTA from smart contract # 1 can go to pay for smart contracts, add liquidity to smart contract # 2,
   or to the treasury of the IOTA community.

Like the title says, I've looked all over and its quite expensive to just buy while I'm just starting out. Someone might have it around here for free.

I have pretty solid Rhino skills, but want to take it to the next level with parametric designs. I've started the basic course on LinkedIn Learning (Lynda) but it's moving too fast for me. Any recommendations on a good course that introduces the concept of algorithmic modelling at the same time as teaching me Grasshopper?

I am experimenting with mathematical techniques to create sheet based music, and have searched but found little around composers who use math/algo techniques to compose music.

Do you use maybe this style of composition and what techniques do you use and how do they sound?

Thanks for your help.

(If you are curious, here is a piece for 5 pianos I have written in Python: https://musescore.com/user/37663311/scores/6655514

Two pieces in youtube: https://www.youtube.com/watch?v=HabMjOkAlSA&list=PLfnNHqLVzcnJLsyrBLQV507vH31ID4fW6

)

I've recently started trading stocks as an alternative to holding money in the bank, and I've been curious about automatic trading, but the question is so vague and broad, I couldn't formulate a proper google search request.

What is stopping someone from writing algorithms that scan the market every day for stocks that perform well, then sell and repeat? For example yesterday some pharma company got approved for some medical device, their stock doubled upon market opening. What is stopping an algorithm that detects sudden % change to buy the stock faster, at a lower price, than a human would? Does that mean that automatic trading always wins in such cases? Can you even make such an algorithm that scans the market as a whole?

A slightly broader question - in general, unless we are talking about insider information and "pump and dump" schemes, how do people find out which stock might suddenly shift in the price? Is it just a case of reading the news, researching, understanding, and anticipating the market, knowing what is happening in different sectors of the economy? Or it's all about insider trading/gray zones and algorithms?

Hey r/algotrading, I've been working on a stock trading algorithm these past couple months. My interest in trading began this January and since I'm lazy as shit and I know how to code, I decided to code myself something that would trade for me.

For this project, I used Python and the TD Ameritrade API. I will begin by saying that the TD Ameritrade API is absolute garbage and you should use something else if you want to try something like this.

The code for TradeAlgo can be found here: https://github.com/4pz/TradeAlgo

TradeAlgo uses web scraping to pull a list of stocks which are predicted to rise already. After the list is scraped, each symbol is then checked to validate if they match the parameters set in the code. (These parameters are created by me after extensive research on how to predict a rising stock)

After this, the total balance of your TD Ameritrade account is pulled using the TD Ameritrade API and your total balance is split among the stocks which matched the set parameters. You can change how much money from your account is allocated to be used with the algorithm by changing the balance variable to the desired amount.

Finally, the buy function is called to execute all orders with a trailing stop loss to ensure minimal losses.

I've also included a way to only see a list of recommended stocks without actually buying them so if you want to make your own educated decisions after seeing what TradeAlgo advises, you can do that.

Make sure to check out the repositories ReadMe for detailed setup and usage instructions!

If you have a GitHub account and can star the repository, I'd appreciate it.

Repository Link

How TradeAlgo Should Look if All is Done Properly

Its native token, ADA, is now the seventh-largest cryptocurrency by market capitalization. The launch will be the first time a stablecoin launches on the top-blockchain network.

The stablecoin is the result of a collaboration between Input-Output Global, Cardano’s parent company, blockchain-solutions provider Emurgo, and the Ergo Foundation, the stabelcoin’s principle developers.

You may downvote me to oblivion as I know how many of you feel about Cardano, but I wanted to share news which is essentially good for the whole space.

How to create an algorithmic trading bot with Python

Overview - An overview of the project.

1. Design - Requirements and how the trader operates.
2. Getting financial data into Python - Pulling financial data into Python from MetaTrader5.
3. Open a trade using the MT5 API with Python - How to open a trade programmatically via MetaTrader 5.
4. Close a trade with MT5 using Python - How to close an open trade with MetaTrader 5.
5. Creating an algotrader/trading bot with Python – Part 1 - Creating the trading bot loop and opening trades with an entry strategy.
6. Creating an algotrader/trading bot with Python – Part 2 - Implementing a strategy reader.
7. Creating an algotrader/trading bot with Python – Part 3 - Closing a trade with an exit strategy.
8. Creating a strategy for your algorithmic trading bot – Part 1 - Creating a dynamic strategy with JSON for trading part 1.
9. Creating a strategy for your algorithmic trading bot – Part 2 - Creating a dynamic strategy with JSON for trading part 2.
10. Dynamically calculate lot size for your algorithmic trading bot - Dynamically calculate your position size based on account size and risk.
11. Send messages from Python to Slack - Sending open trade/close trade alerts to slack.
12. Send an email from Python - Sending open trade/close trade alerts via email.
13. Trade management for the [algorithmic trading bot](https://www.conorjohanlon.com/trade-management-for-

How to create an algorithmic trading bot with PythonOverview - An overview of the project.

1. Design - Requirements and how the trader operates.
2. Getting financial data into Python - Pulling financial data into Python from MetaTrader5.
3. Open a trade using the MT5 API with Python - How to open a trade programmatically via MetaTrader 5.
4. Close a trade with MT5 using Python - How to close an open trade with MetaTrader 5.
5. Creating an algotrader/trading bot with Python – Part 1 - Creating the trading bot loop and opening trades with an entry strategy.
6. Creating an algotrader/trading bot with Python – Part 2 - Implementing a strategy reader.
7. Creating an algotrader/trading bot with Python – Part 3 - Closing a trade with an exit strategy.
8. Creating a strategy for your algorithmic trading bot – Part 1 - Creating a dynamic strategy with JSON for trading part 1.
9. Creating a strategy for your algorithmic trading bot – Part 2 - Creating a dynamic strategy with JSON for trading part 2.
10. Dynamically calculate lot size for your algorithmic trading bot - Dynamically calculate your position size based on account size and risk.
11. Send messages from Python to Slack - Sending open trade/close trade alerts to slack.
12. Send an email from Python - Sending open trade/close trade alerts via email.
13. Trade management for the [algorithmic trading bot](https://www.conorjohanlon.com/trade-management-for-algo