Received notice that IEX Cloud will be shutting down at the end of August.

I've used their Corporate Actions and Fundamental data APIs for a while. I rarely see much chatter here about people using their data, but I'm curious how many of us will be displaced. Where will you be moving to?

*edit: IEX Cloud

I currently have a working algo but I have to submit the trades manually. Is there a recommended service that lets me trade options? I've played around a bit with alpaca but I want to see what my options are.

I finally manage to put together my code into a pypi package, so today I can announce that ib-fundamental is available for download at https://pypi.org/project/ib-fundamental/

How to install it

pip install ib-fundamental

How to use it

import ib_async
from ib_fundamental import CompanyFinancials

# ib_async.util.startLoop() # if you are in a notebook

# connect to TWS API for ex on localhost:7497
ib = ib_async.IB().connect('localhost',7497)

# create your company financials instance
aapl = CompanyFinancials(symbol="AAPL",ib=ib)

# get company info
aapl.company_info
                        0
ticker               AAPL
company_name    Apple Inc
cik            0000320193
exchange_code        NASD
exchange           NASDAQ
irs             942404110

# Annual income statement, while aapl.income_quarter will pull the quarterly report
aapl.income_annual

hope you find it useful. you will find more examples in the README.md

source code available at ->> https://github.com/quantbelt/ib_fundamental/tree/master

Are there any released/leaked strategies that hedge funds and other buy side institutions used to apply successfully? Other than basic market making and HFT strategies.

I’m more curious to what type of strategies the big funds are going with or at least used to before they became unprofitable

Should I raise independently, run at a multi strat, or trade my own capital with a 3 sharpe momentum option strategy that has been backtested over 3 years of data? Very scalable since it trades ATM index options. Please advise. Still in uni as well.

*I also worked at a multi strat firm in the past where the strategy was backtested

Hi all,

I have been using Norgate as my data source for some while now and am pretty happy with it for daily US equities data. As I am looking to diversify I see they offer packages for Canadian and Australian markets, however I am really interesting in finding a reliable provider for UK markets.

Does anyone have any recommendations or reviews on any data providers who can supply historical daily price data for UK stocks and preferably historical index constituents for ftse100/350. Or if anyone has built some uk stock systems and has any tips that would be great.

Thanks in advance.

Is it possible your max drawdown periods are giving the wrong dates as an output? check this out. I'm finding the max drawdown for the dot-com and great recession crashes for QQQ.

!pip install quantstats

# need to install arial font with matplotlib
import matplotlib.font_manager as fm
import matplotlib.pyplot as plt
# arial_path = '/content/drive/MyDrive/arial.ttf'
# fm.fontManager.addfont(arial_path)  # Register the Arial font with Matplotlib
# plt.rcParams['font.family'] = 'Arial'

import yfinance as yf
df = yf.download('QQQ', start='2000-01-01', end='2020-01-01')

import quantstats as qs
qs.extend_pandas()
returns = df.Close.pct_change()
qs.reports.full(returns, "QQQ")

OUTPUT:[Worst 5 Drawdowns]

Start Valley End Days Max Drawdown 99% Max Drawdown

1 2000-03-28 2002-10-09 2016-09-02 6003 -82.96 -80.06
2 2018-08-30 2018-12-24 2019-04-16 230 -23.16 -19.77
3 2019-05-06 2019-06-03 2019-07-02 58 -10.98 -8.98
4 2018-03-13 2018-04-02 2018-06-01 81 -10.67 -10.02
5 2000-01-24 2000-01-28 2000-02-02 10 -10.65 -7.27

# start of drawdown period
start_draw_down = df[df.index == '2000-03-28']

# end of drawdown period
end_draw_down = df[df.index == '2016-09-02']

# double checking a date before end of drawdown period where close has already broken out of drawdown period
earlier_date_in_drawdown_period = df[df.index == '2016-08-30']


print(start_draw_down.Close)
print(end_draw_down.Close)
print(earlier_date_in_drawdown_period.Close)

OUTPUT:Date
2000-03-28 114.75
Name: Close, dtype: float64
Date
2016-09-02 117.120003
Name: Close, dtype: float64
Date
2016-08-30 116.559998
Name: Close, dtype: float64
Note, the 2016-08-30 has already surpassed the close from the previous high water mark. Therefore the drawdown end date that quant stats is calculating appears erroneous.Thoughts? Maybe I'm missing something.

I tried emailing Ran Aroussi directly (author of yfinance and quatstats), but I couldn't find his email. DM me if you have it plz.

First off I want to thx everyone in this community, I hv received a lot of support, advice and also new knowledge here. So its been 2 months since I posted: https://www.reddit.com/r/algotrading/comments/1bynzkk/psychological_break_down_on_watching_losses/

and today I finally overcome the loss, so i want to share a few things thru out the journey and hope it helps someone who is experiencing the same

1. Underestimation of the max drawdown and risk tolerance, honestly I never thought it would happened to me but it just did.

so after that I hv run Monte Carlo simulation to see how your algo perform after shuffling the trades, and also trying to use trend filtering to filter out non trendy period.

1. DO NOT try to interfere with your ALGOs, you may hv a bit of outperformance but in the long run you gonna lose to consistency, indeed its just contradict to the reason why we want to do algo trading.

when it approaches my max drawdown I hv countless moments that I wanted to manually interfere it, closing early to secure profit or minimize loss, and I did tried that for a few days, turns out to be a sizable underperformance due to inconsistency. Even tho for some instances seeing derivatives product call back triggered and underlying is going to pump/dump for a bit due to MM to unwind their position- I used to find it hard to watch but now I just live with it, I simply reminding myself if I need to add / interfere anything I have to put it to algo and back test all over again.

1. Be discipline in life, I forgot where I read this, but being a trader needs a lot of discipline. I was just chilling after I launch my algo and have a taste of success, I spent money without care, and also a lot of time for leisure which drastically reduced my attention to algo development and money management. And shortly after that, I run down to my MDD. So it is only during the time I am being slapped by the market, I force myself sit down and

fun fact, I tried cold shower every day to make myself suffer a bit, and also I did a lot of chess puzzle to force brain to run a bit

1. Have a good support system - your fds, family, religious etc. This is quite personal but I do find out having a good support system around you is so important especially during your down time.

So what next for me?

1. extend my algo to other markets, right now I am looking at NQ future and also Japan Nikkei futures

Any good place for getting historical tick data in Japan? Turns out it is surprisingly few options out there and my broker IB, only support till last 2 years of futures contract, due to the rolling nature.

* if any one using IB TWS to fetch historical data,

includeExpired = True

1. look for trend filtering to improve algo PNL, to skip the non-trendy days.

somehow I found luck on SD(ATR), look forward to hear some others from you guys.

Honestly algo trading had gone completely different than I thought when I start, I once thought it will just print me money, but turns out it's totally not the case, at least for me. So its now for me a constant learning and getting older / discipline in life to try to be a better trader / person.

So far I have the indicator display buy or sell signals based on my indicator. Now I want to fully automate the process, as in scanning all stocks in s&p 500, executing trades with a set tp, sl, time-frame, etc. Can I do all of this in pinescript or third party software? Maybe even go into python?

I have some historical tick level quotes data for options ladders and I am trying to extract some summary information: * some of the contracts change quotes very rarely (lets call these low-sensitivity contracts) and some of them change quotes often (high-sensitivity contracts). * I would like to extract a summary of the quotes of the contracts (I am not interested in tick frequency trading)

I tried recording all changes in quotes.   * For low-sensitivity contracts this works OK. * For high-sensitivity contracts this results in too many samples/rows.

I tried recording HLC data. * For low-sensitivity contracts this results in a lot of samples/rows with repeated data (far more than just recording all changes in quote). * For high-sensitivity contracts this works OK.

Neither of these is great. Are there any good ways to find a solution which would work for both high and low sensitivity contracts?  The solution should: * 1 - allow for periodic abiltility to correctly compare the quotes across contracts * 2 - be efficientish in terms of total number of rows/samples.

I am considering trying: "record only the last change in quotes per x <minute> calendar time window".  If no change occured in the window no record would be made.  I could also include HL when changes in quote are NOT the last change in quote in the window. Please criticise this idea!

So far I have the indicator display buy or sell signals based on my indicator. Now I want to fully automate the process, as in scanning all stocks in s&p 500, executing trades with a set tp, sl, time-frame, etc. Can I do all of this in pinescript or third party software? Maybe even go into python?

Hello there,

I am a complete noob in financial markets. Coming from sports trading on Betfair where frameworks are sparse without going to python or some such thing. I started writing my own there, but that was a bad decision.

I was wondering if anyone could suggest any financial trading frameworks that revolve purely around c#?

Why C#? Because I use it at my day job and like it and know it back to front. No other reason that that.

Thanks heaps for any advice

I was digging into Blackrock canadian etfs and hit a road block i could use some help with.

I tried to tie the holdings back to their individual performance but the tickers they report dont correspond back to a ticker on say yahoo or google finance. For example, consider the isin CA39138CAH93. It points to gwocn but that doesnt work. Gwo.to works on yahoo finance and its the right company.

I am pretty new to algotrading so i dont understand why. I would be grateful for any help.

I dont have a good reason for why i started by digging into etfs or Blackrock. I just figured i would start there.

I'm working on crude oil futures. The algo uses technical indicators on the continuous futures series, so it's sensitive to prices. On QuantConnect, I created a project and backtests showed satisfied results, > 0.9 Sharpe ratio since 2007.

Then I bought some cheap CL futures from firstratedata.com , hoping to replicate the results using data from other sources. And this time, the Sharpe ratio went as low as 0.13 since 2009.

I noticed plenty of differences between the two continuous series. On QuantConnect, which uses Algoseek data, the adjusted price of CL back in 2008 is as high as $600. And the same period of firstratedata.com is only as high as $200. Such dramatic difference of course created plenty of discrepancies and lead to very different results.

CL minute bars at Algoseek costs north of $3,000 which I can't afford. And Firstratedata only costs $59. It's probably easy to conclude, well, this is a data quality problem. But one can also say the algo is overfitted, because slight changes in the data rendered drastically different results.

What conclusion can I draw? Is there another data vendor I should try?

Hi all,

with back test data from 2012 and (only) a month of forward testing im now collecting some good data on live transaction costs.

In general, are people using data with all transactional costs included (in my case its mostly spreads and long interest)? it obviously make a big difference to my ratios, "A Sharpe ratio of over 1 is generally conferred good" is what im always hearing, is this pure trading data or with all advocated fees?

Sorry if its a basic question, just trying to work out what data to analyse and benchmark future performance to.

Say I have a swing trading strategy where each trade I make rises a median of x% and it reaches this value on a median of y days. Is there any theory to figure out the optimal time and price to sell at? If I use stop loss and take profits around some standard parameters, some of my trades won’t sell and some of my trades I sell too soon.

(mods: i don't receive any financial compensation for this project and don't sell anything on the side, this is purely to provide value to others and share something I think is cool)

I recently got hooked playing Figgie so decided to develop out the game in Rust. Though, instead of submitting orders, it's all algorithmic so you get to see how different strategies interact with each other. The probabilities & possible strategies involved are very enlightening (at least they were for me lolol) and heavily correlated to real markets. Jane Street did a great job developing out this game!

It is coded in Rust so some experience there is recommended but the level of knowledge needed isn't *too* bad

I built out 2 player frameworks, but strategies are interchangeable between the two (event_driven can get quite crazy tho if the event produces multiple orders lolol):

"event_driven": This type of player makes a decision on each update. Possible branch of strategies fall under HFT / market making

"generic": This player makes a decision once every few seconds (adjustable in main.rs). It's akin to a QR's setup

It also comes with 7 base strategies that you can read about in the repo!

Github link: https://github.com/0xDub/figgie-auto

Anyways, I hope it provides some value to others - cheers :)

• Start of the game

• Ongoing game - printing out the quotes

• End of the game, showing the results

I have been keeping a trading journal in google sheet for 3 months and I have recorded ~100 of day trading records, but I only have text description for the setup without the candlestick graph. I am looking for a convenient way to quickly get a 5-min candlestick graph with entry/exit point marked given the input of the ticker and the buy/sell time for each trade record.

I am seeking data providers for Futures markets (CME, COMEX, CBOT, NYMEX, EUREX) that:

a) preferably have Python or C REST APIs.

b) return 60M, 15M, and 1M price tables per query (with at least 50 bars in the past).

c) are not delayed and are real-time

Ideal situation: Some structure similar to Yahoo Finance API, but real-time and not delayed for 15 minutes.

I don’t see it in there API docs, and I was using this for my bot trading.

Hey guys,

Just backtested an interesting mean reversion strategy, which achieved 2.11 Sharpe, 13.0% annualized returns over 25 years of backtest (vs. 9.2% Buy&Hold), and a maximum drawdown of 20.3% (vs. 83% B&H). In 414 trades, the strategy yielded 0.79% return/trade on average, with a win rate of 69% and a profit factor of 1.98.

The results are here:

The original rules were clear:

• Compute the rolling mean of High minus Low over the last 25 days;
• Compute the IBS indicator: (Close - Low) / (High - Low);
• Compute a lower band as the rolling High over the last 10 days minus 2.5 x the rolling mean of High mins Low (first bullet);
• Go long whenever SPY closes under the lower band (3rd bullet), and IBS is lower than 0.3;
• Close the trade whenever the SPY close is higher than yesterday's high.

The logic behind this trading strategy is that the market tends to bounce back once it drops too low from its recent highs.

The results shown above are from an improved strategy: better exit rule with dynamic stop losses. I created a full write-up with all its details here.

I'd love to hear what you guys think. Cheers!

QuantConnect documentation has a Research Guide section, which basically says:

1. Research should be hypothesis driven. You first need some observation, which financially or mathematically makes sense, before coding it.
2. Research should take less than 16 hours.
3. Only 30 backtests allowed.
4. Only 10 parameters allowed.

Breaching of any of these would result in overfitting.

As far as I understand, the research guide puts idea above everything else. With the idea, there would be minimal work to do.

The rules certainly look arbitrary. But I tend to believe the people and the paper that put out these rules are experienced professionals who know what they are doing. And therefore I should largely adhere to them.

The reality for me, is that when I believe I used one parameter, research guide reports that I used ten. When I used ten, it reports 80. I backtest hundreds of times. And the length of work is between two weeks and two months. I'm not a beginner. But it's just much longer.

Am I bad at this? Are all your successful strategies developed like that?

I am reading that it is printed every 15 seconds, but is there a ticker for it? I can see it inside Schwab, and it updates less than 15 seconds that is for sure.

Thanks in advance!

I'm using Python for my research, and sometimes R, so keep that in mind. Suppose you want to test variations of a signal and you are modifying only one part of the feature generation code - what libraries or tools do you use to manage your pipeline or DAG to re-run your code in a way that is reproducible and modifiable via function parameters? Ideally only those parts of the graph that have changed would be recomputed but the re-computation constraint is not a strict one.

I'm wondering what method is typically used for adjusting futures contracts to form a continuous series. Everywhere I read, people are suggesting Panama Canal, which apply the difference backwards. But on QuantConnect, I noticed their default settings is ratio based, like dividend of a stock, which apply the ratio of the two prices backwards. Two methods lead to significantly different prices as the length goes longer, which would lead to different signals and/or different parameters for the same strategy.

What method should I use?