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Forextory is the cheapest tool to help you download Forex historical data since 2004 with 99% modelling quality for traders to backtest trading strategies.
I need an automatic download of historical forex price data. I am willing to pay for a tool, but it must be script-able, with the ability to output to CSV format. Any ideas? UPDATE: Oanda is my broker, and at least one person suggested I use their API to pull the data I need. I finally put on my big boy pants and scrounged around the web for some code I could plagiarize, and I found it. It was really pretty simple - as most things are, once you figure them out. Thanks for all the suggestions.
[Rookie question]What are some reliable sources of historical forex OHLCV data?
This is a rookie question. I'm just dipping my toes in forex and would like to build some algorithms to trade. For this, I need a reliable source of historical OHLCV data. I've found that different sites' data differ from each other. Some sites also have OHLCV data on the weekends too. Can anyone guide me on this? Thanks :)
Does anyone have any historical data on another special dividend stock that was traded with a CUSIP number, heavily shorted, and then went private? This can’t be the first one… or is it?
I have had a few stocks be delisted in the past, but all trash penny stocks. And the reason they got delisted was because they all were basically worthless. Is MMTLP truly a unicorn? Would be good to dig in and look at other similar special dividend stocks that went private if any such a thing exists. Please discuss. By the way, from what we all know we hold something worth literally an order of magnitude at minimum more than what it was traded at today. Literally and not the new Webster dictionary of literally that means figuratively. We will get some money from that oil land and it sure as hell ain’t $10 a share.
Don't pick a Forex broker based on some promotion. If you don't want to do your own trades, pick your broker based on his VERIFIABLE five year historical performance and actual trading charts. Otherwise just toss your money in the nearest toilet and flush.
I have backtested 15000+ variations of alternative trading strategies based on 5 years of historical data.
I was playing around with a few popular alternative data APIs. Some of them are:
US Congress trades
GitHub Commits
Wikipedia Views
Google Trends
Sentiment on Reddit
I backtested different variations of them on 3-5 years of historical data. I also calculated benchmarks for them. I sorted them based on their performance in relation to the benchmark. The results are below. ---------- GitHub Commits Every month If number of commits on GitHub is down for Bitcoin (BTC) by 30% compared to the average from the previous month Then Buy 100 USD of Bitcoin (BTC) Profit/Loss: 432 USD Change: 144 % Time period : 3 yr 9 mo Benchmark Profit/Loss : 142 USD 7 USD invested monthly HOLD Wikipedia Views Every month If views on Wikipedia are up for Bitcoin (BTC) by 5% compared to the average from the previous month Then Buy 100 USD of Bitcoin (BTC) Profit/Loss: 191 USD Change: 14% Time period : 2 yr 9 mo Benchmark Profit/Loss: -48 USD 44 USD invested monthlv Google Trends Every month If interest over time on Google is down for Bitcoin (BTC) by 30% compared to the average from the previous month Then Bu 100 USD of Bitcoin (BTC) Profit/Loss : 700 USD Change: 19 % Time period • 2 yr 10 mo Benchmark Profit/Loss: 65 USD 26 USD invested monthly ----------- It seems that most of those strategies are there simply by chance, if you test a sufficiently large number of strategies some of them will just work. Some of them definitely don't work, US Congress Members have 45 days to make their trades public. Some of them seem to work, for example, GitHub commits. I do understand that the quality of the code cannot be judged by the number of commits, but perhaps developers are the first ones to know that the project is not going well and they lose interest.
[OC] Analysis: Using historical data to shed light on concerns about Christian McCaffrey's ability to bounce back (and stay for a while) at a high level after two injury-riddled seasons
Coming into training camp, like every season there were positive vibes and high hopes by folks in the Giants, Panthers, and Cowboys training camps about their star running backs health and outlook going forward. We had reason to be concerned about E.Elliot, S.Barkley and C.McCaffrey given their declines relative to their early career peaks. Going into the season I analyzed the likelihood that these running backs would make it back (or reasonably close) to their peak years when they were at the top of the league. My analysis wasn't based on each of the players specific situation, injury type, reason for decline, and outlook (I'm not that smart), it was simply a look back at all running backs who have experienced a similar statistical pattern as these three and what happened to those guys; young stars, followed by a decline earlier than expected based on their age.. My criteria
RB making the pro bowl before age 26 since 1978 (N = 107)
Then I narrowed this list down by players who had a clear decline in production (injury-related or not). This took the sample size down to 35 (out of the 107)
Then I looked at what happened next and categorized their remaining years into three categories: a bounce back (at or close to their early pro bowl production), or the decline was truly the beginning of a continued gradual decline, or they dropped off a cliff. I also hypothesized that early draft picks would have a higher rate of bouncing back, because teams are more vested in their success and are more willing to stick with that player. The results confirmed that was the case. A team is more likely to keep trying to force a top ten pick like George Rogers to play well, than they are to stick with later round picks like Barry Foster or Rueben Mayes after a decline. Overall, 31% of running backs who have a somewhat similar criteria to McCaffrey, Barkley and Elliot returned close to their early dominance for a sustained period of time. But if they were a top 10 draft pick like these three guys, they came back 71% of the time. https://preview.redd.it/jbncanqqlcv91.png?width=1007&format=png&auto=webp&s=1acc77e5ea4f256fd6fb17c8f0fc3ad20ec85a00
Examples of guys who fit the criteria of a full bounce back: Edgerrin James, Steven Jackson, Jerome Bettis.
Examples of a continued gradual decline: Natrone Means, Devonta Freeman, Jordan Howard.
Examples of an abrupt end to their career: Jay Ajayi, Eddie Lacy, Barry Foster.
Caveats galore: This is highly subjective. There was no formula to determine "bounce back" or "gradual decline". I eyeballed their performance (mostly yards from scrimmage) and made the determination. Same with the decline in production for the group of 35 decliners out of 107 running backs who made the pro bowl before age 25. My criteria for early success (pro bowl by age 25) and a decline at or before their prime (age 26 or younger) are arbitrary, but I thought it was a sensible way to do this. And of course, we have some real sample size issues in this data. Hope you find this chart interesting.
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