Most cell phone companies have a way to send emails that will arrive at your cell as a text message. This makes it easy
Your simulation doesn’t have maintenance windows. Your simulation doesn’t have server failures or badly written calls to a library you didn’t write. Real life has all these things…. and furthermore….
My bot built lookup tables for the maximum precision of both price and amount purchased for each coin, and used them to compute buy order amounts. Do it wrong, and you’ll likely get an error back from the server.
It is entirely possible to have an error that results in your bot trying to sell a coin at the price of another. You have to do something really dumb — and I did. Due to a poor design decision on my part, an early version of my bot tried to sell some BTC at the current cost of some BCH, which was an order of magnitude lower.
When you trade live, you need to issue your buys and sells as either a limit order or market order. With a market order, the transaction will be completed at whatever the going rate is. That’s bad. Be careful with that, because that price can MOVE in a matter of seconds. That’s why you have a bot in the first place! I chose to place my orders as limit transactions, so that if the price moved dramatically, it wouldn’t buy or sell at a different price than I had intended.
And that’s it. Maybe you already knew or just learned these methods. But try to use it in your project. After every project I designed, I turned and looked through this list. And every time I found things to fix.
Try to do that in real life, and it will likely fail. The trading platform will have limits on the amount of precision you can specify for both the amount purchased and the price, and it varies by coin. This makes perfect sense when you think about it — DOGE is currently selling at a fraction of a cent, and BTC is currently selling over $19,000. You can’t reasonably buy small fractions of DOGE the way you can with BTC.
In retrospect this is obvious, but it bit me the first time my code tried a live transaction. In simulation, it’s fine to issue a buy for (cash_on_hand/coin_price). Sure, the simulation might store that you bough 12.2342348998729384797 ETH, but who cares?
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as a potential blocklist, we need to filter out the false positives. One easy way to differentiate likely legitimate results with malicious ones is just by looking at the server’s history. Malicious C2s are generally ephemeral; they’re coming and going quite frequently, while legitimate servers tend to stay the same for long periods of time. This is where vendors with Internet historical data really come in handy. If the server matching the Cobalt Strike JARM has had its attributes unchanged for over a year, it’s more likely a legitimate false positive, while a server matching the Cobalt Strike JARM that didn’t exist 2 months ago is much more likely to be a malicious true positive. Combine that with other server attributes like name, hosting provider, certificate authority, etc. and we have ourselves a high quality Proactive Blocklist.You have to deal with losing power, losing internet, and program crashes. If you are computing values like moving averages or RSI, or looking at candlestick patterns, you need to be very sure that your data is sequential and hasn’t had a break. If it does, your program could make bad calls. You need to save state, and check date stamps of data. If a break in data is found during the period of time you are computing indicators against, you need to pause execution until you have the full range of data that you are calculating against.
Reality isn’t quite as easy. Actual trades take a little time, and the price is changing. When your bot is trading live, sometimes only part of your order will complete before the price shifts significantly. It may not even complete any of the order. I refer to this as a “swing and miss”. Your bot needs to handle these situations gracefully. I opted to save the order transaction number and check the results every so often — if, after a hour, it didn’t result in a buy of coin, the bot cancelled the buy order and tried again.
When you simulate trades, you are probably making some assumptions. Your program decides to buy or sell at the price at that moment in time, and it succeeds, every time. Your simulation racks up the profit!
The only reason it didn’t make me very, very sad was because the prices were so far apart that the trading service flagged it as an error. Had it been merely off by, say, a factor of four, it would have let me do it, and it would have cost me a lot.
I recently wrote my first cryptocurrency trading bot. I started by playing with historical prices for Bitcoin at ten minute intervals — I loaded them into Python Pandas dataframes and ran some simulations, buying low and selling high. It looked great! The code was simple, too — I figured it wouldn’t take more than a few hours to bolt on some robin_stock functions and go to town. I was wrong. Here’s what I learned as I went — perhaps it will save you some time.
And that’s it. Maybe you already knew or just learned these methods. But try to use it in your project. After every project I designed, I turned and looked through this list. And every time I found things to fix.
This is just good programming practice, but it gets driven home pretty quickly if you don’t write it in from the beginning. Every time you issue a call that talks to the server — getting prices, issuing an order, checking status — check for exceptions. Every. Single. Time.