Alpha Trader Group
What happened in recent days was very odd for me and all the team members who had a chance to look at the greater picture than can be seen from the perspective of a single trader.
The teams attention was drawn by the discrepancy between the trading results achieved by different groups of traders. Some traders won big while the others lost their trades and all this while all conducted the same System trades on the same asset and with the same expiry time. We had to get to the bottom of that and began searching for the answers. The first thing we established was that mostly it occurred when the delta between the entry rates and expiry rates was small, and on contrary – when the delta between the entry and expiry prices was big – all the traders ended up with the same result. Naturally – the first thing that came to our mind was the Risk factor played by the Brokers as they usually are an immediate suspect in the minds of most of us. All this because once they used to mark a winning trader and whatever direction he took – call or put – they activated a Risk factor against him reducing significantly his chances of winning the trade if the outcome was close. But that practice has been obsolete for some time and another fact that contradicted that assumption was the fact that most of traders that were affected by unfavorable pricing at the entry level were the traders with smaller trade sizes and most of those who got favorable (close or similar to the price displayed by Reuters or Netdania, the price system identified as valuable enough to take a trading position) price and ended up winning were the traders with a bigger trade sizes. So, any foul play on the part of the brokers did not make sense. Why would they marginalize the small trade size positions and would not do the same to bigger trade size positions?
So, at that stage we had established the following : the discrepancy occurred when the delta between the entry price and expiry price was small (close trade outcomes), the reason was the pricing at the entry level while the winning trades got more favorable pricing than the losing trades and the losing trades were of the small size trades while the winners were bigger size trades. But why? Since the Brokers did not seem to interfere as it would not make any sense to interfere with the trades and make the small trades to lose and big trades to win. So the answer was obvious and all we had to do is check the timing of the trades. Of course the hindsight is always 20/20 and that was the case here too. The trades that were won while the margins between the entry and expiry prices were small – the winning trades were those of bigger size and taken close to the execution of the trade by the system while the losing ones were those executed later and were of the smaller size.
When we ran it in parallel the picture became even clearer: it was one to one – bigger the trade size – the trading position was entered closer to the execution time and subsequently got the better price. Smaller the trade size, it got executed further down the timeline and subsequently the less attractive size. The Lag time as we call it decided many trades outcomes.
Well, very good that we established the facts, but what about the factors that influenced such inequity between the traders and their trades? Why would the bigger trades be executed earlier than smaller trades while we did not enter that in the logic of the system? And why would be such lag time of few seconds while we took all the precautions and reduced to the minimum the lag time by threading and packeting thus helping the syncing process.
The answers were right in front of us but it took us time to see them: because of mass joining process to the bot by new beta testers and a drastic increase in the number of users – the lag time piled up affected by the time occurring in transition from packet to packet (traders were packeted in groups to avoid system overload on Broker platforms avoiding “getting stuck” situations). Ok, while we had an answer to why lag time was of a few seconds at the least – we still did not have an answer on why the System chose to trade with the bigger trades first and smaller trades later?
After some brainstorming and looking into the system’s performance logic we were shocked to discover that VOL1 (100PercentProfitBot) has developed it’s own logic based on the code and performed on Artificial Intelligence capacities. Since the main goal of Money Management Module integrated withing the system is producing the maximum profits – VOL1 acted on that logic by first trading for the traders with a bigger market value – bigger bankroll and most accounts. Those who had more funds in more accounts establishing a singular superiority over those who had least funds in least number of the accounts. That was amazing thing to see how the system prioritized it’s goals reflecting on overall Money Management directive to accumulate as much profit as possible per each action it takes.
We began running all the data to figure the numbers involved and this is what we came up with:
The middle of the graph of the winners stand at 2044 money units (the sustem does not differ between the currency values for the user). That means that accounts with average 511 money units over 4 accounts and 404.80 money units over 5 accounts are the last line of the users who win the trades in a 20-0 rate we had last night (5 trades were won and those with 4 accounts with that bankroll at least still had 20-0). Once you go bellow that line the winning rate begins to decrease like one trader who commented that while some posted 18-2 and the others 10-2 (had less accounts funded) etc all the way to the bottom where there were 3-2 results with single broker (not all the bad pricing cause losses as some trades are won by a big margin making the pricing insignificant).
So, in order to gradually increase the system overload and also create some separation between the pole positions – we created Alpha Traders Group or 5000 club as we call it here because of the average bankroll size of those traders who were assigned there first. We want to create more equality in trading prioritization by the system, therefore we created Alpha to accommodate bigger traders first (bankroll and the broker accounts) and assigned there also the traders who depleted their funds and funded it again to be at the level 2044 money units scattered over 4 or 5 broker accounts. We also will have the groups for smaller traders and smaller etc. The only thing is that I’d like to supervise all this process and see it through.
Conclusion – the main one – although sounds like a commercial, is that More Money you put to work for you – better the chances of gaining in the process.