u/Henry_old

seeing too many traders setting up complex postgresql or mongodb clusters to log tick data for their algorithms. every network hop between your bot and your database is a millisecond you cant afford to lose. unless you are running a massive distributed hedge fund you are just killing your write performance with overhead. switched my entire logging and state management to sqlite in wal mode. it is local atomic and handles thousands of concurrent writes without blocking the main event loop. enterprise bloat is for corporate web apps not for high performance execution engines. keep your data on the same machine and keep the filesystem simple or keep losing to the guys who do

you don't have a psychology problem, you have a latency problem.retail spends months optimizing python scripts, then deploys on public rpc nodes. institutional algorithms co located on bare metal sweep the liquidity before your visual dashboard even updates. if your execution delay is over 50ms, your algorithmic logic has a mathematically negative expected value. you are paying a structural latency tax.stop staring at lagging indicators. the only absolute truth is the raw api execution timestamp. audit your backend routing for slippage leaks. pure execution is all that matters

seeing everyone in this community complain about the network being "congested" during volume spikes. warning: this is a harsh technical reality check and mods hate infrastructure truth. the network is never congested, your access point is just inferior. public rpc nodes rate-limit your requests by default. while you manually click swap buttons in a frontend wallet, backend python algorithms are routing signed transactions directly through dedicated jito or helius nodes with sub-second latency
bots pay compute budget priority fees at the programmatic level, completely bypassing the public queue. the blockchain works perfectly. your infrastructure is just built for retail exit liquidity. raw execution layer always wins against consumer interfaces

as a dev building execution algorithms watching retail day traders debate rsi and trendlines is hilarious. the institutional bots wiping out your accounts do not look at candlestick patterns. they parse raw level two binary data to calculate exactly where retail liquidity and stop losses are clustered. when price suddenly wicks past your perfect support level and instantly reverses it is not market psychology or bad luck. it is a hardcoded script intentionally triggering your stop loss to absorb your liquidity for a larger fill. you are bringing a geometry set to a data war. you are trying to predict human emotion but you are actually just feeding a math equation that can see your pending orders

see so many retail quants wasting months rewriting their python logic in rust or c to save half a millisecond of execution time. your code speed is completely irrelevant if you are running the bot on a standard cloud instance in ohio while the matching engine is in tokyo or new jersey. speed of light dictates network latency. if your tcp handshake takes 50 milliseconds your optimized binary does not matter at all. institutional money pays for physical server colocation right next to the exchange routers. you are losing to physics not to the python gil. focus on moving your docker containers physically closer to the exchange api endpoints before you rewrite your entire codebase

normal users complain when it costs fifty bucks to send a transaction. but the real threat is network bloat. people are currently stuffing the bitcoin blockchain with junk data and token spam. every single full node has to store all this garbage in ram just to stay synced. when the database gets too massive normal guys cant afford to run nodes on standard hardware anymore. if only massive data centers can afford to run nodes then bitcoin just becomes a regular centralized bank. miners are getting rich off the spam fees while the actual network decentralization quietly dies

https://preview.redd.it/ds987am1qc0h1.png?width=982&format=png&auto=webp&s=dc3f035d1eeda2b04cc8a2781f7f436478ebc2ca

manual journals lie because they miss execution latency, pulled api logs for 1200 manual trades and found my win rate drops 40 percent within 60 seconds of a loss, excel cant track that adrenaline window so i coded a custom python tool to audit the raw data, it uses a weighted scoring model to flag tilt severity automatically, the script tracks time decay between your trades and measures position size escalation along with drawdown context, this benefits manual day traders by forcing them to see their exact latency triggers instead of relying on false memories, uploaded the math and an anonymized terminal report to my github linked in my profile if anyone wants to stop guessing and audit their own execution

wanted to share an update on the revenge trading detection work i posted about earlier, the community feedback helped a lot so posting the results, expanded the sample to 6 accounts 1200 trades total across bybit binance okx bitget htx kraken coinbase and bitvavo, the pattern was consistent traders systematically underestimate how often they revenge trade, one account self reported 2 times but actual count from raw data was 14 instances over 3 months with average position size 230 percent above their baseline, the detection uses a weighted scoring model now instead of a binary flag, time decay after loss is 35 percent as how fast you re enter matters most, position size delta is 30 percent as size escalation is the clearest signal, drawdown context is 20 percent based on loss magnitude relative to equity peak, frequency spike is 15 percent for trade clustering in short windows, scores run 0 to 100 and anything above 60 is high severity, the worst cluster in the sample was 4 trades in 23 minutes after a single 340 dollar loss hitting a score of 88, uploaded the full scoring methodology and anonymized sample output to my github linked in my profile for anyone who wants to dig into the math or adapt the weights, curious if others have quantified this since most resources treat revenge trading as a binary yes no which misses a lot of the nuance

pulled my api logs and found win rate tanks 40 percent within 60 seconds of a red trade because of massive revenge size spikes, manual tracking misses the exact latency so i built a custom script to flag this severity score automatically, curious if anyone else audits their raw api data like this instead of guessing?

ran a full year of my manual trade logs through a pandas,numpy script i wrote to detect behavioral anomalies. found a massive gap between my perceived 'conviction and actual tilt patterns. worst instance: 9.6x size revenge trade within 32 seconds of a loss. currently modeling a 'cognitive bias score based on volatility clustering to flag these spikes. curious if anyone else uses python to audit their own psychological biases through data

trying to land a swap on jupiter or raydium manually during congestion is just a donation to the network. i switched to a custom engine using helius rpcs and python to handle dynamic fee estimation and jito tips because the default ui values are too slow for current block density anybody else ditched the web uis for direct node execution or are you just increasing slippage and praying?

ran a full year of trades through a loss chasing detector i've been building worst instance: re-entered coai 32 seconds after a loss at 9.6x previous size. score 88/100 didn't feel like revenge at the time. felt like conviction. the score says otherwise curious if others have found similar gaps between what felt intentional and what the data shows

paying 0.01 sol just to get a swap through on jupiter and it still drops half the time anyone found a better way to route through congested blocks or is it just over for manual traders

insane fully diluted valuations and constant unlocks retail is just exit liquidity for venture capital at this point who else is only buying 100 percent circulating supply now

mev bots taking everything before retail clicks approve anyone else noticing massive drop in manual success rates today

paying massive slippage just to force a swap through routing algorithms feel completely broken right now anyone else bleeding capital on simple onchain swaps

thanks for the input last week — the score idea clicked moved from a binary flag to a severity score per instance components - time decay 35% faster re-entry = higher score - size delta 30% position size vs previous - drawdown context 20% was account already in drawdown - freq spike 15% trade count increase after loss

https://preview.redd.it/m2qp8wqfouyg1.png?width=2338&format=png&auto=webp&s=84f30fd4e928723487ee9faa58bc32c06ff2ff85

https://preview.redd.it/4eeadbbiouyg1.png?width=2360&format=png&auto=webp&s=f75861dabf8dca6e64944533f30856e66c4f7572

screenshot shows it live — 1 instance flagged score 47 medium severity curious if the weights feel right or if anyone weights these differently

https://preview.redd.it/jkaq3sayouyg1.png?width=2422&format=png&auto=webp&s=cb361c48d5072f10b7f637a07b70533f21eca91d

been working on behavioral analysis for trading accounts. current definition: same symbol within 10 min after a loss, next position >= 90% of previous size edge cases i'm not sure about: - scaling in: excluded if multiple entries in same direction - averaging down: same symbol, adding to a losing position — revenge or strategy? - what if size is smaller but still same symbol same direction? ran it on a real account (1 year, 758 trades). flagged 11 revenge trading instances.

https://preview.redd.it/kx6nmmqaxqyg1.png?width=3398&format=png&auto=webp&s=4ab5fda53c87a9bc51c0b22d8a20cc3a98e58c6c

curious how others define this in their own tracking

I'm building TraderAudit — a tool that analyzes raw trade data to spot behavioral leaks and hidden profit drains. The core engine is running, but I need outside eyes to tear it apart before scaling.

If you have old execution logs (CSV/API data), dump them in and tell me where the system fails.

Specifically looking for feedback on:

1. Parsing logic: Does it break on your specific broker/exchange format?
2. Data visualization: Is the leak detection actually obvious, or is the dashboard cluttered?
3. Missing logic: What specific metric is missing that makes this useless for your strategy?

Link: https://audit.vmforgedev.com

Rip it apart in the comments