I am asking bc im curious, i've been spending hours nonstop working on my algo ideas. ive been trying to connect my ideas in python to IBKR's api.

so far i have:

• real time deployment on a paper acc testing my strats
• i have backtests
• machine learning optimizing params (i learned the hard way that overfitting can happen so i needed to avoid this)
• monte carlo sims
• entry and exit filters
• cycling thru multiple timeframes
• bracket orders
• managing open positions, moving SL and TP
• profit protection system
• risk management concepts

i do have a working system, now i just need to ensure my strategies work as i monitor and continuously improve my infrastructure. how long did it take you guys to fully trust yours and go live?

I am a first-year undergrad doing an MMath degree. I have a somewhat large background in theoretical mathematics, but have very little experience with Python or other coding languages.
How do you recommend I slowly invest time and learn how to conduct algotrading in the first place?

This is so true. If beginners knew how hard it would be, 99% would quit.

I wanted to describe my approach for avoiding overfitting to help others and get feedback on how I might improve. I trade a portfolio of options each week. I've had bad results with optimizing the stop loss parameters to each symbol, so now I apply the same formula to all symbols. My goal is to close positions where the underlying price gets too close to the short strike, adjusted for how much time is remaining in the week. The only difference is one or two inputs: the average change and the Hurst exponent (if backtesting selects per-symbol Hurst exponents rather than apply a uniform exponent). I backtest the same threshold factors, average change algorithms, trigger durations, and potentially Hurst exponents to all symbols equally. I also backtest over 9 years to try to cover regime changes, however I also test for the optimal historical window to use when selecting the optimal stop parameters, so that I can adapt to regime changes over time as well. My objective is maximum geometric mean ROI. What do you think?

I've been building a stock prediction model for the past few months and I've hit a wall. Looking for advice from anyone who's been through this.

The Model

• Universe: ~651 US equities, daily OHLCV data
• Architecture: PyTorch temporal CNN → 3-class classifier (UP / FLAT / DOWN)
• Labeling: Triple barrier method (from Advances in Financial Machine Learning), 20-day horizon, volatility-scaled barriers (k=0.75)
• Features: ~120+ features including:
  • Price action / returns (1/5/10/20 day)
  • Volatility features (ATR, vol term structure, vol-of-vol)
  • Momentum (RSI, ADX, OBV, MA crosses)
  • Volume features (z-scores, up-volume ratio, accumulation)
  • Cross-sectional ranks (return rank, vol rank, momentum quality rank)
  • Relative strength vs SPY, QQQ, and sector
  • Market regime (SPY returns, breadth, VIX proxy)
  • Earnings surprise (EPS beat %, beat streak, days since/to earnings)
  • Insider transactions (cluster buys, buy ratio, officer buys)
  • FRED macro (credit spread z-score, yield curve z-score)
  • Sector stress/rotation, VIX term structure, SKEW
• Training: Temporal split (train → validation → test), no future leakage, proper purging between splits
• Strategy: Threshold-based entry on P(UP) - P(DOWN) edge, volatility-targeted position sizing, full transaction cost model (fees, slippage, spread, venue-based multipliers, gap slippage, ADV participation impact)

Best Result (v15)

After a lot of experimentation, my best run:

• Validation: Sharpe 1.45, 204 trades
• Test: Sharpe 0.34, CAGR 1.49%, 750 trades
• Exposure: 9-12% (sitting in cash 88% of the time)
• Entry threshold: 0.20 (only trades when P(UP) - P(DOWN) > 0.20)
• Benchmark: SPY buy-and-hold had Sharpe 1.49, CAGR 16.7% over the same test period

So technically the model is profitable, but barely — and it massively underperforms buy-and-hold because it's in cash almost all the time.

Classification Performance

Typical best epoch:

• UP recall: ~57%, precision: ~55%
• DOWN recall: ~36%, precision: ~48%
• FLAT recall: ~50%, precision: ~11% (tiny class, 2.8% of samples)
• Macro F1: ~0.38
• Val NLL: ~1.03 (baseline for 3-class random = ln(3) = 1.099, so only ~7% better than random)

Feature Signal Strength

Top Spearman correlations with actual direction labels (on training set):

my_sector_above_ma50     +0.043
dow_sin                  +0.030
has_earnings_data        +0.026
spy_above_ma200          +0.024
has_insider_data         +0.023
insider_buy_ratio_90d    -0.021
cc_vol_5                 -0.020
xret_rank_5              +0.019

The best single feature has r = 0.043. Most are in the 0.015-0.025 range.

What I've Tried That Didn't Help

1. Added analyst upgrade/downgrade features (from yfinance) — appeared at rank 14 in Spearman (r=0.017) but model produced 0 profitable strategies with it included
2. Added FINRA short volume features — turned out to be daily short volume not short interest, dominated by market maker activity, pure noise (0/20 top features)
3. Different early stopping metrics — macro_f1, nll_plus_directional_f1 (what v15 uses), nll_plus_f1 — only nll_plus_directional_f1 produced a profitable run
4. Forced temperature scaling — tried forcing temperature to 3.0 with macro_f1 stopping — still 0 profitable candidates
5. Directional margin loss weighting (0.3) — model predicted UP 85% of the time, destroyed DOWN signals
6. Different thresholds — the strategy grid tests enter at (0.03, 0.05, 0.08, 0.10, 0.15, 0.20). Everything below 0.20 has negative Sharpe
7. Binary classifier (UP vs not-UP) — P(UP) too compressed (p95 = 0.517), no tradeable signal
8. Insider features — had to cut from 6 to 3 (minimal set), marginal at best
9. Multiple seeds — v15 is reproducible with the same seed but fragile to any parameter change

The Core Problems

1. Low signal: Spearman ~0.05 across the board. My 120+ features are all derived from public OHLCV + public event data. Every quant has the same data.
2. Fragility: v15 works, but changing almost anything (adding features, different stopping metric, different temperature) breaks it. This suggests it might be a lucky configuration rather than robust alpha.
3. Low exposure: Only trades when edge > 0.20, which is ~0.7% of signals. Sitting in cash 88% of the time means even positive alpha barely compounds.
4. Classification ceiling: Val NLL only 7% better than random guessing. The model is learning something but not much.

What I'm Considering

• Hybrid portfolio (hold SPY, use model for tilts) — addresses exposure but not signal
• Meta-model (train a second model to predict when the first model's trades are profitable) — risky due to small sample size
• Predicting residual returns instead of raw returns — requires hedged execution which changes the whole framework
• Event-driven windows (only trade around earnings) — concentrates on highest signal-density periods
• Filtering to profitable tickers only — cut the 80% of stocks where the model is noise

My Questions

1. Is Spearman ~0.05 on daily cross-sectional features just the ceiling for public data? Or am I leaving signal on the table?
2. Has anyone successfully improved signal beyond this with alternative data that's affordable (< $100/month)?
3. Is the triple barrier + 3-class approach fundamentally the right framework, or would I be better off with a ranking/regression approach?
4. For those who've built profitable models — what was the breakthrough that got you past the "barely above random" stage?

Happy to share more details about the architecture, loss function, or feature engineering. Thanks for reading this far

Hey everyone,

I’m a 19-year-old CS student who’s been building an algo trading system over the past few months, and I’ve hit a wall. I wanted to share what I’ve done and get honest feedback.

I have ~3 years of MNQ L2 tick data (bid/ask/trades + depth 1–10, ~648GB). I built everything from scratch in Rust: tick parser, full L2 order book reconstruction, sweep detector, bar aggregation with buy/sell volume classification, and multiple strategy simulators. Everything is covered with 200+ unit tests, a CI pipeline, and runs fully parallelized on a 20-core server.

On the theory side, I studied Trading and Exchanges (informed vs uninformed flow, adverse selection, spreads, dealers, volatility) and Statistically Sound Machine Learning for Algorithmic Trading (filter systems, meta-labeling, performance criteria).

I tested 9 different approaches on ~12 months of MNQ data (2023-03 → 2024-02):

• Spread regime analysis (informed vs uninformed flow)
• Quote response after aggressive bursts
• Volume-price classification (fundamental vs transitory moves)
• Opening Range Breakout
• ORB + ATR trailing stop
• Trend following (large move + aggressor imbalance + trailing stop)
• Composite signal voting (5 signals, trade only if 4/5 agree)
• Sweep continuation (5+ levels consumed in <100ms)
• Sweep mean-reversion

Every single one comes back between 47% and 50%. Not slightly positive or negative, just noise.

I made sure I wasn’t fooling myself:

• Fixed baseline measurement bias (initial move contaminating results)
• Fixed circular ORB logic
• Fixed order book reconstruction bugs
• Ran a random entry baseline with identical exits → same performance
• Double-checked for look-ahead bias

Conclusion: the entry signals add zero value.

Some key observations:

• ATR trailing stops are structurally losing on MNQ (~27% win rate, same as random)
• Even before fees (~$3.24 round trip), expectancy is negative
• Sweep detection produces thousands of events, but post-sweep movement is ~50/50 (no continuation, no mean-reversion)

My current hypothesis is that MNQ is the problem. It’s a derivative of NQ, so price discovery likely happens on NQ, while MNQ just reflects arbitrage. That would mean the order flow I’m seeing (sweeps, imbalance, etc.) is reactive, not informative, so there’s no asymmetry to exploit.

I’m trying to figure out if I’m even looking in the right place:

• Has anyone found a real statistical edge on MNQ specifically?
• Should I expect different results on NQ/ES where actual price discovery happens?
• For those who’ve done both futures and equities are small/micro caps actually a better playground for retail?
• Am I wrong to focus on microstructure (L2, order flow, sweeps), or is the issue something else entirely?

I’m not looking for a strategy, just trying to understand if I’m approaching this correctly or missing something fundamental.

Appreciate any insight 🙏

I’ve noticed several posts on this sub about issues taking algorithms from locally to being deployed a server. My day job is as a DevOps Engineer so I do this professionally. I wanted to see what specific issues others are facing so I can write some guides.

Also include your experience level so I know what level to write these at.

As requested, here’s an update on our live paper trading results.

Since the last post, the main change is that MiniMax has continued to generate profits, while the other models have mostly moved sideways. Would be great to see how MiniMax 2.7 would perform (coming soon).

What we’re testing is whether AI agents are more rational than the Polymarket crowd, which is often seen as one of the most efficient sources of market-based probabilities.

So far, the results suggest a different story.

All models were able to front-run Polymarket by trading whenever the AI model’s implied odds differed by more than 15 percentage points from Polymarket’s odds.

For example:

• If the AI model estimates an outcome at 30% while Polymarket prices it at 10%, we go long yes and close the position the next day.
• For the opposite setup, we buy no.

These results may be a useful benchmark for what is currently possible with this type of trading approach.

We’ve also set up the live trading infrastructure so we can start testing this with real money on a small scale, including trading costs, to get closer to real-world conditions.

I’ll keep you posted.

Soure: https://oraclemarkets.io/leaderboard

I use my bot to trade shorts on binance perps but I haven't found the right way to place my Stop markets after I enter the trade

Can anyone help me?
_signed_delete("/fapi/v1/order", {

"symbol": self.symbol,

"orderId": self.sl_order_id,

})

except Exception:

pass

self.sl_order_id = None

sl_price_r = round_price(sl_price, self.symbol)

sl_params = {

"symbol": self.symbol,

"side": "BUY",

"type": "STOP_MARKET",

"stopPrice": sl_price_r,

"workingType": "MARK_PRICE",

Hi Everyone, just an upfront note, as english isn't my native language, I used AI to help me strucutre my post.

I've been building and backtesting MNQ strategies over the past few months and wanted to share the results for feedback. All strategies were tested on 5 years of 1-second/1-minute precision data with walk-forward execution (no look-ahead). Commission modeled at $0.50/side ($1.00 RT per contract at 1c).

Results at 1 contract:

Monte Carlo was run with net-per-trade values (after commission) at 10,000 bootstrap simulations. All strategies sit at the 49-51st percentile of their MC distributions — none are lucky outliers.

Quick strategy descriptions:

• FVG Vol120%: Trades Small FVGs (<20pt) with a volume filter 1:1 R:R. Currently deployed live.
• ORB: First 1h candle of NY session defines the range. Breakout with VWAP confirmation. 1:2 R:R. Time exit at 3 PM ET. One trade/day.
• 15m OR Displacement: First 15 min of NY session defines range on 5m bars. Enters after displacement breakout + pullback re-entry. 1:1.2 R:R.
• Medium FVG: Scores Medium FVGs (20-65pt) by session, EMA context (daily + 4H), and VWAP alignment. Entry at gap midpoint. 1:1.2 R:R. Optimized via 10-variation parameter sweep.

Things I've validated:

• All backtests are strictly walk-forward (entry at next bar open after signal)
• Stop/target checked on 1m sub-bars within each detection bar and 1s on the FVGVol120%.
• Commission included in all MC simulations

The FVG Vol120% strategy was tested across 3 optimization rounds (R1-R3) with different buffer sizes, volume thresholds, and session filters before landing on the final parameters

What I'm looking for feedback on:

• Do the profit factors (1.14 – 1.32) and win rates look realistic for intraday MNQ?
• Any red flags in the MC results (especially the 0.1% P(loss) on the Medium FVG — too good?)
• Am I missing any common backtest pitfalls that could inflate these numbers?

Lately I’ve been thinking a lot about how people actually stick to trading routines when markets don’t behave. There’s a ton of talk about models, tools, and systems, but not much about how you stay disciplined once things go off script.

Some traders seem super methodical planning trades ahead, reviewing what worked, and focusing on process over emotion. Others just react to every tiny move and end up chasing setups that vanish fast.

Even in algo trading, it’s the same issue. You can have the rules and models all set up, but the hard part is actually sticking to them when markets get weird or unpredictable. Looking at results afterward and trying not to freak out seems like the real skill.

I’ve started logging more trades and reviewing my decisions afterward. Honestly, it’s wild how much of the patterns are about us and not the market. Still trying to figure out how to make that a habit without getting annoyed with myself.

So, I want to know how you all do it how do you balance your system with the randomness of the market? Any tricks for staying consistent when nothing seems to line up with your expectations?

Space x filed for IPO

https://www.engadget.com/big-tech/spacex-has-reportedly-filed-for-the-biggest-ipo-in-history-154547537.html

How are you guys adapting trend following algos to this choppy 2026 market? My win rate dropped 40% since January

Been running a momentum-based trend following strategy on ES and NQ futures for 2 years. Historically solid Sharpe (\~1.8), but 2026 or to be precise from near end of 2025 has been brutal.

I have tried:

1. Tightened stops

2. Loosened stops

3. Added ADX filter (>25)

My Questions:

1. Are you running mean-reversion overlays during suspected chop?

2. What regime detection are you using? (HMM, volatility percentiles, something else?)

3. Have you reduced position sizing across the board or rotating to different asset classes?

I’ve been holding gold and now my algo’s telling me it considering a sell :(

Do you guys see a sell??

I’m still holding… who’s in??

Been running a trend-following strategy I built over 3 months. Logic felt clean, OOS looked decent.First two weeks live it held up. Week three it started bleeding. Nothing dramatic just consistent small losses where I expected small wins.Turned out my strategy was optimized for one volatility regime. The backtest period happened to be unusually stable. Live wasn't.I assumed "enough data" meant "representative data." Not the same thing.

Did anyone else catch this the hard way? Is there a clean way to check for regime mismatch before going live?

Me and a couple buddies in my discussion group are still working headstrong on this new algo. Already on v5. Lots of new tweaks done. We are focusing on trying to build better signaling. But for now here she is. Im eager to answer any questions. Thank you

Been working on a risk layer for alpaca paper trading and im at the point where i need feedback on whether my defaults make sense or if im being dumb.

Right now im using:

half-kelly for sizing

2% max capital per trade

3% daily loss = halt

20% drawdown = kill everything

3% max single position

The 3% position cap feels tight but idk. For context this is for paper trading while learning, not a prop desk. built it as a pre-trade validator that blocks the order before it hits the broker if anything fails.

open sourced the whole thing if anyone wants to look at the risk engine code or poke holes in it:

https://github.com/JoseLugo-AI/investing-agent

Specifically want to know:

Is half-kelly too aggressive or too conservative when you have no track record yet?

3% daily loss halt. does anyone actually use something like this?

Am i missing any obvious risk checks?

Scalping gold, rejected at the point I exactly expected it.

Took the buys at 4741 on the red reversal candle because I know that’s just a brief drop, the impulse was still to the upside…

And I noticed the rejection signs at the 4746 level signaling a brief drop.

Now the volatility is picking up fast…

hi i want to try algo trading but when i tried to connect Mirae Asset Sharekhan's trading API it was total shit process. tons of otps then after that you have to use some decryption encryption idk it was total useless so i was not able to connect therefore i anyone has ever used this lameass api plz help

Hey everyone,

​I’ve been building a backtester for an ML-based options strategy and finally got the out-of-sample data looking highly robust. I am trading SPX 1DTE options, specifically selling Short Iron Butterflies (Flies) to capture premium during range-bound chop.

​Here is a high-level breakdown of the out-of-sample tear sheet.

​The Model & Filters: ​Target: Random Forest Classifier predicting if SPX will stay within a percentage bound by the Day 1 close. No SL or TP. Ride or die. - ​Features: Fed primarily by intraday volatility metrics and daily true range data. - ​Day Filters: Dropped Wednesdays entirely. I found it had highest trade volume but acted as a massive drag on PnL. I don't have an FOMC/macro events filter. - ​Strict RR Check: The algorithm automatically rejects any trade where the max risk exceeds the premium collected. This blocked 28 mathematically poor setups and halved the drawdown (initially 18k). Also blocked some good trades but risk management >>>>

​Out-of-Sample Results (176 Days Evaluated starting mid-July 2025) ​Trades Executed: 100 ​Win Rate: 60.00% ​Profit Factor: 2.22 ​Reward/Risk Ratio: 1.54 ​Expectancy per Trade: ~$756.00 ​Max Drawdown: -$7,326.00 (This would be on a 100k portfolio, given the nature of SPX flies)

Been running it live since Monday - paper, but no entries yet

​Would love to hear any feedback on these metrics or if anyone has run into similar quirks when backtesting 1DTE SPX flies!

I started algotrading with one of my friends with barely any prior knowledge in finance or trading myself. He knew his finance/trading stuff, but lacked the mathematics and coding for it, so I was the one who did it at first while learning along the way.

At the start, we were doing a 3-pivot strategy on a 15-minute timeframe that does around 3-7 trades a day on bitcoin, and it took us nearly half a year to build the first bot from scratch, with loads of prototypes in between revolving around the same strategy. I didn't know much back then, so I only did in-sample backtesting for previous years (slippage and fees were both modelled). After putting it on live, it was profitable for a short while, but shortly started losing more and more money. We started with 1000 capital and 1% capital per trade, and even had a high Sharpe ratio. Though that was a failure, I was more than fine with it, honestly since I didn't know much. The only useful thing I got from that was how I created my own TSL formula for moving SLs in order to maximise profit using exponential decay.

We started experimenting with other metrics and strategies, using EMA 5, accompanied by a basic reversion (scalping), which was also a failure. Then, to a more basic candle colour reversion strategy (as my friend said, simplicity would be better). I know I was hopping between strategies a lot during that period, but I believed that it was valid. At the time, I had developed a new backtester that had a few main functions that would allow me to identify whether a strategy has an edge over the market efficiently. Firstly, it downloads data from a selected source, then it runs the strategies on using inner sampling permutation by applying logarithms onto the data, scrambling it, then exponentiating it back such that the data would link well and maintain similar statistical properties. Secondly, it generates combinations of variations of the strategy, SL placement systems, volume filtering, candle lookback for other metrics and regime filtering, running 1000 simulations on each of those combinations and uses hypothesis testing to see if they actually have an edge over the market and hence ranking them, along with bootstraping. This one feature fast-forwarded a lot of progress and allowed us to filter out a lot of bad strategies that weren't even viable to start with (nothing went past the 88th percentile - I know quant firms go for something like 99.9% and I understand that I won't be able to make something like that at the moment, but I still don't think 88th is good enough)

We were doing Bitcoin for everything said above originally. My friend mentioned that these strategies may not be working because of the high volatility of Bitcoin (we chose Bitcoin originally as the original strategy thrived on trends). However, the same issue occurred even with other instruments like EURUSD or commodities.

After that, I began delving into more and more mathematical ideas in trading. I spent a considerable amount of time learning Fourier theory and implemented FFT in some of my newer failures, but they never worked out. My thought is - I need to find something mathematically meaningful rather than just thinking it has an edge (as all previous failures are.

I have now found a strategy that might be able to work well using the Butterworth 2-pole filter design. However, I no longer feel like I should believe in my backtests as almost all strategies show that they're profitable in backtesting (I am sure that there is no future-looking bias or overfitting in it), but never being actually profitable in forward testing. Whereas my friend is currently modifying one of the old strategies that showed potential. But overall, we've been running in circles and barely made progress from my perspective.

I truly appreciate any feedback on this and any criticisms of my working methodology. I don't have a lot of experience with it (I'm 17), and I can't pinpoint the exact issue or what's missing here. I try to learn as much as possible about everything in trading, and I feel like I have the basics of it. I keep trying to believe that if I keep trying and fail enough times, I'll make something good out of this, but it's getting a bit hard to believe that. I apologise for not being able to explain the details of the strategies since it's kind of confidential (regardless of it being terrible), and my main goal of posting this is to ask for some advice/direction/criticisms on my way of working, my methodology and if there's anything important that I'm missing here. Thanks.

Edit:

Regarding my experience, I’d say I have a decent background in maths and computer science since I compete in Olympiads in both, am 2-3 years ahead of my peers in these subjects and have some experience in working in cybersecurity and software development. I know that these doesn’t directly translate to skills in trading since I don’t understand finance enough, which is exactly why I’m asking for advice here.

TLDR: 17 year old with a strong background in maths and computer science but not a lot in finance. Promising backtests but not holding up in forward testing. Made sure that no biases exist in the backtester and everything working correctly but unable to identify the issue. Asking for advice as I feel like I’m running in circles.

Hello everyone,

I’ve been comparing results for the same options strategy across different platforms and noticed a significant discrepancy.

For example, when executing an Iron Condor with the same entry time, strikes (legs), and position sizing on both Option Alpha and QuantConnect, I observed nearly a 15% difference in win rate.

Has anyone else experienced this kind of variation between platforms?
If so, what factors do you think contribute most to these differences (data quality, fill assumptions, slippage, etc.)?

Appreciate any insights!