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How I Built a Real-Time Nifty 50 Forecast Accuracy Engine — And What It Taught Me- self service tool for intraday trader
▲ 18 r/algotradingcrypto+11 crossposts

How I Built a Real-Time Nifty 50 Forecast Accuracy Engine — And What It Taught Me- self service tool for intraday trader

Most market forecasters have the same problem.

They post a forecast in the morning. The market closes. They move on.

Nobody measures. Nobody improves.

I decided to change that.

The Problem With "I Was Right"

After years of analyzing Nifty 50 intraday movements, I realized something uncomfortable.

I could look at my forecast at 3:30 PM and say "I got the direction right." But that told me almost nothing useful.

Was I right at 9:15 AM or only after 2:00 PM? Was my model 10 minutes early or 10 minutes late? Did I get the morning session right but miss the afternoon? Was Model A better than Model B today — and by how much?

These questions had no answers. Until I built something to answer them automatically.

What I Built

A real-time Nifty 50 forecast accuracy engine that runs , updates every minute during market hours, and computes 30 different metrics automatically.

It looks like a standard chart. But under the hood it is doing something most trading tools don't do — comparing forecast shape against live market data, minute by minute, all day long.

Here is what it tracks:

Correlation metrics:

  • Full day Pearson correlation
  • Last 60, 30, 15 and 5 minute rolling windows
  • Best matching 30-minute window of the day
  • Worst matching 30-minute window of the day

Direction accuracy:

  • Overall up/down direction match percentage
  • Up move accuracy separately
  • Down move accuracy separately
  • Longest correct direction streak
  • Current streak at any moment

Magnitude accuracy:

  • Average error per bar in points
  • Percentage of bars within 5, 10 and 20 points
  • Maximum error (worst single minute)

Time shift detection:

  • Is the forecast running early or late vs actual?
  • By how many minutes?
  • At what shift does correlation peak?

Session analysis:

  • Morning session match (9:15 to 12:00)
  • Afternoon session match (12:00 to 15:30)

Trend accuracy:

  • Did forecast predict the right day direction?
  • Did it catch the peak within 30 minutes?
  • Did it catch the trough within 30 minutes?
  • How close was the forecast high vs actual high?
  • How close was the forecast low vs actual low?
  • End of day accuracy

Overall:

  • Composite weighted score
  • Automatic ranking when running multiple models

The Discovery That Changed Everything

The most surprising metric was time shift.

For weeks my correlation scores looked decent — around 65 to 70 percent. I thought that was reasonable. Then I added time shift detection.

It showed my model was consistently running 10 to 15 minutes ahead of the actual market.

The forecast shape was correct. The timing was off.

Once I knew that, I could account for it. Within two weeks my full day correlation jumped from 68 percent to 81 percent — not because my model got better, but because I finally understood how it was wrong.

You cannot fix what you cannot measure.

Running Multiple Models

The second insight came from comparing models side by side.

I run three different forecast approaches each morning. Before this tool I would look at them visually and pick the one that "felt" most reasonable.

Now I have a comparison table. Every metric. Every model. Automatically ranked.

Some days Model A wins on correlation but Model B wins on direction accuracy. Some days one model nails the morning session while another gets the afternoon right.

The table shows exactly where each model is strong and where it falls apart. That is information you cannot get from looking at lines on a chart.

The chart itself has full interactions — hover tooltips, crosshair, zoom, pan, timeframe switching from 1 minute to 30 minutes, moving averages. What the Hover Shows

When you move your cursor over the chart you see:

  • Exact time label
  • Live Nifty value at that minute (change from open)
  • Each forecast model value at that minute
  • Difference between actual and forecast in points

In the analysis table every cell highlights the best performer in green. You can see at a glance which model is winning, which metric each model leads, and what the composite score is right now.

What This Is Not

This is not a trading system. It does not give buy or sell signals.

It is a measurement and improvement tool. Its job is to tell me honestly how accurate my forecast was today — in 30 different ways — so I can understand my model better and improve it over time.

The goal is not to be right every day. The goal is to understand exactly how and when and why I am wrong, so the model gets better over time.

What Is Next

will update and have real time from Monday or whatever possible at earliest

The Bigger Point

Anyone can post a forecast. Very few people measure it rigorously.

If you are serious about market forecasting — intraday or otherwise — you need a measurement system as rigorous as your forecasting system.

Otherwise you are flying blind and calling it analysis.

Build the feedback loop. Measure everything. Improve systematically.

That is how forecasting becomes a skill rather than a guess.

*I publish daily Nifty 50 intraday forecasts along with real-time accuracy tracking. Follow for updates on methodology, results and the ongoing development of this tool.*They post a forecast in the morning. The market closes. They move on.

Nobody measures. Nobody improves.

I decided to change that.

The Problem With "I Was Right"

After years of analyzing Nifty 50 intraday movements, I realized something uncomfortable.

I could look at my forecast at 3:30 PM and say "I got the direction right." But that told me almost nothing useful.

Was I right at 9:15 AM or only after 2:00 PM? Was my model 10 minutes early or 10 minutes late? Did I get the morning session right but miss the afternoon? Was Model A better than Model B today — and by how much?

These questions had no answers. Until I built something to answer them automatically.

What I Built

A real-time Nifty 50 forecast accuracy engine that runs , updates every minute during market hours, and computes 30 different metrics automatically.

It looks like a standard chart. But under the hood it is doing something most trading tools don't do — comparing forecast shape against live market data, minute by minute, all day long.

Here is what it tracks:

Correlation metrics:

  • Full day Pearson correlation
  • Last 60, 30, 15 and 5 minute rolling windows
  • Best matching 30-minute window of the day
  • Worst matching 30-minute window of the day

Direction accuracy:

  • Overall up/down direction match percentage
  • Up move accuracy separately
  • Down move accuracy separately
  • Longest correct direction streak
  • Current streak at any moment

Magnitude accuracy:

  • Average error per bar in points
  • Percentage of bars within 5, 10 and 20 points
  • Maximum error (worst single minute)

Time shift detection:

  • Is the forecast running early or late vs actual?
  • By how many minutes?
  • At what shift does correlation peak?

Session analysis:

  • Morning session match (9:15 to 12:00)
  • Afternoon session match (12:00 to 15:30)

Trend accuracy:

  • Did forecast predict the right day direction?
  • Did it catch the peak within 30 minutes?
  • Did it catch the trough within 30 minutes?
  • How close was the forecast high vs actual high?
  • How close was the forecast low vs actual low?
  • End of day accuracy

Overall:

  • Composite weighted score
  • Automatic ranking when running multiple models

The Discovery That Changed Everything

The most surprising metric was time shift.

For weeks my correlation scores looked decent — around 65 to 70 percent. I thought that was reasonable. Then I added time shift detection.

It showed my model was consistently running 10 to 15 minutes ahead of the actual market.

The forecast shape was correct. The timing was off.

Once I knew that, I could account for it. Within two weeks my full day correlation jumped from 68 percent to 81 percent — not because my model got better, but because I finally understood how it was wrong.

You cannot fix what you cannot measure.

Running Multiple Models

The second insight came from comparing models side by side.

I run three different forecast approaches each morning. Before this tool I would look at them visually and pick the one that "felt" most reasonable.

Now I have a comparison table. Every metric. Every model. Automatically ranked.

Some days Model A wins on correlation but Model B wins on direction accuracy. Some days one model nails the morning session while another gets the afternoon right.

The table shows exactly where each model is strong and where it falls apart. That is information you cannot get from looking at lines on a chart.

The chart itself has full interactions — hover tooltips, crosshair, zoom, pan, timeframe switching from 1 minute to 30 minutes, moving averages. What the Hover Shows

When you move your cursor over the chart you see:

  • Exact time label
  • Live Nifty value at that minute (change from open)
  • Each forecast model value at that minute
  • Difference between actual and forecast in points

In the analysis table every cell highlights the best performer in green. You can see at a glance which model is winning, which metric each model leads, and what the composite score is right now.

What This Is Not

This is not a trading system. It does not give buy or sell signals.

It is a measurement and improvement tool. Its job is to tell me honestly how accurate my forecast was today — in 30 different ways — so I can understand my model better and improve it over time.

The goal is not to be right every day. The goal is to understand exactly how and when and why I am wrong, so the model gets better over time.

What Is Next

will update and have real time from Monday or whatever possible at earliest

The Bigger Point

Anyone can post a forecast. Very few people measure it rigorously.

If you are serious about market forecasting — intraday or otherwise — you need a measurement system as rigorous as your forecasting system.

Otherwise you are flying blind and calling it analysis.

Build the feedback loop. Measure everything. Improve systematically.

That is how forecasting becomes a skill rather than a guess.

I publish daily Nifty 50 intraday forecasts along with real-time accuracy tracking. Follow for updates on methodology, results and the ongoing development of this tool.

u/Potential_Leek_4814 — 16 hours ago
▲ 2 r/algotradingcrypto

Has anyone actually built a profitable trading workflow around Claude or ChatGPT… Please, if you have more than 3 months of consistent trading history with real money…

Not asking whether AI can write code.

I’m curious whether anyone is using an LLM as part of a live trading pipeline that has remained profitable over time.

Where does it genuinely add value?
Research
Feature engineering
Strategy generation
Risk management
Trade execution
Market regime analysis

Where does it completely fall apart, if you have tried & failed or hav made consistent profit for more than 3 months of trading?
Interested in hearing from people running live systems rather than paper trading. No backtesting results please..

reddit.com
u/IMAK82 — 16 hours ago
▲ 4 r/algotradingcrypto+1 crossposts

Can Turtle Trading actually work on crypto futures? Early live data

I've been building a crypto Turtle Trading system for the last few months and recently started running it live on KuCoin Futures.

The idea isn't to become another "buy my signals" channel.

The goal is to build a fully transparent systematic trading project in public:

  • Trading engine generates signals (and execute orders for my Account)
  • Service distributes and stores events
  • Daily Turtle breakout strategy (only 55d breakout)
  • ~25 USDT futures markets monitored (only Markets with volume >5.5mln over 6months)
  • Public Telegram channel publishing every valid signal
  • No manual cherry-picking

I've been manually executing signals for ~1.5 months to observe behavior before enabling full automation. After all open position will reach exit level fully automated trading will start.

Current sample:

Wins: +6.5R (HYPE) +5R (BCH) +3.3R (WLD) +1R (SOL)

Losses: Mostly -2R standard stop losses

Still open: ETH +2.5R SUI +1R

Some observations so far:

  • Win rate is low (expected for Turtle systems), about 30%
  • Big winners are carrying the system
  • Crypto market structure behaves differently from classic Turtle markets (a lot of whipsaw, that's why I increased the minimum volume avoiding easly manipulated Markets)
  • I'm still experimenting with pyramiding and risk management (without fully automation I wasn't able to enter all the additional entries)

Not selling anything. Just documenting the process publicly and collecting feedback from people who have experience with systematic trading and trend following in crypto.

Curious if anyone here has experimented with Turtle-style systems on crypto futures.

Happy to share results and lessons as the sample grows.

If anyone wants to follow the project evolution and signal observations (and help with feedbacks), I post the public Telegram channel where every signal is published automatically. Link in comments.

reddit.com
u/Mitchy764 — 1 day ago
▲ 8 r/algotradingcrypto+3 crossposts

Wundertrading multi pair grid bot

Hello everyone,

For several weeks now, I’ve been running this multi-pair bot in demo mode on the Wundertrading platform with $10,000. Despite the unfavourable market conditions, the bot has performed reasonably well, as you can see from the screenshots. I’d like to ask the community what they think.

u/Background-Country49 — 1 day ago
▲ 14 r/algotradingcrypto+4 crossposts

Caught the SPCX fade on Canborsa

SPCX looked overheated the moment it ripped from $135 to $225. The pullback to around $161 was ugly, but not surprising.

That kind of move usually has a simple story behind it. Hype gets retail excited, early holders get liquidity, and once the momentum fades the air comes out fast. People always act shocked when a clean vertical move doesn’t hold. It usually doesn’t.

I shorted it on Canborsa DEX around $220 with 5x leverage once the tape started rolling over. At that point the risk/reward made sense. A 15% drop at 5x is already a 75% return, which is exactly why leverage cuts both ways and why timing matters more than bravado.

The part that stands out is that I could do it onchain directly. Canborsa is the only RWA perp DEX on Canton, so the setup is there for tokenized stocks, commodities, and crypto without KYC or a middleman. That’s the real story, not the price chart.

Who else shorted the top on SPCX and actually caught the move?

reddit.com
u/MDiffenbakh — 1 day ago
▲ 9 r/algotradingcrypto+6 crossposts

I Built a Telegram Bot That Streams My Trading Bot’s Trades in Real Time (noncustodial trading)

I’ve spent the last 2+ years building IMALI as a solo developer.
One feature I’m excited about is the Telegram bot, which streams paper trading activity from my OKX Spot and OKX Futures bots.
In this video you’ll see:
Live paper trade alerts
Entries and exits
Spot and futures activity
Profit/loss updates
Strategy decisions as they happen
I built it because I wanted users to see how the bots behave before risking real money.
The goal isn’t to promise unrealistic returns—it’s to make automated trading easier to understand through transparency and real-time notifications.
If you’re curious, I’d appreciate your feedback.
You can also try the one-click demo and paper trading yourself:
https://imali-defi.com
What would you want to see in a trading bot’s Telegram alerts that most platforms don’t provide?

u/Agile_Strategy_223 — 2 days ago
▲ 6 r/algotradingcrypto

I ran 300+ paper trades on pump.fun graduates and then tested every "obvious" entry filter against the data. Almost everything was noise — here's what actually moved P&L.

I've been running a bot paper trading freshly graduated pump.fun tokens for a few weeks. Just over 300 closed trades now. I got sick of tweaking settings on gut feel so I sat down and actually tested every filter I believed in against the trade history. Most of what I believed turned out to be rubbish, so posting the numbers in case it saves someone else the time.

Stuff I was sure about that turned out to be noise:

Token names/themes. I bucketed 2,400+ tokens (animal coins, celebrity, politics, AI, crude jokes) and checked how many ever did a 2x. Base rate was 23%. Animal coins 24%. Celebrity 24%. None of the buckets separated from the base rate by anything you could trade. The only pattern in the actual monster winners was names tied to a live news moment, and you can't detect that from a wordlist.

Market regime. Built myself a daily heat index, basically what % of new tokens hit 2x that day. The index is real, it fell from 29% to 16% over two weeks. But correlation with my own daily P&L was -0.09. My best day landed on a hot day, my worst day landed on the hottest day of all. If your losses come from your exits, the tide doesn't save you.

Time of day. 13:00-14:00 UTC genuinely is the worst window in the wider data (13-17% hit rate vs 34% at the best hours). I was convinced this was my edge. Then I simulated actually gating my own trades by hour and it came out slightly worse than doing nothing, because it filtered winners at the same rate as losers.

Hard take profit. Simulated a flat +25% TP across all my trades. Made everything worse, net went from -0.26 SOL to -1.20. About 39% of trades did touch +25%, but the ones that ran past it are the entire book. One went +490%. Cap those and there's nothing left to pay for the losers.

What the data actually pointed at instead: my losers, not my winners. 54% of losing trades never went green at all, I was buying things already rolling over. Another third went +10% and then got chopped. Splitting the stop loss into two modes (tight until a trade proves itself, wide and trailing after) flipped the same trade history from -0.26 to +3.7 SOL in backtest. Live it's less pretty, thin books gap straight through stops, my -10% stops actually fill around -15%. Still testing it forward before I trust it, small sample so far.

The honest summary is every entry filter I tested had a lovely story behind it and none survived contact with the data. The only edges I've found so far are in exit mechanics and in not buying tokens that are already dying.

Anyone here actually found an entry-side signal on fresh launches that held up out of sample? Genuinely asking, mine all died.

u/paulf280 — 3 days ago
▲ 5 r/algotradingcrypto+2 crossposts

Finally happy with this thing

Spent the last few weeks tweaking the entry logic on the ICT indicator. The old version was triggering too late – price would already be moving away from the zone by the time I got the signal.

New version detects wick rejections inside the candle. It's basically the same strategy but with much better timing.

TP and SL levels also fixed. Signals are cleaner, stops are tighter, increasing the strategies R:R.

If you're one of the people testing it, you'll see the update. Let me know what you think.

u/benchpress1oo — 4 days ago
▲ 8 r/algotradingcrypto+7 crossposts

One of my favorite features isn’t AI… it’s the Start/Stop button.

That might sound strange, but I built IMALI so users stay in control.
When you press Start, the platform begins scanning markets based on your selected strategy. It looks for opportunities that meet the strategy’s rules and risk parameters before considering a trade.
When you press Stop, the bot stops opening new positions. You stay in control instead of wondering what the software is doing.
A few other controls I built because I wanted them myself:
• Switch between Paper and Live Trading in seconds.
• Choose your own trading strategy based on your risk tolerance.
• Set your preferred market (Crypto, Stocks, Futures, or DEX where supported).
• View every trade from one dashboard.
• Connect or disconnect your exchange whenever you want.
My goal wasn’t to create a “black box” bot.
It was to build software that helps people understand what their automation is doing while giving them the ability to take over at any time.
If you’re curious, you can try the one-click demo here:
👉 https://imali-defi.com/trade-demo
I’d love your feedback.

u/Agile_Strategy_223 — 4 days ago
▲ 5 r/algotradingcrypto+1 crossposts

I built a Bitvavo trading bot with EMA + ADX + RSI filters and backtested it on 2.5 years of data — here's what I found

I spent the last few months building and refining an automated

trading bot for Bitvavo. Here's what I learned from backtesting

it on 2.5+ years of historical data.

**The strategy**

- EMA 10/50 crossover for trend signals

- ADX filter (min 25) to avoid sideways markets

- RSI filter (max 70) to avoid overbought entries

- 4 hour cooldown after each sell

- Stop-loss at -3%, take-profit at +6%

**Backtest results (ETH-EUR, 2.5 years)**

- Strategy: +14.0%

- Buy & Hold: -37.6%

- Win rate: 75%

The ADX filter made the biggest difference. Without it, the bot

was getting whipsawed in sideways markets and losing on almost

every trade. Adding it dropped the number of trades significantly

but pushed the win rate from 22% to 75%.

SOL didn't work well with this strategy at all (-13% vs +275%

buy & hold) — too volatile for trend following. Moved it to a

grid bot instead.

The bot runs 24/7 on a cheap VPS with Telegram notifications

for every trade.

Happy to answer questions about the strategy or implementation.

reddit.com
u/MarcRietdijk — 4 days ago
▲ 4 r/algotradingcrypto+2 crossposts

TImeFramed Variable Breakout Strategy Backtest Results & Forward Test Init

TL;DR: Backtested a fractal breakout strategy over various date ranges for many, many assets. Currently I'm going to deploy this live for some extended forward testing on Solana on the 5 minute timeframe. 1.3 Sharpe, 8.96% max drawdown, 114/398 winning trades since Feb 2026. Code is open source. Starting live forward test today. Will post 15/30/60-day updates with real results, good or bad.

The idea

This strategy looks for Bill Williams fractals as points of contention and breakout opportunities. It enters when price crosses above a fractal that you design, and price is also above (or below for shorts) a volume weighted variable moving average. The thesis is to capture micro trends with simple entry logic.

Methodology

  • Instrument(s): Literally any. This strategy is super robust.
  • Timeframe: Depending. Indices like to have shorter time frames, 5m-1hr. Yet Crypto likes higher time frames like 4h or 8h.
  • Backtest period: Feb 8 2026 – June 30 2026 ([X] years/months)
  • Entry rule: BW fractal crossover (x candles before must completely be below high of target candle, y number of candles after target must also be fully below) High of target candle is held in memory and when price crosses above that price, and price is above the variable moving average, enter position. Pyramiding is allowed in this version of the strategy.
  • Position sizing: 100% of equity
  • Slippage/commission assumptions: slippage: 1 tick. Commission was not factored in for this particular backtest.

Results

Metric Value
Total return 21%
Sharpe ratio 1.309
Max drawdown 8.96%
Win rate 34.76%
Profit factor 1.265
Number of trades 328
Avg trade duration 14 five minute bars

https://preview.redd.it/tavvszqs0nah1.png?width=1394&format=png&auto=webp&s=e207b97caeb94e97f14dd6b271f1c1e259896a52

Honest caveats

  • Overfitting risk: Strategy remains surprisingly robust over many different backtesting regimes, securities, timeframes. This particular backtest is definitely overfit though.
  • Sample size: Again, shown backtest isn't really enough to show that this is worth a damn, but you could customize this as much as you'd like given the code is free to use.
  • Regime dependency: thorough regime resilience.
  • What would make this strategy fail? looking back through losses, the biggest chink in this strategy's armor is the tendency to reverse. Finding good balance between entering ALL fractal breakouts and the right ones can be difficult. these pivot points are pivot points for a reason. this strategy struggles in ranging markets without the random walk + upwards or downwards.

What's next

The real test is live money reacting to live conditions. Starting today I'm running this forward on a dedicated Alpaca account so the numbers are separated from my other strategies and easy to audit.

I'll post updates at 15 days, 30 days, and 60 days with unedited performance — win or lose.

I'm implementing this (the alert → broker wiring) using a tool I built called Algorelay. Mentioning it since it's how I'm running the forward test and connecting this to alpaca, not because this post is trying to sell it. Full strategy code is free and open source regardless of what you use to run it. But if you just want the pinescript without having to copy and paste the cocde I've published the strategy on TV as well: https://www.tradingview.com/script/iBDdbEvy-TImeframed-Variable-MA-Breakout/

Code: https://github.com/AlgoPulse-Research/pine_library · License: MIT · Forward test account: [Alpaca account nickname, e.g. algorelay-strat-00X] · Questions/pushback welcome, that's the point of posting the honest numbers.

reddit.com
u/Alternative-Two-5300 — 5 days ago
▲ 3 r/algotradingcrypto+2 crossposts

My Trading Bot Was "Broken" for a Week. It Wasn't the Strategy.

I spent a week debugging my automated crypto trading bot, thinking the strategy was failing. It wasn't. The strategy was fine. The data was lying to it.

What I built

An automated trading platform that scans hundreds of crypto pairs, enters on momentum, and exits with stop-losses, take-profits, and trailing stops. Standard stuff. The logic was solid.

But for a week, it looked like it was bleeding money. Positions weren't closing. Trades were looping. The dashboard showed positions that didn't exist. Profits were invisible.

What was actually happening

The database and the exchange had diverged. Badly.

  • Phantom positions: Trades that had been closed on the exchange days ago were still marked "open" in the database. The bot kept trying to sell them. Every attempt failed silently because there was nothing to sell. It retried every seven seconds. Forever.
  • Orphaned positions: Real holdings on the exchange had no corresponding database record. The bot couldn't see them, couldn't manage them, couldn't set stop-losses. They just sat there.
  • Multiple bots fighting: The spot bot, futures bot, stock bot, and DEX bot all shared one account. They each had their own idea of what positions existed. None of them agreed.
  • Silent failures: When a sell order failed because the position didn't exist, the bot didn't recognize the error message. It just tried again. And again. And again.

The strategy wasn't wrong. The data was corrupted. The bot was making decisions based on information that had nothing to do with reality.

The fixes (in order of impact)

1. Database reconciliation
Every cycle, the bot now compares what's in the database to what's actually on the exchange. Positions that don't exist on the exchange are closed in the database. Positions on the exchange that aren't in the database are created. This alone fixed most of the looping.

2. Error recognition
The exchange returns "All operations failed" when you try to sell something you don't have. The bot now recognizes this and closes the database position instead of retrying forever.

3. Single source of truth
Stopped the futures, stock, and DEX bots. Only the spot bot trades now. One bot, one account, one set of positions. No more conflicts.

4. Cleaned the database
Closed 114 stale positions that had been dead for days. Created new records for the 5 actual holdings. The bot finally had accurate information.

The result

The bot went from "broken" to "slightly profitable" without changing a single line of strategy code. Same entry logic. Same exits. Same risk management. Just accurate data.

July 1st was the first full day with clean data: 57 trades, 48% win rate, breakeven overall, but the last 6 trades were net positive. The trend is up.

The lesson

Most trading bot failures aren't strategy failures. They're infrastructure failures.

Your model can be perfect. Your backtests can be beautiful. But if the bot doesn't know what positions it actually holds, none of that matters.

Data integrity is the unsexy foundation that everything else depends on. Get that right first. The rest follows. DM me for access.

reddit.com
u/Agile_Strategy_223 — 4 days ago
▲ 4 r/algotradingcrypto+1 crossposts

Biggest real world problems that make arbitrage bots fail despite looking profitable on paper?

I'm a 2nd year CS student building a real-time crypto/forex arbitrage detection engine as a learning project.

The core idea is to model currencies as a graph and use Bellman-Ford to detect profitable cycles. I'll also account for trading fees, spreads, and slippage. Its a earning project, nothing like overnight money printing bot.  

Before I go too far, I'd love to hear from people who have actually built or worked with trading systems.

What problems did you run into that aren't obvious from tutorials or research papers?

Things like:

  • Transfer costs
  • Latency
  • Liquidity
  • Anything that made a seemingly profitable opportunity impossible to execute

I'd like to incorporate as many real-world constraints as possible into the project, so I'd really appreciate any lessons or horror stories.

reddit.com
u/not_69lover — 5 days ago
▲ 2 r/algotradingcrypto

I tested RSI and volume divergences ~2,200 ways on BTC. Zero beat random.

Divergence trading is everywhere in crypto: price makes a higher high, RSI makes a lower high, 'momentum is fading, short it.' I wanted to actually know if it works (and if), so I tested it about as hard as I could.

8 variants (regular + hidden, bull + bear, RSI + volume), 6 timeframes from 5m to 1D, 4 confirmation delays, walk-forward over 6+ years of BTC, significance from permutation tests. About 2,200 configurations total. Sounds a lot, combining those adds up real quick.

Pure chance would hand you roughly 220 'significant' looking results at p < 0.10. I found 4. At p < 0.05: zero.

The prettiest one (+1,990 bps on the daily) was n=8 signals, p=0.31. That is the exact cherry someone picks to sell you a course. I mean, could this be a fantastic edge? Sure it could. But I would not trust it and never ever put real money in a result like this.

My best guess why: the divergence isn't the signal, the pivot is. If you systematically enter at local highs and lows, your odds are structurally worse than random.

To be fair, this only kills divergence as a standalone entry. As one factor inside a specific regime it might add something, I haven't tested that. And also, there might be other ways to bring the idea of divergences into code.

Has anyone actually walk-forwarded divergences and gotten a different answer? Genuinely curious.

reddit.com
u/espressodoppioo — 5 days ago
▲ 2 r/algotradingcrypto+1 crossposts

Looking for Beta Testers &amp; UX Enthusiasts

I’m currently looking for a couple of people to test the new UX/UI I’ve been building.

A few months ago, I started working on a new experience for traders in general. There are still many things to improve, but I’m reaching a stage where I’d like to share it with a few people and get honest feedback.

I’m still working on the chat composer and a couple of other features. Most of the core actions are already implemented, but of course it takes time if it should feel good and useful.

The goal of the platform is to let users train their own models like SAC, PPO, and later Dreamer directly on the platform, compare them, backtest them, and eventually deploy them in a controlled way.

Another dedicated feature is the chat composer, where AI can help analyze charts, draft trading plans, set SL/TP ideas, long/short ideas, and support the trading workflow a bit like Codex, but for charts and trading.

I’d be really happy if someone wanna try this kind of tool and give feedback. Any feedback is welcome.

If you’re interested, feel free to send me a DM. I’ll try to answer asap.

I’m also looking for other founders, builders, or early users who want to help shape this into a real alternative to the established trading tools — something more open, more intelligent, and more affordable.

Current overview

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u/Accomplished-Car8427 — 6 days ago
▲ 45 r/algotradingcrypto+2 crossposts

3000$ challenge done

I've been working on my strategy for almost a year now, backtesting, criticising,rebuilding, and repeating when I started see positive results and getting an edge over the market, I asked a question, can my strategy survive a 100$ account, as you see I started the live testing on a demo account it struggled at first considering the market state in that period of the year but it did it 3000$ in a month, so I decided to learn from my mistakes and I made a pinescript to fully automate the process and turn off feelings and overthinking trades.

This is not an advertisement, but if anyone is interested in using the indicator and giving a feedback youre welcome to DM me. I want you to rip that strategy apart and criticise as much as you can

u/benchpress1oo — 10 days ago
▲ 6 r/algotradingcrypto+1 crossposts

Pine script

Hi guys
I hope you are doing well. I want to share something with you. I and claude are working to build a signal giving indicator for gold from the last 2 weeks and today, claude also gave me a pine script which is quite long and full of stuff.
Actually the story starts with i ask the claude about three most profitable strategies in the world. I gave me 3 options and then i asked it to write a pine script for the 1st strategy.
It gave me the script, it might be full of errors.
Because i came from a medical background so i need a nice teammate whom with i can build that indicator if anyone is interested, please dm me.

And it can be your last try to make it, then we can be rich and change the lives of families.

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u/No_Confection_391 — 8 days ago
▲ 4 r/algotradingcrypto+3 crossposts

What’s the biggest mistake you have made, that has cost you the most?

I’m researching how traders think about the future with AI. I’ve noticed algorithmic trading and AI are getting exponentially better at finding patterns and predicting markets.
I’m building tools to help humans stay relevant and make better decisions as AI gets better at trading. So I’m asking:
How do YOU think about staying competitive when AI can do this better? What do you think is the human edge that machines don’t have?
Would love to hear from people actually trading about what the future looks like for humans in this industry.
Any thoughts?

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u/Optimal_Emu3624 — 9 days ago
▲ 6 r/algotradingcrypto+5 crossposts

Solo Developer Ready for all the smoke. My trading bot is finally stable and live

I’m a solo developer building an AI-assisted trading platform, and honestly this project has pushed me harder than anything I’ve ever worked on.
I’ve tested it with my own money.
I’ve run more than 75,000 paper trades.
I’ve rebuilt the UI multiple times because early users were confused.
I’ve fixed exchange API issues, onboarding problems, dashboard bugs, and paper trading flows while still driving Uber to keep things going.
At one point, I almost drove my car into the ground trying to fund the work because I genuinely wanted to build something safer and clearer for users before they ever risk real money.
The platform is called Imali-Defi.
The goal is simple:
Help normal people understand automated trading before they go live.
The system now includes:
✅ one-click demo to start
✅ paper trading before live trading
✅ crypto spot bot
✅ crypto futures bot
✅ stock trading bot
✅ DEX/sniper infrastructure
✅ AI-assisted confidence scoring
✅ strategy selection
✅ risk controls
✅ dashboard analytics
✅ referral system
✅ white-label SaaS architecture
How it works:
Start with the one-click demo
Learn the dashboard
Choose a strategy
Run paper trading with virtual funds
Review trade activity and analytics
Move to live trading only when comfortable
The bots are not “magic AI.”
They use structured rules, indicators, confidence scoring, position sizing, stop-loss logic, trailing stops, and market-condition filters.
I’m currently looking for 100 early users willing to test the platform, give feedback, and help shape the next version.
This is still actively improving, but the core platform is working and the onboarding is much clearer than the earlier beta.
If you’re interested in testing an automated trading platform built around paper trading first, comment or DM me.

https://preview.redd.it/o8ah6ioc49ah1.png?width=1179&format=png&auto=webp&s=7346d3623fe7e84246919a064d86cd3c119c54ce

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u/Agile_Strategy_223 — 7 days ago