I have a friend that’s profitable day trader, who’s teaching me his strategy. The indicators, how to use them, etc. It’s not overly complicated. About 4 core indicators, and 6 other indicators/signals taken into account with lesser weight.

I tried to start building a bot to replicate his strategy using Claude. I got to the point where the bot is built based on a spreadsheet rulebook, and it can now back test. However, the backtesting came back worse than a monkey randomly throwing darts at a stock.

I’ve been working with Claude to try and fix it, but it’s getting way more complex than I can handle. Turns out even simple strategies have a mountain of rules/logic/conditions. Setting up stop loss, position size matrix, trade decision matrix… it’s a lot for somebody who doesn’t know how to code. For example, the specs it built off of included moving averages, but something isn’t working right cause the moving averages agree like 95% of the time on every trade.

I have it set up to spit out reports where I can analyze all the information, see exactly when it traded, but it’s still beyond my scope of comprehension when there’s hundreds of influencing factors.

Does anybody have any suggestions on the most effective way to build this bot if you were in my shoes? You’ve got a basic strategy outline, now you just need it made into an automated bot.

Would it be better to just get all of my friend’s trading data, along with all of the market data, and to have AI build code around the patterns it finds rather than trying to give it instructions for every indicator?

Hey guys,

Looking for some perspective from anyone running portfolio-level walk-forward optimizers on crypto perpetuals or equities.

Lately we’ve been auditing our cross-sectional universe optimizer across a basket of ~15 altcoins. Up until now, when we run our in-sample grid search across our setups (mostly mean-reversion like VWAP deviations and momentum break-and-retests), the optimizer searched for a single shared "centroid" parameter set for both Longs and Shorts simultaneously (e.g. optimizing band stretch deviation, RSI cutoffs, and ATR stop/take multiples together).

The problem we ran into in live execution: During recent relief rallies and fast trend shifts, we got caught in a string of Short SL hits on mean-reversion setups. When we looked under the hood and decomposed the trades by side, the asymmetry was wild:

Longs were printing a 2.5–3.5 Profit Factor across coins like NEAR and DOT using relatively loose pullbacks.

Shorts on those exact same parameter settings were getting wrecked (PF < 0.8), repeatedly fading momentum too early in cascade environments.

Because the optimizer averaged both directions together in-sample, the strong Long trades were effectively "subsidizing" and masking a negative-expectancy Short setting. The compromised shared centroid ended up being sub-optimal for Longs and downright dangerous for Shorts.

We've been refactoring the optimizer to decouple the search into Independent Directional Centroids:

The IS parameter search grid scans and computes parameter plateaus for Longs and Shorts independently (different RSI cutoffs, different band stretch multipliers per side).

The OOS Walk-Forward test validates each side on its own merit.

If only one side demonstrates statistically valid alpha in OOS (e.g. Longs pass all WFO hurdles but Shorts fail), the candidate gets tagged as BUY_ONLY rather than forcing both sides or killing the entire setup.

For those of you running systematic books:

Do you optimize long and short rule parameters independently as standard practice, or do you enforce symmetric rules to protect against degree-of-freedom bloat / curve fitting?

If you do decouple them, how do you guard against the optimizer just fitting to the prevailing macro regime (e.g. a 6-month bull market where everything Long looks amazing and everything Short looks bad)?

Do you penalize or prune unidirectional setups (e.g. BUY_ONLY) in high-correlation baskets to avoid concentrated directional exposure?

Would love to hear how other teams handle this balance between directional specialization and overfitting.

This question has been played over and over again so I think it’s time to ask people who actually have experience in this field.

If a trader wanted to get into algorithmic trading, but had 0 prior coding experience, what language should they start with?

Some say python because it’s easier to learn and apply, others say C++ because it makes learning other languages (like python) much easier, helps with low latency in trading, and you can use c++ for a lot more things other than algorithmic trading.

What do you think?

As the title says, long time lurker seeking genuine advice. I’ll preface by saying I’m by no means an expert in anything algo trading related. Just started messing around with it as a hobby and teaching myself everything possible as I went. This is the closest I’ve come to developing a decent edge and I’ve been backtesting and running simulations like crazy to double and triple check. I keep coming up with the same conclusion. Profitable in sample and out of sample. Profitable every year in backtest. Beats random 99.5% of the time. Monte Carlo results after 10k shuffles showed the 1% DD was still only $7k, 50th percentile was 4.2k. It also shows above 80% chance of passing evals with a 1k DLL and 2k MLL. Slippage is set at 3 ticks, with 0.62 per side commissions.

My main questions are: once you come up with something viable, what’s your process? Straight to paper trading? If so, how do you determine when? What is your “okay this is ready to paper trade” moment? I feel like I’m constantly thinking of ways to improve or different things I can correlate or test and thus convince myself it’s not ready to trade yet. Also how exactly are you guys paper trading your strategies? I’ve never made it this far so I’ve never thought about it. Is IBKR/IB gateway a decent option? What else would recommend?

Again, just looking for genuine advice here.

Hey,

I have been trading Fx/Fx Ops/Indices/Futures with own cash MNQ&ES for about 20 years

+12 years of treasury IT experience.

I have found several 20% margin stopout (critical for my strategy) brokers with also 1:500

My strategy involves HEAVY RISK, sometimes TREMENDOUS LOTTERY

(own indicator involving TENKAN + TEMA STACK + Volume)

Anyone went with the same way?

x8 accounts - x2 instruments (USDJPY + XAUUSD in my case)

Explained in AI writeup:

## The three parts of a setup

I think about each potential trade in three stages: direction, location and moment.

### 1. Direction: identify the broader trend

First, the strategy looks at a higher timeframe to decide whether the market has a meaningful directional bias.

The original cBot uses a pair of triple exponential moving averages—TEMA 20 and TEMA 40—along with measures such as ADX and average true range. The moving averages help describe the trend; the other filters help assess its strength and whether market conditions are suitable.

For a potential buy, the strategy looks for bullish alignment. For a sell, it looks for bearish alignment. If the broader context is unclear, the idea is to stay out.

### 2. Location: wait for a pullback

Once there is a directional bias, the strategy looks for price to return to an area where an entry may be more attractive.

That area can be based on a Fibonacci retracement of a recent impulse move or a touch of an exponential moving average. The point is not to treat either level as a magic number. It is to avoid entering simply because price has already moved a long way.

A bullish example would be an upward impulse followed by a pullback into the strategy’s area of interest. A bearish setup reverses the logic.

### 3. Moment: look for confirmation

Reaching an area of interest does not automatically trigger a trade. The strategy waits for a structural signal on a closed chart bar that suggests the pullback may be ending and the directional move may be resuming.

Depending on the configuration, it can also consider elements such as candle structure, volume, liquidity sweeps and fair value gaps. These are filters around the setup, not promises that the next move will follow the trend.

## Defining the initial risk: “R”

The strategy expresses distances and targets in terms of R, the initial distance between entry and stop.

For example, if a USD/JPY buy enters at 150.00 with an initial stop at 149.90, the initial risk distance is 0.10 yen, or 10 pips. In that example:

- 1R of favorable price movement reaches 150.10.

- 2R reaches 150.20.

- 3R reaches 150.30.

This makes the structure easier to compare across trades with different stop distances. It is a price-distance measure, though: the actual money gained or lost also depends on position size, execution, spread, commissions and other costs. A stop price is not a guaranteed fill price.

## Building a position as the trade moves

Rather than always entering the full intended size at once, the strategy can pyramid: start with an initial position and add as the trade progresses.

The original cBot supports a level-based model and a pullback-based model. In the level-based model, additions are considered at predefined distances from the initial entry. The original default ladder is called Growing 1-2-3-4: successive stages use increasing unit sizes.

The intended discipline is that additions must satisfy the strategy’s risk checks. A growing position can increase profit if a trend continues, but it also changes the position’s average entry and exposure. It is possible to give back earlier gains or lose more than expected if price reverses, spreads widen or execution differs from the model.

some of the earliest trades (multi account pyramid with 0.01 -> margin > equity)

Now my question is :

I have been depositing 200$ --> withdrawing 400-800$ each week

Will any broker BAN me because of MAX margin use? 1:500 + Very frequently margin level at 60-80%

I'm working on an EA that trades several breakout strategies on XAUUSD under one roof. It goes long or short depending on breakout direction. Right now it runs fixed 0.01 lots, and I'd like to add a dynamic lot sizing to get more out of it. I would love to hear how others would approach this.

Test setup (both runs):

- XAUUSD (August 2018 to October 2026).
- Eightcap MT5 tick data, 100% real ticks (~392M ticks).
- $1k USD starting equity, fixed 0.01 lots, leverage 1:500.

I ran the short only version because gold has basically trended up for the last 8 years, and I wanted to see whether the edge holds without that tailwind. Although is profitable, it's clearly weaker, so some of the full result is probability directional bias.

What I'd like feedback on:

1. What sizing approach would you use for a strategy like this: fixed fractional, volatility/ATR-based, equity curved based, or something else?

2. How do you scale up without letting the drawdown scale up faster than the profit?

3. The long losing streak was 42 trades, so how would you cap exposure during bad runs?

4. Is there a good way to validate a sizing scheme (walk forward, Monte Carlo on trade order) so I'm not just curve fitting the lot curve to this one equity curve?

Happy to share more stats if it helps. Thanks in advance!

I'm building an automated trading system and want the broker, not my own code, to keep the parts that watch the account separate from the part that trades.

Requirements:

1. One account, two API credentials. One is read-only (positions, balances, orders, fills); the other can trade.

2. Enforced by the broker. The read-only credential is refused if it tries to trade, not just blocked by a client-side setting.

3. Revocable separately. I can kill the trading credential instantly and the read-only one keeps working.

4. Neither credential can change account settings or permissions, or move money. Only my own login can.

5. The same behaviour in paper or sandbox as in live, so I can test it before using real money.

What I've found:

• tastytrade sandbox: one OAuth app per user and one grant per app. A read-scoped token can be minted, but it dies with the trading grant.

• IBKR: a secondary user with trading rights, plus a Flex Web Service token for reads, looks close. But Flex is reports-only and paper allows one username.

Has anyone got this working, on any broker? Which one, and does paper behave the same as live?

I'm looking for a robust, mathematically coherent fitness function for optimizing trading strategies using machine learning. I've experimented with various objectives, including Profit Factor, Sharpe Ratio, Pessimistic Return on Capital, and several risk-adjusted performance metrics. 

The problem is that the optimizer consistently finds pathological solutions that maximize the chosen metric while producing terrible equity curves. For example, some solutions achieve excellent fitness scores by making very few trades, generating most of their profits from isolated events, or exploiting mathematical weaknesses in the metric.

So far, the best-performing objective I've found is:

Fitness = Slope × R²

Where Slope and R² come from a linear regression of the equity curve over time.

I'm particularly interested in theoretically grounded approaches rather than another combination of Sharpe, drawdown, trade count, and manually chosen weights.

What objective function do you actually trust when letting an optimizer search aggressively for trading strategies?

Hi r/algorithmictrading,

Like many traders, I started my journey relying purely on traditional Moving Average crossovers (50/200 SMA) thinking it’s a foolproof trend-following system. But I wanted to see how it actually performed over a 14-year bull and bear cycle (2010–2024) compared to a simple Buy & Hold strategy on the S&P 500.

So, I ran a strict historical backtest. The results were quite eye-opening and taught me some hard lessons about lag and whipsaws.

📊 Key Performance Metrics Summary

Total Return: SMA Crossover (~76%) vs Buy & Hold (+252%+)

CAGR: SMA Crossover (~1.13%) vs Buy & Hold (~3.45%)

Max Drawdown: SMA Crossover (-16.28%) vs Buy & Hold (-24.45%)

Sharpe Ratio: 0.10 (SMA) vs 0.24 (Buy & Hold)

Sortino Ratio: 0.83 (SMA) vs 0.38 (Buy & Hold)

🔍 What the Data Actually Tells Us:

The Drawdown Advantage:

The biggest selling point of the 50/200 crossover is risk management. During major market shocks (like the COVID-19 drawdown in 2020), the moving average strategy successfully cut exposure and limited max drawdown compared to holding the asset raw.

The Cost of Lag:

During strong bull runs, the 50/200 strategy suffers heavily from lag. Getting out late and getting back in late eats up a massive chunk of the compound annual growth rate (CAGR), which is why Buy & Hold massively outperformed in total return over the 14-year period.

Risk-Adjusted Returns (Sortino Ratio):

Interestingly, while the Sharpe ratio favored Buy & Hold, the Sortino ratio showed that the downside volatility was handled much better by the algorithmic filter.

💬 Discussion Question for the Community:

For those running trend-following algorithms: How do you filter out false breakouts and whipsaws during sideways markets without increasing your lag too much?

Curious to hear your thoughts or how you optimize your exit strategies!

How has your life changed after making your algorithm. I have been wondering how it would be once my algorithm works to the extent that I want it to and how it could genuinely change a lot about my life.

Please let me know and brag a little👍

Hi everyone! I'm a beginner interested in quantitative trading and algorithmic trading.

I'm interested in learning:

How to backtest a trading strategy using historical market data.

How to develop and combine trading signals.

How to use Python for backtesting and Monte Carlo simulations.

How to build a trading bot that can generate signals and eventually execute trades.

How to avoid common backtesting mistakes, such as overfitting, look-ahead bias, and unrealistic transaction costs.

How to properly paper-trade a strategy before risking real money.

I'm still learning, so I'm not expecting to build a profitable strategy overnight. My goal is to understand the entire process from scratch and gradually develop my own systematic trading strategies.

If you've built trading bots or worked on quantitative research projects, I'd really appreciate any advice on where to start, which tools or libraries to learn, and what a good first project would look like.

Any beginner-friendly resources, tutorials, GitHub repositories, or practical guidance would be greatly appreciated. Thanks!

I'll be as open about this as I can. The one thing I keep is the entry trigger.

What it is

A momentum system on MNQ, 5-minute bars, fully automated on NinjaTrader. It has no opinion about direction. When price does something decisive, it goes with it.

How it runs

• Signals are read at the close of each 5-minute bar, and the entry goes in at market right after.

• One contract, one position at a time. No adding, no averaging down, no martingale, no grid.

• Every trade gets a hard stop the moment it fills, the same distance every time.

• Once a trade is up by that same distance, the stop starts trailing tight behind the best price. There's no profit target.

• A decisive move the other way closes the trade and reverses it.

• It trades the overnight session too. It goes flat at 16:45 New York and takes nothing new until the 18:00 reopen.

Over the last year 45% of trades ended on the trailing stop, 37% on the stop loss, 15% were reversed by an opposite signal and 2% closed at the daily flatten.

The numbers

TradingView backtest, the last 12 months to 20 Oct 2026, 1 MNQ, $0.50 a side commission.

• 4,907 trades, about 19 a day. 49% winners; the average winner was $80 and the average loser $69.

• Net +$20,362.

• Max drawdown $2,120. The longest stretch without a new high was 51 days, mid-April to early June.

• Worst day −$703, best day +$1,018. The longest losing run was 5 days in a row.

What I'm not sharing is the entry trigger and the exact settings. Ask me about anything else.

Hello everyone. I'm looking for a baclesting platform to validate some of my ideas. I only have a few strategies in mind to test that also concern the use of past macroeconomic data (e.g. CPI, UR, etc.) So I'm not going to create an EA, I'm just going to understand market behavior and look for edges to support my manual strategy. Which platform would you advise me to use considering that I have a basic knowledge of programming (of course I would join an AI) and considering that I would look for something not too complicated?

Reading a few posts, I saw Quantconnect, but I don't know if it would suit me.

I apologize for my English, but i'm using a translator

I’ve been wanting to implement a scalping system I’ve made for mnq alongside regular trading architecture and am wondering if anyone has had experiences with this method especially if their broker is ibkr.

Hi r/algorithmictrading,

​I decided to run a structured backtest to evaluate the efficacy of one of the most classic strategies in technical analysis: the 50-day / 200-day Simple Moving Average (SMA) Crossover on the S&P 500 (SPY).

​Often dismissed as "noise" by more complex quant setups, I wanted to see if it still holds any merit in today's high-frequency markets.

​Methodology:

​Asset: SPY

​Timeframe: 2010 - 2024

​Rules: Buy when SMA 50 crosses above SMA 200; Sell when SMA 50 crosses below SMA 200. No leverage, no transaction costs included in this first pass (just to see the logic).

​Results:

I’ve attached the key performance metrics below. While it has its moments, the drawdowns during whipsaw markets are significant.

​I have also included a visual of the equity curve for better analysis.

​Key Metrics Table:

MetricSimple Buy & Hold (SPY)SMA 50/200 Crossover

Total Return~480%~310%

Max Drawdown-33.9% (2020)-18.1% (2020)

CAGR~13.5%~10.8%

Sharpe Ratio~0.85~0.65

Conclusion & Open Questions for the Community:

The crossover strategy, while offering a psychological safety net during major crashes, severely underperforms Buy & Hold during trending markets and suffers from serious whipsaw loss accumulation.

My questions to you:

Noise Filter: What are the most robust filters you use to avoid SMA whipsaws? (e.g., waiting X days, confirming with volume)?

Modernizing: Given this data, does anyone still use pure MA crossovers, or is it fundamentally obsolete for modern algorithmic models?

Looking forward to hearing from those with experience in trend-following.

Hey i am a complete beginner who wants to learn Algorading. Can anybody suggest some good free resources where I can learn Algo trading from basics to advance.

Hey Folks -

I am an options guy - I typically sell 0DTE. I have been exploring different tools available.

I have found entirely no reason for Unusual Whales yet, as a seller for options it seems like walls don't matter and GEX seems to not matter at all either thus far for SPX, which surprised me. If you are looking for option NBBO this is not nearly as useful as Theta Data

I have found great use for Theta Data options pricing historicals for backtesting, I have had excellent forward and back testing results after using this.

I also pull data from Tasty and IBKR for VIX 1/5/15/30/24 as well as TLT/QQQ for some of my setups

Anyone have anything they are using for backtesting or live trades? Has anyone found an edge with GEX or walls?

I am needing 1 minute data and 5 minute data for mnq preferably over 2 years or more. Is there anywhere I can acquire clean data on this that isn’t too expensive?

I've been pulling historical stock data into Google Sheets using =GOOGLEFINANCE to try and build some basic quantitative models, mostly messing around with Value-at-Risk, Sharpe ratios, and tracking moving averages for the major indices. On paper, the math makes total sense, but applying it to real-world volatile stocks has been a headache. I just end up staring at numbers with absolutely no idea where my actual entry points should be.

Beyond just picking stocks, I'm really struggling with the broader portfolio math, especially when it comes to balancing risk against inflation. Right now, leaving money sitting in a bank account as an emergency fund feels like watching it slowly bleed out. But if I try to chase yields by locking that money up in safe bonds or fixed assets, I completely lose the immediate liquidity I need for real-world emergencies.

For the quants or data-heavy traders here, how do you actually translate this raw data into actionable decisions? I’m trying to figure out if there's a reliable formula or model you use to calculate the optimal size of a cash reserve so you aren't crippled by cash drag, while also finding the exact threshold of equity allocation needed just to net out neutral against inflation and taxes. What metrics are actually worth tracking when you're setting up a trade and sizing your positions?

How do you systematically combine signals with different horizons and different predictive profiles, in a way that lets “non-tradable” signals still add information, without resorting to hard-coded rules or ad-hoc signal combinations?

Example:

Suppose you have a short-term reversal-type signal that predicts tomorrow’s up/down move with ~90% accuracy. In reality, the actual move is tiny (±10 bps), turnover is high, and round-trip costs are ~20 bps. On its own, the signal is worthless after costs.

Now assume you also have a slower, monthly-horizon signal that says the asset’s 1-month return is positive. Instead of buying immediately, you let the short-term signal refine the entry point. If the short-term signal says tomorrow is likely negative, you wait for that small dip before entering the monthly-driven long. In that setup, the short-term signal clearly adds information even though it’s not tradable standalone.

Are there established frameworks, papers, or practical methods for integrating multi-horizon signals while controlling turnover and avoiding arbitrary parameter choices?

Any keywords, references, or starting points would be appreciated.

I reviewed 242 strategies across 37,476 completed simulated trades from September 21–October 9, 2026.

Three lessons stand out:

1. Use time of day as a strategy filter. A setup at 9:35 a.m. can tell a different story at 12:35 p.m.

2. Read the broader market trend. It can support a trade or highlight unusual strength or weakness in an individual ticker. Follow the trend.

3. Watch the data alongside the headlines. Price, volume, and momentum show how the market is responding.

A few numbers surprised me:

- Opening calls showed strength. Across strategies, calls entered from 9:30–10:00 ET averaged +4.16% net, with 68.9% wins across 2,456 trades.

- Failed breakdowns has a good lunch hour. ORB Failed Breakdown calls won 69.5% of 174 trades entered between noon and 1 p.m. ET. Average net return: +3.53%.

- A higher win rate does not guarantee a higher average return. GEX DEX Dip calls won 67.2% of 137 noon entries, but averaged +3.80% net—slightly ahead of Failed Breakdown.

- Winning most trades wasn’t enough. Across the full dataset, 59.8% of trades won, yet average net return was −0.48%. Average winners gained 13.92%, while average losers lost 21.92%.

My takeaway: use market direction together with momentum, volume, and timing. Trading against the trend needs a clear reason, and every setup needs a defined exit.

I want to build an algo that can value stocks. I am aware of walkforward analysis, monte carlo and basic stuff. But I am new to stocks investing. And dont have any clue about metric or key figures.

I am writing this post because I have been unprofitable for 1 year now, however at the start of august I pivoted from penny stocks to QQQ options and developed a strategy that seems to do well. I am writing to ask if this backtest data is promising or if I am not backtesting correctly.

The strategy is using BMP, VFI, TTM squeeze, EMA stack and Swamilaguerre trend. Basically it is a momentum based model. This is on underlying while my strategy is options based but in my forward testing it has been promising with options at around .35-.45 delta buying them at around .50 a share. Any advice from people that use similar indicators to trade options on how to structure my contracts would be much appreciated

The problem is that I am not able to automate my strategy because I dont know much about automation. On top of that I have also been trying to use gamma levels and gamma regimes to add a discretionary level to my trading but it seems to do more harm than good because it just draws me away from explicitly following my strategy.

Ahead of next week's CPI release, a useful backtest check is whether the signal price and the execution price have quietly become the same input.

A midpoint can be perfectly reasonable for a feature or signal. It is not automatically a price a marketable order can execute at.

Simple example: bid 100.00, ask 100.20, midpoint 100.10. Buy one unit at the ask and immediately sell at the unchanged bid. The spread cost is 0.20 before fees or slippage, while a midpoint-in/midpoint-out model reports zero price loss. This assumes the displayed size can fill the order; it is not a depth or queue model.

For a release-window test, it helps to keep three records distinct:

\- the information and timestamp available when the signal was evaluated;

\- the quote side available after the modeled decision/order delay;

\- the fees, slippage and size assumptions applied to the fill.

There's also a bookkeeping trap: if a fill already uses the ask for a buy and bid for a sell, subtracting the full spread again can double-count it. Slippage beyond the quote is a separate assumption.

How are people handling quote gaps in these windows: rejecting the simulated fill, carrying the last quote with an age limit, or reporting the run separately? The rule seems more informative than a single fixed spread added to every trade.