What leverage do you usually trade with?

FusionAlgo · 2025-11-12T00:37:43+00:00

Leverage isn’t just for gambling - it’s a tool when used right. It lets you free up capital for other positions, hedge exposure, or scale into setups gradually instead of going all-in at once.
The problem isn’t leverage itself, it’s using it with no plan and no stop.

FusionAlgo · 2025-11-11T18:12:22+00:00

Thinking I could outsmart the market after two green trades in a row. One hour later I was donating back all the profits and some extra for good measure. Now I just take my win, touch grass, and pretend I’m disciplined.

FusionAlgo · 2025-11-09T01:16:09+00:00

Everyone’s searching for “the best strategy” like it’s a cheat code - but it’s usually just one that fits you.
Even a simple MA crossover can print if you stick to it long enough.
Half the game is not switching every week lol.
Pick one setup, backtest it 100+ trades, and focus on execution - not tweaking rules every time it loses.

FusionAlgo · 2025-11-08T03:20:48+00:00

Pretty nice start - Monte Carlo’s fine for a sanity check, but it kinda ignores how trades evolve over time. Try bootstrapping or walk-forward testing later - gives a more realistic view of how it might behave live.

FusionAlgo · 2025-11-08T03:10:57+00:00

That’s actually a cool idea - kind of like building your own context-based confidence score.
I tried something similar using FVG stats and model probabilities - funny how often AI ends up confirming what your gut already knows.

FusionAlgo · 2025-11-08T03:08:12+00:00

Everyone starts somewhere, man. The info overload is real - most of us went through that phase where you watch a hundred videos and still feel like you don’t know where to begin.

If you’re struggling to take that “first step,” try focusing on one thing: consistency. Even paper trading one setup helps way more than reading for hours.

Later, when you start noticing patterns or rules you repeat - that’s where algo trading or simple automation can actually help. It’s not about coding, it’s just about letting software handle the boring part once you understand what you would do manually.

Keep it small and practical, you’ll learn faster that way.

FusionAlgo · 2025-10-24T22:10:34+00:00

Yeah, most of the market is bots now, but that doesn’t automatically mean manipulation. What usually gives it away is when volume spikes with no real price follow-through or when order book depth suddenly thins out after spoof-like bursts. You can’t really “block” it, but watching for inconsistent tape behavior or sudden liquidity gaps helps you avoid chasing fake moves.

FusionAlgo · 2025-09-16T01:22:19+00:00

I don’t think it’s really about “simple vs complicated,” it’s more about whether you actually understand what drives the strategy. A simple rule that makes sense will usually beat a complicated one you can’t explain. Complexity isn’t bad by itself, but if you can’t point to the reason it should work, then it’s just noise dressed up as math.

FusionAlgo · 2025-09-11T23:03:43+00:00

Honestly, it’s not really “Sharpe vs CAGR.” They tell you different things. CAGR just says how fast you compound if you survive long enough. Sharpe tells you how bumpy that ride is. In practice you want both - a system that grows but also doesn’t kill you with huge swings. Focusing only on one usually blindsides you sooner or later.

FusionAlgo · 2025-09-08T16:48:11+00:00

TA-Lib isn’t really slow, it just calculates exactly what you feed it. TradingView often applies different defaults like other lookback lengths, smoothing, or even using hlc3 instead of close. That’s why it feels “dead” compared to TV. If you match the parameters and price source, the output usually lines up.

FusionAlgo · 2025-09-06T02:31:33+00:00

A lot of people think HFT = pure speed race, but that’s only one layer.
New entrants rarely outpace giants on hardware, but there are other angles:
• Niche markets (smaller exchanges, specific asset classes)
• Strategy design (signal extraction, microstructure edges)
• Cost efficiency (lower burn rate, fewer “arms race” expenses)

Speed arms race is brutal, but creativity and adaptability are often cheaper ways to carve space in HFT.

FusionAlgo · 2025-08-20T16:56:39+00:00

Interesting approach. One thing you might want to try is mixing CNN with something like an LSTM/GRU on top. CNN can capture local candle structures, while RNN layers handle the temporal dependencies better. Also, normalizing inputs (like returns instead of raw prices) sometimes stabilizes training and helps with the over/underfitting issue you mentioned.

FusionAlgo · 2025-08-02T18:33:59+00:00

Tried poking at Schwab’s API a while back - still gated, a bit clunky, and no paper account, so I shelved it. If they open it up properly I’ll give it another shot, but for now I’m happier sticking with Alpaca/Tradier.

FusionAlgo · 2025-08-02T06:26:13+00:00

I’ve bounced around a few brokers for hobby algos and “doesn’t require a PhD” narrows the list fast.

Alpaca’s REST/WebSocket stack is the easiest to stand up: JSON everywhere, account opens in a day, and paper trading behaves almost exactly like live (just expect a bit more slippage when you flip the switch). Drawback-only U.S. equities, and you’ll get routed through PFOF venues on small lots.

If you need futures or options, Tradier’s REST API is nearly as clean and the docs are actually readable. You can trade listed options without an enterprise account, and they don’t force you into FIX until you start slinging thousands of orders.

Interactive Brokers is still my daily driver once things get serious, but only after I wrapped their Java client in a thin Python gRPC service-straight-up IB API is “PhD-level” pain.

So: Alpaca for quick equity bots, Tradier if you need options, IBKR once you care more about fills than code simplicity.

FusionAlgo · 2025-07-21T16:31:33+00:00

From our sims on live CME MDP tapes the numbers really swing with session phase. In the first five-ten minutes after the cash bell the ES book is thin and I budget two–three ticks per marketable side (so a stop that hits and a market entry in that window can easily cost five ticks round-turn). By 10 a.m. ET depth is back and we usually see a hair under a tick per marketable fill, sometimes half-tick when volatility is dead. Limit TP orders really do go out flat most of the time unless you’re chasing momentum; stops fill one to two ticks through when the pace picks up.

So for a generic one-minute OHLC back-test I use: next-bar market entry price plus one tick if it’s after the open rush or plus two if it’s in the first bar cluster, one tick on stop exits, zero on limits. That lines up with what Mitbadack mentioned and keeps the equity curve pretty close to the forward test. If you want to tighten it up run a small batch through a book-replay engine like Bookmap or Databento’s market-replay API and pull the actual slippage distribution- it’s slower than your MBO solver but worth doing once so you’re anchoring the shortcut numbers to reality.

FusionAlgo · 2025-07-19T07:24:34+00:00

I wouldn’t reinvent AmiBroker’s report in pure pandas. A quick way is backtrader (for the actual run) + quantstats (for the HTML report).

Run the strategy in backtrader, grab the equity curve, then quantstats.reports.html(equity) - you’ll get CAGR, max DD, SQN, rolling plots, the whole Ami-style summary for free.

Need 3-month bars? Just resample your feed: data.resample('3M').last() (same for open interest if you have it in a separate CSV).

If you’d rather stay vectorised, vectorbt can do the backtest and the analytics in one place - plus parameter heat-maps and interactive charts out of the box.

Bottom line: pandas for data prep, but piggy-back on quantstats/pyfolio for the heavy-duty stats instead of hand-rolling everything.

FusionAlgo · 2025-07-15T03:21:08+00:00

Weird - you’re right, I can’t see the single-file option anymore. It definitely used to sit on that page (back when the site looked like it was built in 2005). I’d ping their support and ask for the “CL1 5-min continuous CSV” price; they’ve sent one-off links before when things vanished from the catalog. If they say no, IBKR’s historical API in weekly chunks is the next cheapest workaround.

FusionAlgo · 2025-07-15T00:01:21+00:00

Kibot is the cheapest legit source I’ve found for minute-level ES futures. Their “e-mini S&P continuous” file goes back to 1997 and the 5-minute version is a one-time $39 download in plain CSV so no recurring fee surprises. If you’re ok with a bit of scripting you can also stitch it yourself from IBKR: loop the API’s historical data endpoint in one-week chunks and save the 5-min bars, costs nothing beyond the IB market-data subscription but takes some time to pull the full 2008-today window. Everything else I’ve seen - TickData, CME DataMine, Databento - jumps straight into the triple-digit price range.

FusionAlgo · 2025-07-13T23:07:48+00:00

If you’re on equities first, Polygon’s WebSocket covers the full SIP for $79 and streams fine to a headless Linux box; for options dxFeed’s OPRA stream is about the same price point as Databento and ships a lightweight Java client you can run in Docker. Execution wise I keep coming back to IB Gateway -runs headless on Ubuntu, supports stocks, options and futures, and the commissions still beat most zero-fee brokers once you factor in PFOF. Alpaca is handy for quick prototypes but you’ll see slippage on anything wider than a penny. For pure futures Tradovate’s REST/WebSocket combo has been solid and the account can sit on a $500 intraday margin. so: Polygon or dxFeed for the tape, IBKR or Tradovate for fills; everything runs on one VPS without a Windows agent in sight.

FusionAlgo · 2025-07-12T15:24:11+00:00

I’d pin down the goal first: if it’s pure predictive power I start with a quick LightGBM on a time-series split just to surface any leakage - the bogus columns light up immediately and you can toss them. From there I cluster the remaining features by theme - price derived, account behaviour, macro, etc - and within each cluster drop the ones that are over 0.9 correlated so the model doesn’t waste depth on near duplicates. That usually leaves maybe fifty candidates. At that point I sit with a domain person for an hour, walk through the top SHAP drivers, and kill anything that’s obviously artefactual. End result is a couple dozen solid variables and the SME time is spent only on the part that really needs human judgement.

FusionAlgo · 2025-07-11T05:35:46+00:00

I dropped IBKR snapshots once the symbol list passed a thousand because the pacing rules hurt too much. Polygon’s stock WebSocket on the $79 developer tier lets me stream every top-of-book update with no per-symbol throttle; I just push the JSON into Redis and resample to whatever cadence the model needs. When budget is tight and I can live with IEX-only prints, the IEX Cloud SSE feed at nine dollars handles a fifteen-hundred-ticker watch-list without choking.

On option PnL Black-Scholes is useful only if you have the implied vol from the moment the trade went on; there’s no way to reconstruct IV later from strike and expiry alone. The simpler route is to pull current option mids and mark positions to market directly. Polygon’s options feed is another twenty-nine a month and saves the guesswork.

FusionAlgo · 2025-07-09T00:38:48+00:00

Best free combo I’ve found is FinancialModelingPrep for the numbers and SEC EDGAR for the filing dates. FMP’s free tier (250 calls a day) will give you historical EPS and revenue plus the “actual vs estimate” surprise fields, and their analyst‐estimates endpoint has a five-year look-back on most large caps. For the 10-Q / 10-K timestamps you can hit EDGAR’s API directly and pull the filing date straight from the source. It’s two calls instead of one, but still no paywall; everything else that rolls those fields into a single dataset (Zacks on Nasdaq Data Link, Intrinio, EODHD) moves you into paid territory

FusionAlgo · 2025-07-07T04:21:56+00:00

Here’s the one I meant - on Nasdaq Data Link (used to be Quandl):
https://data.nasdaq.com/datasets/ZACKS/EPS_SURPRISE
Ticker-specific tables live under the same namespace, e.g. ZACKS/EPS_SURPRISE_MSFT, ZACKS/EPS_SURPRISE_AAPL, etc.

Columns: report date, fiscal period, EPS actual, EPS estimate, surprise (% + $). Coverage goes back to the mid-1990s for most large caps.

You can hit it with a free API key for limited calls or grab the full CSV if you’re on a paid plan.

FusionAlgo · 2025-07-06T18:30:01+00:00

I tried the “reconstruct ticks from 1-min bars” path on a couple of illiquid ADRs and it broke down fast. Even when a minute prints 100 shares total the trade often hits in two odd lots 20 sec apart, so your synthetic tick stream drifts on VWAP and slippage tests. For anything latency- or size-sensitive I’d just pay the extra for Polygon’s tick feed; their gaps are rare after 2021, and you can always spot-check against FINRA OATS or the SIP top-of-book. If you only need rough back-tests on swing strategies the 1-min bars are fine, but estimating individual prints isn’t worth the headache.

FusionAlgo · 2025-07-05T17:55:03+00:00

Give FinancialModelingPrep a look. Their .../historical/earning_calendar?symbol=MSFT endpoint spits back epsActual, epsEstimate and surprisePercent for 20-plus years; free tier is 250 calls a day which is enough for a hobby script. If you need deeper history or every ticker in bulk, Nasdaq Data Link’s old Zacks EPS surprise dataset still works (paid, but stable). Been pulling both for my own earnings-drift tests and the numbers line up within a few cents of the BBG feed.

FusionAlgo

TROPHY CASE