yanmovfer,github

2018-ccf-bdci--top3

参赛者需要根据给出的基金净值、基金业绩比较基准、对应指数行情、基金间相关性等数据，构建模型、算法进行训练。

abu

阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构

anchor-link

Persistent, fast and secure signature provider for EOSIO chains built on top of EOSIO Signing Requests (EEP-7)

apple_itunes_win

Windows iTunes full technology, login, purchase, install IPA, use account authorization to the device and more.

apple_tools

Windows iTunes full technology, login, purchase, install IPA, use account authorization to the device and more.

appstore_ios10_win

windows fake ios appstore protocol using iTunes algorithm

auto-trade-crypto-bot

Crypto trading bot in Python with Telegram and ccxt

auto_trade_blockchain

it automatically analyze the Coin and give opnion how you choice sell or buy.

ccxt

A JavaScript / Python / PHP cryptocurrency trading API with support for more than 120 bitcoin/altcoin exchanges

chainer

A flexible framework of neural networks for deep learning

coinexchange

deepfacelab

DeepFaceLab is a tool that utilizes deep learning to recognize and swap faces in pictures and videos. Includes prebuilt ready to work standalone Windows 7,8,10 binary (look readme.md).

deepfakes_faceswap

from deekfakes' faceswap: https://www.reddit.com/user/deepfakes/

dmac-btc-trading-example-with-tuning

I dug up some code that I played with a while ago as a toy to test out ideas. It takes a DMAC, Dual Moving Average Crossover and buys when the short average crosses the long up and sells on the reverse of that. It auto-optimizes on backtest data to pick the best combination of the length of the averages, using brute force search. This is a real simple strat, I would not advise trading with something like this without additional work and risk management, stops for instance. But, I did it as a check on the timing intervals of the BTC market at the time. This version of it runs through a years worth of data tunes and the spits out the results for various parameter tunings and prints out the HODL gain, as if you bought on Jan 1 and held for the year. The strat assumes $100000 in at Jan 1, so look at that as your 100% mark to see what the strat would gain for the year. It is revealing in that it shows that sometimes buy and hold is the winner other times the strat wins. Best Bank is the best amount it makes with the short and long averages (Averages are in terms of days in this example)adjusted as seen. Count is just that, a count of how many combinations it has tried. It prints out the best ones within the range. I am not blocking the averages from going upside down either. For example for 2018 the winner has a short avg of 35 days and a long of 30, it's "backwards" from the normal idea of DMAC but makes the best gains on backtest. I am posting this here for educational purposes for people that might be new-ish to trading just to show a comparison between a dirt simple strat and HODL. Notice how buy and hold beats the strat by a lot in 2011,2013,2017 and the strat beats HODL for 2014 and 2018 in this example. For 2011,2012 and 2017, the strat is a dud in that it doesn't find decent parameters for the average to make good gains. All this is just food for thought.

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